Effective virtual reality environments for presentation of marketing materials

Effective virtual reality environments including in-store virtual reality environments such as supermarket aisles, store shelves, cooler displays, etc. are generated using frameworks and customer layout information. Marketing materials are integrated with the virtual reality environment while introducing imperfections and/or other cues of realism to create a virtual reality environment representation. Imperfections may include misaligned marketing materials, product label blemishes, packages placed slightly askew, etc. Sensory experiences output to the user via the virtual reality environment representation elicit interactivity with a user and user movements, motions, and responses are used to evaluate the effectiveness of the marketing materials and/or the virtual reality environment representations.

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Description
RELATED APPLICATIONS

This patent is related to U.S. patent application Ser. No. 12/056,190; U.S. patent application Ser. No. 12/056,211; U.S. patent application Ser. No. 12/056,221; U.S. patent application Ser. No. 12/056,225; U.S. patent application Ser. No. 12/113,863; U.S. patent application Ser. No. 12/113,870; U.S. patent application Ser. No. 12/122,240; U.S. patent application Ser. No. 12/122,253; U.S. patent application Ser. No. 12/122,262; U.S. patent application Ser. No. 12/135,066; U.S. patent application Ser. No. 12/135,074; U.S. patent application Ser. No. 12/182,851; U.S. patent application Ser. No. 12/182,874; U.S. patent application Ser. No. 12/199,557; U.S. patent application Ser. No. 12/199,583; U.S. patent application Ser. No. 12/199,596; U.S. patent application Ser. No. 12/200,813; U.S. patent application Ser. No. 12/234,372; U.S. patent application Ser. No. 12/135,069; U.S. patent application Ser. No. 12/234,388; U.S. patent application Ser. No. 12/544,921; U.S. patent application Ser. No. 12/544,934; U.S. patent application Ser. No. 12/546,586; U.S. patent application Ser. No. 12/544,958; U.S. patent application Ser. No. 12/846,242; U.S. patent application Ser. No. 12/410,380; U.S. patent application Ser. No. 12/410,372; U.S. patent application Ser. No. 12/413,297; U.S. patent application Ser. No. 12/545,455; U.S. patent application Ser. No. 12/608,660; U.S. patent application Ser. No. 12/608,685; U.S. patent application Ser. No. 13/444,149; U.S. patent application Ser. No. 12/608,696; U.S. patent application Ser. No. 12/731,868; U.S. patent application Ser. No. 13/045,457; U.S. patent application Ser. No. 12/778,810; U.S. patent application Ser. No. 12/778,828; U.S. patent application Ser. No. 13/104,821; U.S. patent application Ser. No. 13/104,840; U.S. patent application Ser. No. 12/853,197; U.S. patent application Ser. No. 12/884,034; U.S. patent application Ser. No. 12/913,102; U.S. patent application Ser. No. 12/853,213; and U.S. patent application Ser. No. 13/105,774.

TECHNICAL FIELD

The present disclosure relates to effective virtual reality environments for presentation of marketing materials.

DESCRIPTION OF RELATED ART

Virtual reality environments are used in a variety of contexts including simulation, game, training, social interaction, and entertainment contexts. Virtual reality environments may be substituted for a natural environment where using a natural environment is impossible, impractical, inconvenient, or inferior. In some contexts, marketing materials such as product, product packages, services, and offerings may be presented in a natural environment. However, mechanisms for presenting stimulus material in natural environments are limited. In some examples, individuals are asked to respond to surveys quickly after exposure to stimulus materials in a variety of environments, but information collected is typically limited. Furthermore, conventional systems are subject to brain pattern, semantic, syntactic, metaphorical, cultural, and interpretive errors that prevent accurate and repeatable analyses.

Consequently, it is desirable to provide improved methods and apparatus for providing effective virtual reality environments for presentation of marketing materials.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular example embodiments.

FIG. 1 illustrates one example of a system for virtual reality environment evaluation.

FIGS. 2A-2E illustrate a particular example of a neuro-response data collection mechanism.

FIG. 3 illustrates examples of data models that can be used with a stimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with the neuro-response collection system.

FIG. 5 illustrates one example of a report generated using the neuro-response collection system.

FIG. 6 illustrates one example of a technique for evaluating a virtual reality environment.

FIG. 7 provides one example of a system that can be used to implement one or more mechanisms.

 DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of the invention including the best modes contemplated by the inventors for carrying out the invention. Examples of these specific embodiments are illustrated in the accompanying drawings. While the invention is described in conjunction with these specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims.

For example, the techniques and mechanisms of the present invention will be described in the context of particular types of marketing materials. However, it should be noted that the techniques and mechanisms of the present invention apply to a variety of different types of stimulus materials including marketing and entertainment materials. It should be noted that various mechanisms and techniques can be applied to any type of stimuli. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. Particular example embodiments of the present invention may be implemented without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure the present invention.

Various techniques and mechanisms of the present invention will sometimes be described in singular form for clarity. However, it should be noted that some embodiments include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. For example, a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors while remaining within the scope of the present invention unless otherwise noted. Furthermore, the techniques and mechanisms of the present invention will sometimes describe a connection between two entities. It should be noted that a connection between two entities does not necessarily mean a direct, unimpeded connection, as a variety of other entities may reside between the two entities. For example, a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory. Consequently, a connection does not necessarily mean a direct, unimpeded connection unless otherwise noted.

Overview

Effective virtual reality environments including in-store virtual reality environments such as supermarket aisles, store shelves, cooler displays, etc. are generated using frameworks and customer layout information. Marketing materials are integrated with the virtual reality environment while introducing imperfections and/or other cues of realism to create a virtual reality environment representation. Imperfections may include misaligned marketing materials, product label blemishes, packages placed slightly askew, etc. Sensory experiences output to the user via the virtual reality environment representation elicit interactivity with a user and user movements, motions, and responses are used to evaluate the effectiveness of the marketing materials and/or the virtual reality environment representations.

EXAMPLE EMBODIMENTS

Virtual reality environments for presentation of marketing materials such as products, product packages, brochures, displays, signs, offerings, and arrangements are limited. In many instances, virtual reality environments are difficult to generate, lack physics, and detract from a user experience. In-store virtual reality environments such as showrooms, supermarket aisles, convenience stores, etc., often prevent effective evaluation of marketing materials because they introduce their own biases and influences that differ from those of an actual in-store environment. User responses to marketing materials in a laboratory or evaluation setting can sometimes be different than user responses to the marketing materials in a natural environment, such as a store shelf, a supermarket aisle, a tradeshow floor, or a showroom. However, opportunities to evaluate the effectiveness of marketing materials in natural environments are limited.

According to various embodiments, efforts are made to provide improved virtual reality environments. In-store virtual reality environment frameworks are combined with layout information and marketing materials to generate an integrated in-store 2-D or 3-D virtual reality environment with physics. Imperfections and randomizations are introduced to enhance the in-store virtual reality environment. According to various embodiments, bottles are oriented in slightly different directions, rows of boxes are slightly misaligned, signs are shown with slight wear, etc. In particular embodiments, an in-store virtual reality environment is evaluated for similarity to a natural environment. It is recognized that a virtual reality in-store environment can provide a framework for evaluating stimulus material in a manner that closely mimics a natural environment. According to various embodiments, an in-store virtual reality environment is neurologically analyzed for neuro-response commonality with that of an actual in-store environment.

In many instances, model stores, displays, and mock presentations are set up to test the effectiveness of particular packages, displays, presentations, offerings, etc. However, using actual displays or establishing mock presentations is cumbersome and inflexible. It is highly inefficient to test a variety of presentations or make changes to presentations based on user feedback. Furthermore, even when ample user feedback is obtained, user feedback is subject to brain pattern, semantic, syntactic, metaphorical, cultural, and interpretive errors that prevent accurate and repeatable analyses.

Consequently, the techniques of the present invention provide mechanisms for generating effective virtual reality environments for a variety of purposes including evaluating marketing materials. Virtual reality environments and virtual reality environment templates can be generated to allow reuse, customization, and integration with marketing materials.

Neuro-response data is analyzed to determine the effectiveness of virtual reality environments including in-store virtual reality environments. Individuals are provided with mechanisms to interact with the virtual reality environment and marketing materials and other stimulus materials can be manipulated in the framework of the virtual reality environment. Sensors, cameras, microphones, motion detectors, gyroscopes, temperature sensors, etc., can all be used to monitor user responses to allow not only manipulation of the virtual reality environment but modification the marketing materials presented. According to various embodiments, in-store virtual reality environments can be generated and evaluated without the use of any neuro-response data. However, in particular embodiments, neuro-response data is used to evaluate the effectiveness of in-store virtual reality environments and/or marketing materials and make real-time adjustments.

Neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements can be used to evaluate subjects during stimulus presentation. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephlography (MEG), and Optical Imaging. Optical imaging can be used to measure the absorption or scattering of light related to concentration of chemicals in the brain or neurons associated with neuronal firing. MEG measures magnetic fields produced by electrical activity in the brain. fMRI measures blood oxygenation in the brain that correlates with increased neural activity. However, current implementations of fMRI have poor temporal resolution of few seconds. EEG measures electrical activity associated with post synaptic currents occurring in the milliseconds range. Subcranial EEG can measure electrical activity with the most accuracy, as the bone and dermal layers weaken transmission of a wide range of frequencies. Nonetheless, surface EEG provides a wealth of electrophysiological information if analyzed properly. Even portable EEG with dry electrodes provides a large amount of neuro-response information.

Autonomic nervous system measurement mechanisms include Electrocardiograms (EKG) and pupillary dilation, etc. Effector measurement mechanisms include Electrooculography (EOG), eye tracking, facial emotion encoding, reaction time etc.

Multiple modes and manifestations of precognitive neural signatures are blended with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately perform neuro-response analysis. In some examples, autonomic nervous system measures are themselves used to validate central nervous system measures. Effector and behavior responses are blended and combined with other measures. According to various embodiments, central nervous system, autonomic nervous system, and effector system measurements are aggregated into a measurement that allows evaluation of stimulus material effectiveness in particular environments.

In particular embodiments, subjects are exposed to stimulus material and data such as central nervous system, autonomic nervous system, and effector data is collected during exposure. According to various embodiments, data is collected in order to determine a resonance measure that aggregates multiple component measures that assess resonance data. In particular embodiments, specific event related potential (ERP) analyses and/or event related power spectral perturbations (ERPSPs) are evaluated for different regions of the brain both before a subject is exposed to stimulus and each time after the subject is exposed to stimulus.

According to various embodiments, pre-stimulus and post-stimulus differential as well as target and distracter differential measurements of ERP time domain components at multiple regions of the brain are determined (DERP). Event related time-frequency analysis of the differential response to assess the attention, emotion and memory retention (DERPSPs) across multiple frequency bands including but not limited to theta, alpha, beta, gamma and high gamma is performed. In particular embodiments, single trial and/or averaged DERP and/or DERPSPs can be used to enhance the resonance measure and determine priming levels for various products and services.

According to various embodiments, enhanced neuro-response data is generated using a data analyzer that performs both intra-modality measurement enhancements and cross-modality measurement enhancements. According to various embodiments, brain activity is measured not just to determine the regions of activity, but to determine interactions and types of interactions between various regions. The techniques and mechanisms of the present invention recognize that interactions between neural regions support orchestrated and organized behavior. Attention, emotion, memory, and other abilities are not merely based on one part of the brain but instead rely on network interactions between brain regions.

The techniques and mechanisms of the present invention further recognize that different frequency bands used for multi-regional communication can be indicative of the effectiveness of stimuli. In particular embodiments, evaluations are calibrated to each subject and synchronized across subjects. In particular embodiments, templates are created for subjects to create a baseline for measuring pre and post stimulus differentials. According to various embodiments, stimulus generators are intelligent and adaptively modify specific parameters such as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facial emotion encoding, reaction time, etc. Individual modalities such as EEG are enhanced by intelligently recognizing neural region communication pathways. Cross modality analysis is enhanced using a synthesis and analytical blending of central nervous system, autonomic nervous system, and effector signatures. Synthesis and analysis by mechanisms such as time and phase shifting, correlating, and validating intra-modal determinations allow generation of a composite output characterizing the significance of various data responses.

According to various embodiments, survey based and actual expressed responses and actions for particular groups of users are integrated with neuro-response data and stored in a stimulus material and virtual reality environment. According to particular embodiments, pre-articulation predictions of expressive response for various stimulus material can be made by analyzing neuro-response data.

FIG. 1A illustrates one example of a system for generating an effective in-store virtual reality environment.

According to various embodiments, a marketing materials framework generator 101 constructs products, product packages, displays, labels, boxes, signs, offerings, and advertising using custom or template designs. In particular examples, companies, firms, and individuals wanting to test marketing materials provide the designs that can be incorporated into designs or wireframes for use in virtual reality environments. A virtual reality environment generator 105 can construct custom or template based designs for store aisles, shopping malls, showrooms, tradeshow floors, etc., that can be integrated with marketing materials 101 using a particular layout and placement from layout generator 103.

According to various embodiments, marketing materials, layout, and a virtual reality environment are integrated using an environment integration mechanism 111. In particular embodiments, the environment integration mechanism introduces randomness and realism into a presentation. Randomness and realism may include automatically introduce imperfections in the marketing materials, misalignment of the products on a shelf, slight blemishes on product labels, etc. The environment may also include imperfections. For example, there may be boxes with products not yet unloaded onto a shelf, an area of the floor may be in the process of being cleaned, etc. According to various embodiments, product imperfections, layout imperfections, and environment imperfections contribute to making a virtual reality environment more effective and realistic. In particular embodiments, actual product, layout, and environment imperfections found in in-store environments are used to improve the accessibility and acceptability of environments to users.

According to various embodiments, the integrated environment is provided to a presentation device 121. The presentation device 121 may include screens, headsets, domes, multidimensional displays, speakers, motion simulation devices, movable platforms, smell generators, etc., to provide the subject 123 with a simulated environment. Subject response collection mechanism 131 may include cameras recorders, motion detectors, etc., that capture subject activity and responses. According to various embodiments, neuro-response data collection mechanisms are also used to capture neuro-response data such as electroencephalography (EEG) data for the subject presented with stimulus materials. In particular embodiments, feedback and modification mechanism 141 uses subject responses to modify marketing materials and/or the virtual reality environment based on subject actions. According to various embodiments, product packages may be manipulated by a subject in the virtual reality environment. In particular embodiments, store displays may be viewed from different angles, products may be opened, etc.

According to various embodiments, neuro-response data including EEG data is used to make real-time modifications to marketing materials and virtual reality environments. In particular embodiments, lack of interest is detected using neuro-response data and different marketing materials are dynamically presented to the user as the user moves along in a grocery aisle.

FIG. 1B illustrates one example of a neuro-response data collection mechanism that can be used to evaluate virtual reality environments for effectiveness and/or stimulus material in virtual reality environments. Different elements of realism in virtual reality environments may be more effective for certain users. In particular embodiments, the virtual reality environment presentation device 151 is merely a display, monitor, screen, etc., that displays a virtual reality environment to a user. The stimulus material may be a product, product package, service, offering, advertisement, placard, brochure, etc., placed in the context of a supermarket aisle, convenience store, room, etc.

The stimuli can involve a variety of senses and occur with or without human supervision. Continuous and discrete modes are supported. According to various embodiments, the virtual reality environment presentation device 151 also has protocol generation capability to allow intelligent customization of stimulus and environments provided to multiple subjects in different settings such as laboratory, corporate, and home settings.

According to various embodiments, virtual reality environment presentation device 151 could include devices such as headsets, goggles, projection systems, display devices, speakers, tactile surfaces, etc., for presenting the stimulus in virtual reality environments.

According to various embodiments, the subjects 153 are connected to data collection devices 155. The data collection devices 155 may include a variety of neuro-response measurement mechanisms including neurological and neurophysiological measurements systems such as EEG, EOG, MEG, pupillary dilation, eye tracking, facial emotion encoding, and reaction time devices, etc. According to various embodiments, neuro-response data includes central nervous system, autonomic nervous system, and effector data. In particular embodiments, the data collection devices 155 include EEG 161, EOG 163, and fMRI 165. In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision.

The data collection device 155 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (EKG, pupillary dilation), and effector sources (EOG, eye tracking, facial emotion encoding, reaction time). In particular embodiments, data collected is digitally sampled and stored for later analysis. In particular embodiments, the data collected could be analyzed in real-time. According to particular embodiments, the digital sampling rates are adaptively chosen based on the neurophysiological and neurological data being measured.

In one particular embodiment, the system includes EEG 161 measurements made using scalp level electrodes, EOG 163 measurements made using shielded electrodes to track eye data, fMRI 165 measurements performed using a differential measurement system, a facial muscular measurement through shielded electrodes placed at specific locations on the face, and a facial affect graphic and video analyzer adaptively derived for each individual.

In particular embodiments, the data collection devices are clock synchronized with a virtual reality environment presentation device 151. In particular embodiments, the data collection devices 155 also include a condition evaluation subsystem that provides auto triggers, alerts and status monitoring and visualization components that continuously monitor the status of the subject, data being collected, and the data collection instruments. The condition evaluation subsystem may also present visual alerts and automatically trigger remedial actions. According to various embodiments, the data collection devices include mechanisms for not only monitoring subject neuro-response to stimulus materials, but also include mechanisms for identifying and monitoring the stimulus materials. For example, data collection devices 155 may be synchronized with a set-top box to monitor channel changes. In other examples, data collection devices 155 may be directionally synchronized to monitor when a subject is no longer paying attention to stimulus material. In still other examples, the data collection devices 155 may receive and store stimulus material generally being viewed by the subject, whether the stimulus is a program, a commercial, printed material, or a scene outside a window. The data collected allows analysis of neuro-response information and correlation of the information to actual stimulus material and not mere subject distractions.

According to various embodiments, the virtual reality stimulus presentation system also includes a data cleanser device 171. In particular embodiments, the data cleanser device 171 filters the collected data to remove noise, artifacts, and other irrelevant data using fixed and adaptive filtering, weighted averaging, advanced component extraction (like PCA, ICA), vector and component separation methods, etc. This device cleanses the data by removing both exogenous noise (where the source is outside the physiology of the subject, e.g. a phone ringing while a subject is viewing a video) and endogenous artifacts (where the source could be neurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectively isolate and review the response data and identify epochs with time domain and/or frequency domain attributes that correspond to artifacts such as line frequency, eye blinks, and muscle movements. The artifact removal subsystem then cleanses the artifacts by either omitting these epochs, or by replacing these epoch data with an estimate based on the other clean data (for example, an EEG nearest neighbor weighted averaging approach).

According to various embodiments, the data cleanser device 171 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 171 is shown located after a data collection device 155, the data cleanser device 171 like other components may have a location and functionality that varies based on system implementation. For example, some systems may not use any automated data cleanser device whatsoever while in other systems, data cleanser devices may be integrated into individual data collection devices.

In particular embodiments, a survey and interview system collects and integrates user survey and interview responses to combine with neuro-response data to more effectively perform virtual reality stimulus presentation. According to various embodiments, the survey and interview system obtains information about user characteristics such as age, gender, income level, location, interests, buying preferences, hobbies, etc.

According to various embodiments, the virtual reality stimulus presentation system includes a data analyzer 173 associated with the data cleanser 171. The data analyzer 173 uses a variety of mechanisms to analyze underlying data in the system to determine resonance. According to various embodiments, the data analyzer 173 customizes and extracts the independent neurological and neuro-physiological parameters for each individual in each modality, and blends the estimates within a modality as well as across modalities to elicit an enhanced response to the presented stimulus material. In particular embodiments, the data analyzer 173 aggregates the response measures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiological signatures are measured using time domain analyses and frequency domain analyses. Such analyses use parameters that are common across individuals as well as parameters that are unique to each individual. The analyses could also include statistical parameter extraction and fuzzy logic based attribute estimation from both the time and frequency components of the synthesized response.

In some examples, statistical parameters used in a blended effectiveness estimate include evaluations of skew, peaks, first and second moments, distribution, as well as fuzzy estimates of attention, emotional engagement and memory retention responses.

According to various embodiments, the data analyzer 173 may include an intra-modality response synthesizer and a cross-modality response synthesizer. In particular embodiments, the intra-modality response synthesizer is configured to customize and extract the independent neurological and neurophysiological parameters for each individual in each modality and blend the estimates within a modality analytically to elicit an enhanced response to the presented stimuli. In particular embodiments, the intra-modality response synthesizer also aggregates data from different subjects in a dataset.

According to various embodiments, the cross-modality response synthesizer or fusion device blends different intra-modality responses, including raw signals and signals output. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device can also aggregate data from different subjects in a dataset.

According to various embodiments, the data analyzer 173 also includes a composite enhanced effectiveness estimator (CEEE) that combines the enhanced responses and estimates from each modality to provide a blended estimate of the effectiveness. In particular embodiments, blended estimates are provided for each exposure of a subject to stimulus materials. The blended estimates are evaluated over time to assess resonance characteristics. According to various embodiments, numerical values are assigned to each blended estimate. The numerical values may correspond to the intensity of neuro-response measurements, the significance of peaks, the change between peaks, etc. Higher numerical values may correspond to higher significance in neuro-response intensity. Lower numerical values may correspond to lower significance or even insignificant neuro-response activity. In other examples, multiple values are assigned to each blended estimate. In still other examples, blended estimates of neuro-response significance are graphically represented to show changes after repeated exposure.

According to various embodiments, a data analyzer 173 passes data to a resonance estimator that assesses and extracts resonance patterns. In particular embodiments, the resonance estimator determines entity positions in various stimulus segments and matches position information with eye tracking paths while correlating saccades with neural assessments of attention, memory retention, and emotional engagement. In particular embodiments, the resonance estimator stores data in the priming repository system. As with a variety of the components in the system, various repositories can be co-located with the rest of the system and the user, or could be implemented in remote locations.

Data from various repositories is blended and passed to a virtual reality stimulus presentation engine to generate patterns, responses, and predictions 175. In some embodiments, the virtual reality stimulus presentation engine compares patterns and expressions associated with prior users to predict expressions of current users. According to various embodiments, patterns and expressions are combined with orthogonal survey, demographic, and preference data. In particular embodiments linguistic, perceptual, and/or motor responses are elicited and predicted. Response expression selection and pre-articulation prediction of expressive responses are also evaluated.

FIGS. 2A-2E illustrate a particular example of a neuro-response data collection mechanism. FIG. 2A shows a perspective view of a neuro-response data collection mechanism including multiple dry electrodes. According to various embodiments, the neuro-response data collection mechanism is a headset having point or teeth electrodes configured to contact the scalp through hair without the use of electro-conductive gels. In particular embodiments, each electrode is individually amplified and isolated to enhance shielding and routability. In some examples, each electrode has an associated amplifier implemented using a flexible printed circuit. Signals may be routed to a controller/processor for immediate transmission to a data analyzer or stored for later analysis. A controller/processor may be used to synchronize neuro-response data with stimulus materials. The neuro-response data collection mechanism may also have receivers for receiving clock signals and processing neuro-response signals. The neuro-response data collection mechanisms may also have transmitters for transmitting clock signals and sending data to a remote entity such as a data analyzer.

FIGS. 2B-2E illustrate top, side, rear, and perspective views of the neuro-response data collection mechanism. The neuro-response data collection mechanism includes multiple electrodes including right side electrodes 261 and 263, left side electrodes 221 and 223, front electrodes 231 and 233, and rear electrode 251. It should be noted that specific electrode arrangement may vary from implementation to implementation. However, the techniques and mechanisms of the present invention avoid placing electrodes on the temporal region to prevent collection of signals generated based on muscle contractions. Avoiding contact with the temporal region also enhances comfort during sustained wear.

According to various embodiments, forces applied by electrodes 221 and 223 counterbalance forces applied by electrodes 261 and 263. In particular embodiments, forces applied by electrodes 231 and 233 counterbalance forces applied by electrode 251. In particular embodiments, the EEG dry electrodes operate to detect neurological activity with minimal interference from hair and without use of any electrically conductive gels. According to various embodiments, neuro-response data collection mechanism also includes EOG sensors such as sensors used to detect eye movements.

According to various embodiments, data acquisition using electrodes 221, 223, 231, 233, 251, 261, and 263 is synchronized with stimulus material presented to a user. Data acquisition can be synchronized with stimulus material presented by using a shared clock signal. The shared clock signal may originate from the stimulus material presentation mechanism, a headset, a cell tower, a satellite, etc. The data collection mechanism 201 also includes a transmitter and/or receiver to send collected neuro-response data to a data analysis system and to receive clock signals as needed. In some examples, a transceiver transmits all collected media such as video and/or audio, neuro-response, and sensor data to a data analyzer. In other examples, a transceiver transmits only interesting data provided by a filter. According to various embodiments, neuro-response data is correlated with timing information for stimulus material presented to a user.

In some examples, the transceiver can be connected to a computer system that then transmits data over a wide area network to a data analyzer. In other examples, the transceiver sends data over a wide area network to a data analyzer. Other components such as fMRI and MEG that are not yet portable but may become portable at some point may also be integrated into a headset.

It should be noted that some components of a neuro-response data collection mechanism have not been shown for clarity. For example, a battery may be required to power components such as amplifiers and transceivers. Similarly, a transceiver may include an antenna that is similarly not shown for clarity purposes. It should also be noted that some components are also optional. For example, filters or storage may not be required.

FIG. 3 illustrates examples of data models that can be used for storage of information associated with collection of neuro-response data. According to various embodiments, a dataset data model 301 includes a name 303 and/or identifier, client attributes 305, a subject pool 307, logistics information 309 such as the location, date, and stimulus material 311 identified using user entered information or video and audio detection.

In particular embodiments, a subject attribute data model 315 includes a subject name 317 and/or identifier, contact information 321, and demographic attributes 319 that may be useful for review of neurological and neuro-physiological data. Some examples of pertinent demographic attributes include marriage status, employment status, occupation, household income, household size and composition, ethnicity, geographic location, sex, race. Other fields that may be included in data model 315 include shopping preferences, entertainment preferences, and financial preferences. Shopping preferences include favorite stores, shopping frequency, categories shopped, favorite brands. Entertainment preferences include network/cable/satellite access capabilities, favorite shows, favorite genres, and favorite actors. Financial preferences include favorite insurance companies, preferred investment practices, banking preferences, and favorite online financial instruments. A variety of subject attributes may be included in a subject attributes data model 315 and data models may be preset or custom generated to suit particular purposes.

Other data models may include a data collection data model 337. According to various embodiments, the data collection data model 337 includes recording attributes 339, equipment identifiers 341, modalities recorded 343, and data storage attributes 345. In particular embodiments, equipment attributes 341 include an amplifier identifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes like EEG cap layout, active channels, sampling frequency, and filters used. EOG specific attributes include the number and type of sensors used, location of sensors applied, etc. Eye tracking specific attributes include the type of tracker used, data recording frequency, data being recorded, recording format, etc. According to various embodiments, data storage attributes 345 include file storage conventions (format, naming convention, dating convention), storage location, archival attributes, expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/or identifier, an accessed data collection 353 such as data segments involved (models, databases/cubes, tables, etc.), access security attributes 355 included who has what type of access, and refresh attributes 357 such as the expiry of the query, refresh frequency, etc. Other fields such as push-pull preferences can also be included to identify an auto push reporting driver or a user driven report retrieval system.

FIG. 4 illustrates examples of queries that can be performed to obtain data associated with neuro-response data collection. According to various embodiments, queries are defined from general or customized scripting languages and constructs, visual mechanisms, a library of preset queries, diagnostic querying including drill-down diagnostics, and eliciting what if scenarios. According to various embodiments, subject attributes queries 415 may be configured to obtain data from a neuro-informatics repository using a location 417 or geographic information, session information 421 such as timing information for the data collected. Location information 423 may also be collected. In some examples, a neuro-response data collection mechanism includes GPS or other location detection mechanisms. Demographics attributes 419 include household income, household size and status, education level, age of kids, etc.

Other queries may retrieve stimulus material recorded based on shopping preferences of subject participants, countenance, physiological assessment, completion status. For example, a user may query for data associated with product categories, products shopped, shops frequented, subject eye correction status, color blindness, subject state, signal strength of measured responses, alpha frequency band ringers, muscle movement assessments, segments completed, etc.

Response assessment based queries 437 may include attention scores 439, emotion scores, 441, retention scores 443, and effectiveness scores 445. Such queries may obtain materials that elicited particular scores. Response measure profile based queries may use mean measure thresholds, variance measures, number of peaks detected, etc. Group response queries may include group statistics like mean, variance, kurtosis, p-value, etc., group size, and outlier assessment measures. Still other queries may involve testing attributes like test location, time period, test repetition count, test station, and test operator fields. A variety of types and combinations of types of queries can be used to efficiently extract data.

FIG. 5 illustrates examples of reports that can be generated. According to various embodiments, client assessment summary reports 501 include effectiveness measures 503, component assessment measures 505, and neuro-response data collection measures 507. Effectiveness assessment measures include composite assessment measure(s), industry/category/client specific placement (percentile, ranking, etc.), actionable grouping assessment such as removing material, modifying segments, or fine tuning specific elements, etc, and the evolution of the effectiveness profile over time. In particular embodiments, component assessment reports include component assessment measures like attention, emotional engagement scores, percentile placement, ranking, etc. Component profile measures include time based evolution of the component measures and profile statistical assessments. According to various embodiments, reports include the number of times material is assessed, attributes of the multiple presentations used, evolution of the response assessment measures over the multiple presentations, and usage recommendations.

According to various embodiments, client cumulative reports 511 include media grouped reporting 513 of all stimulus assessed, campaign grouped reporting 515 of stimulus assessed, and time/location grouped reporting 517 of stimulus assessed. According to various embodiments, industry cumulative and syndicated reports 521 include aggregate assessment responses measures 523, top performer lists 525, bottom performer lists 527, outliers 529, and trend reporting 531. In particular embodiments, tracking and reporting includes specific products, categories, companies, brands.

FIG. 6 illustrates one example of evaluation of a virtual reality environment. At 601, user information is received from a subject provided with a neuro-response data collection mechanism. According to various embodiments, the subject provides data including age, gender, income, location, interest, ethnicity, etc. At 603, marketing materials are received. According to various embodiments, marketing materials are received from companies, firms, individuals, etc., seeking to evaluate their products, product labels, displays, brochures, services, offerings, etc., in a virtual reality environment. In particular examples, stimulus material is dynamically generated using information provided by advertisers. According to various embodiments, a virtual reality environment appropriate for the stimulus material is generated at 605. Virtual reality environments may include super market aisles, shopping centers, store shelves, showrooms, trade show floors, offices, etc.

At 607, marketing material is integrated into a virtual reality environment using an appropriate layout. The layout may be predetermined, custom generated, and/or dynamically modifiable. In particular embodiments, the layout may place bottles of soda at the end of a store isle. In other examples, boxes of cereal may be placed on the third shelf by other branded boxes of cereal. According to various embodiments, imperfections and other cues of realism are introduced at 609. At 611, interaction data is received from users exposed to stimulus material. Interaction data may be received from haptic gloves, platforms, sensors, cameras, microphones, platforms, magnetic fields, controllers, etc.

In some examples, neuro-response data is received from the subject neuro-response data collection mechanism. In some particular embodiments, EEG, EOG, pupillary dilation, facial emotion encoding data, video, images, audio, GPS data, etc., can all be transmitted from the subject to a neuro-response data analyzer. In particular embodiments, only EEG data is transmitted. At 613, marketing material and the virtual reality environment is modified based on user interaction. In particular embodiments, products may be manipulated by the user in the virtual reality environment. According to various embodiments, marketing material and/or the virtual reality environment can also be modified based on neuro-response data. In particular embodiments, if a user is determined to be losing interest in a product, a different product may be presented. Alternatively, a different envionment displaing the product may be presented after a transition from one store to another. According to various embodiments, neuro-response and associated data is transmitted directly from an EEG cap wide area network interface to a data analyzer. In particular embodiments, neuro-response and associated data is transmitted to a computer system that then performs compression and filtering of the data before transmitting the data to a data analyzer over a network.

According to various embodiments, data is also passed through a data cleanser to remove noise and artifacts that may make data more difficult to interpret. According to various embodiments, the data cleanser removes EEG electrical activity associated with blinking and other endogenous/exogenous artifacts. Data cleansing may be performed before or after data transmission to a data analyzer.

At 617, neuro-response data is synchronized with timing, environment, and other marketing material data. In particular embodiments, neuro-response data is synchronized with a shared clock source. According to various embodiments, neuro-response data such as EEG and EOG data is tagged to indicate what the subject is viewing or listening to at a particular time.

At 619, data analysis is performed. Data analysis may include intra-modality response synthesis and cross-modality response synthesis to enhance effectiveness measures. It should be noted that in some particular instances, one type of synthesis may be performed without performing other types of synthesis. For example, cross-modality response synthesis may be performed with or without intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis 609. In particular embodiments, a stimulus attributes repository is accessed to obtain attributes and characteristics of the stimulus materials, along with purposes, intents, objectives, etc. In particular embodiments, EEG response data is synthesized to provide an enhanced assessment of effectiveness. According to various embodiments, EEG measures electrical activity resulting from thousands of simultaneous neural processes associated with different portions of the brain. EEG data can be classified in various bands. According to various embodiments, brainwave frequencies include delta, theta, alpha, beta, and gamma frequency ranges. Delta waves are classified as those less than 4 Hz and are prominent during deep sleep. Theta waves have frequencies between 3.5 to 7.5 Hz and are associated with memories, attention, emotions, and sensations. Theta waves are typically prominent during states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around 10 Hz. Alpha waves are prominent during states of relaxation. Beta waves have a frequency range between 14 and 30 Hz. Beta waves are prominent during states of motor control, long range synchronization between brain areas, analytical problem solving, judgment, and decision making Gamma waves occur between 30 and 60 Hz and are involved in binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function, as well as in attention and memory. Because the skull and dermal layers attenuate waves in this frequency range, brain waves above 75-80 Hz are difficult to detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present invention recognize that analyzing high gamma band (kappa-band: Above 60 Hz) measurements, in addition to theta, alpha, beta, and low gamma band measurements, enhances neurological attention, emotional engagement and retention component estimates. In particular embodiments, EEG measurements including difficult to detect high gamma or kappa band measurements are obtained, enhanced, and evaluated. Subject and task specific signature sub-bands in the theta, alpha, beta, gamma and kappa bands are identified to provide enhanced response estimates. According to various embodiments, high gamma waves (kappa-band) above 80 Hz (typically detectable with sub-cranial EEG and/or magnetoencephalograophy) can be used in inverse model-based enhancement of the frequency responses to the stimuli.

Various embodiments of the present invention recognize that particular sub-bands within each frequency range have particular prominence during certain activities. A subset of the frequencies in a particular band is referred to herein as a sub-band. For example, a sub-band may include the 40-45 Hz range within the gamma band. In particular embodiments, multiple sub-bands within the different bands are selected while remaining frequencies are band pass filtered. In particular embodiments, multiple sub-band responses may be enhanced, while the remaining frequency responses may be attenuated.

An information theory based band-weighting model is used for adaptive extraction of selective dataset specific, subject specific, task specific bands to enhance the effectiveness measure. Adaptive extraction may be performed using fuzzy scaling. Stimuli can be presented and enhanced measurements determined multiple times to determine the variation profiles across multiple presentations. Determining various profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. The synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. According to various embodiments, the synchronous response may be determined for multiple subjects residing in separate locations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should be recognized that any number of mechanisms can be applied—in sequence or in parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhanced significance data, additional cross-modality synthesis mechanisms can also be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR, EOG, and facial emotion encoding are connected to a cross-modality synthesis mechanism. Other mechanisms as well as variations and enhancements on existing mechanisms may also be included. According to various embodiments, data from a specific modality can be enhanced using data from one or more other modalities. In particular embodiments, EEG typically makes frequency measurements in different bands like alpha, beta and gamma to provide estimates of significance. However, the techniques of the present invention recognize that significance measures can be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance the valence of the EEG emotional engagement measure. EOG and eye tracking saccadic measures of object entities can be used to enhance the EEG estimates of significance including but not limited to attention, emotional engagement, and memory retention. According to various embodiments, a cross-modality synthesis mechanism performs time and phase shifting of data to allow data from different modalities to align. In some examples, it is recognized that an EEG response will often occur hundreds of milliseconds before a facial emotion measurement changes. Correlations can be drawn and time and phase shifts made on an individual as well as a group basis. In other examples, saccadic eye movements may be determined as occurring before and after particular EEG responses. According to various embodiments, time corrected GSR measures are used to scale and enhance the EEG estimates of significance including attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domain difference event-related potential components (like the DERP) in specific regions correlates with subject responsiveness to specific stimulus. According to various embodiments, ERP measures are enhanced using EEG time-frequency measures (ERPSP) in response to the presentation of the marketing and entertainment stimuli. Specific portions are extracted and isolated to identify ERP, DERP and ERPSP analyses to perform. In particular embodiments, an EEG frequency estimation of attention, emotion and memory retention (ERPSP) is used as a co-factor in enhancing the ERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specific objects of stimulus. Eye tracking measures the subject's gaze path, location and dwell on specific objects of stimulus. According to various embodiments, EOG and eye tracking is enhanced by measuring the presence of lambda waves (a neurophysiological index of saccade effectiveness) in the ongoing EEG in the occipital and extra striate regions, triggered by the slope of saccade-onset to estimate the significance of the EOG and eye tracking measures. In particular embodiments, specific EEG signatures of activity such as slow potential shifts and measures of coherence in time-frequency responses at the Frontal Eye Field (FEF) regions that preceded saccade-onset are measured to enhance the effectiveness of the saccadic activity data.

According to various embodiments, facial emotion encoding uses templates generated by measuring facial muscle positions and movements of individuals expressing various emotions prior to the testing session. These individual specific facial emotion encoding templates are matched with the individual responses to identify subject emotional response. In particular embodiments, these facial emotion encoding measurements are enhanced by evaluating inter-hemispherical asymmetries in EEG responses in specific frequency bands and measuring frequency band interactions. The techniques of the present invention recognize that not only are particular frequency bands significant in EEG responses, but particular frequency bands used for communication between particular areas of the brain are significant. Consequently, these EEG responses enhance the EMG, graphic and video based facial emotion identification.

Integrated responses are generated at 621. According to various embodiments, the data communication device transmits data to the response integration using protocols such as the File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP) along with a variety of conventional, bus, wired network, wireless network, satellite, and proprietary communication protocols. The data transmitted can include the data in its entirety, excerpts of data, converted data, and/or elicited response measures. According to various embodiments, data is sent using a telecommunications, wireless, Internet, satellite, or any other communication mechanisms that is capable of conveying information from multiple subject locations for data integration and analysis. The mechanism may be integrated in a set top box, computer system, receiver, mobile device, etc.

In particular embodiments, the data communication device sends data to the response integration system. According to various embodiments, the response integration system combines analyzed and enhanced responses to the stimulus material while using information about stimulus material attributes. In particular embodiments, the response integration system also collects and integrates user behavioral and survey responses with the analyzed and enhanced response data to more effectively measure and track neuro-responses to stimulus materials. According to various embodiments, the response integration system obtains attributes such as requirements and purposes of the stimulus material presented.

Some of these requirements and purposes may be obtained from a variety of databases. According to various embodiments, the response integration system also includes mechanisms for the collection and storage of demographic, statistical and/or survey based responses to different entertainment, marketing, advertising and other audio/visual/tactile/olfactory material. If this information is stored externally, the response integration system can include a mechanism for the push and/or pull integration of the data, such as querying, extraction, recording, modification, and/or updating.

The response integration system can further include an adaptive learning component that refines user or group profiles and tracks variations in the neuro-response data collection system to particular stimuli or series of stimuli over time. This information can be made available for other purposes, such as use of the information for presentation attribute decision making. According to various embodiments, the response integration system builds and uses responses of users having similar profiles and demographics to provide integrated responses at 621. In particular embodiments, stimulus and response data is stored in a repository at 623 for later retrieval and analysis.

According to various embodiments, various mechanisms such as the data collection mechanisms, the intra-modality synthesis mechanisms, cross-modality synthesis mechanisms, etc. are implemented on multiple devices. However, it is also possible that the various mechanisms be implemented in hardware, firmware, and/or software in a single system. FIG. 7 provides one example of a system that can be used to implement one or more mechanisms. For example, the system shown in FIG. 7 may be used to implement a data analyzer.

According to particular example embodiments, a system 700 suitable for implementing particular embodiments of the present invention includes a processor 701, a memory 703, an interface 711, and a bus 715 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor 701 is responsible for such tasks such as pattern generation. Various specially configured devices can also be used in place of a processor 701 or in addition to processor 701. The complete implementation can also be done in custom hardware. The interface 711 is typically configured to send and receive data packets or data segments over a network. Particular examples of interfaces the device supports include host bus adapter (HBA) interfaces, Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such as fast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as data synthesis.

According to particular example embodiments, the system 700 uses memory 703 to store data, algorithms and program instructions. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store received data and process received data.

Because such information and program instructions may be employed to implement the systems/methods described herein, the present invention relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein. Examples of machine-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.

Although the foregoing invention has been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Therefore, the present embodiments are to be considered as illustrative and not restrictive and the invention is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

1. A method comprising:

generating a virtual reality environment using a processor;
receiving marketing materials and a layout for the marketing materials for presentation in the virtual reality environment;
integrating the marketing materials and the virtual reality environment into a virtual reality environment representation;
introducing imperfections into the virtual reality environment representation, wherein the imperfections include one or more marketing materials placed slightly askew; and
determining an effectiveness of the marketing materials in the virtual reality environment representation.

2. The method of claim 1, wherein the effectiveness of the marketing materials is based on neuro-response data gathered from one or more subjects exposed to the marketing materials in the virtual reality environment representation.

3. The method of claim 1, wherein the marketing materials comprise one or more of a product label, a product, a service offering, a sign, a placard or a package.

4. The method of claim 1, wherein the imperfections further include one or more marketing materials with a product label blemish.

5. The method of claim 1, further comprising determining an effectiveness of the virtual reality environment representation.

6. The method of claim 5, wherein the effectiveness of the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to the virtual reality environment representation.

7. A method comprising:

generating a virtual reality environment using a processor;
receiving marketing materials and a layout for the marketing materials for presentation in the virtual reality environment;
integrating the marketing materials and the virtual reality environment into a virtual reality environment representation;
introducing imperfections into the virtual reality environment representation, wherein the imperfections include one or more misaligned marketing materials; and
determining an effectiveness of the marketing materials in the virtual reality environment representation.

8. The method of claim 7, wherein the effectiveness of the marketing materials is based on neuro-response data gathered from one or more subjects exposed to the marketing materials in the virtual reality environment representation.

9. The method of claim 7, further comprising determining an effectiveness of the virtual reality environment representation.

10. The method of claim 9, wherein the effectiveness of the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to the virtual reality environment representation.

11. A method comprising:

generating a virtual reality environment using a processor;
receiving marketing materials and a layout for the marketing materials for presentation in the virtual reality environment;
integrating the marketing materials and the virtual reality environment into a virtual reality environment representation;
introducing imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material;
determining an effectiveness of the marketing materials in the virtual reality environment representation; and
determining an effectiveness of the virtual reality environment representation,
wherein one or more of the effectiveness of the marketing materials or the effectiveness of the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation, and
wherein neuro-response data is gathered using a plurality of modalities including Electronencephalography and Electrooculography.

12. A method comprising:

generating a virtual reality environment using a processor;
receiving marketing materials and a layout for the marketing materials for presentation in the virtual reality environment;
integrating the marketing materials and the virtual reality environment into a virtual reality environment representation;
introducing imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material;
obtaining neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation;
obtaining event related potential measurements from the neuro-response data at multiple regions of the brain;
determining an effectiveness of the marketing materials in the virtual reality environment representation; and
determining an effectiveness of the virtual reality environment representation, wherein one or more of the effectiveness of the marketing materials or the effectiveness of the virtual reality environment representation is based on the event related potential measurements.

13. A method comprising:

generating a virtual reality environment using a processor;
receiving marketing materials and a layout for the marketing materials for presentation in the virtual reality environment;
integrating the marketing materials and the virtual reality environment into a virtual reality environment representation;
introducing imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material;
obtaining neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation;
obtaining event related time-frequency analysis of the neuro-response data to assess one or more of attention, emotion or memory retention across multiple frequency bands;
determining an effectiveness of the marketing materials in the virtual reality environment representation;
determining an effectiveness of the virtual reality environment representation, wherein the effectiveness of one or more of the marketing materials or the virtual reality environment representation is determined based on the event related time-frequency analysis.

14. A system comprising:

an interface to receive marketing materials and a layout for the marketing materials for presentation in a virtual reality environment; and
a processor to: generate the virtual reality environment; integrate the marketing materials and the virtual reality environment into a virtual reality environment representation; introduce imperfections into the virtual reality environment representation, wherein the imperfections include one or more marketing materials placed slightly askew; and determine an effectiveness of the marketing materials in the virtual reality environment representation.

15. The system of claim 14, wherein the effectiveness of the marketing materials is based on neuro-response data gathered from one or more subjects exposed to the marketing materials in the virtual reality environment representation.

16. The system of claim 14, wherein the marketing materials comprise one or more of a product label, a product, a service offering, a sign, a placard or a package.

17. The system of claim 14, wherein the imperfections include one or more marketing materials with a product label blemish.

18. The system of claim 14, wherein the processor is to determine an effectiveness of the virtual reality environment representation.

19. The system of claim 18, wherein the effectiveness of the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to the virtual reality environment representation.

20. A system comprising:

an interface to receive marketing materials and a layout for the marketing materials for presentation in a virtual reality environment; and
a processor to: generate the virtual reality environment; integrate the marketing materials and the virtual reality environment into a virtual reality environment representation; introduce imperfections into the virtual reality environment representation, wherein the imperfections include one or more misaligned marketing materials; and determine an effectiveness of the marketing materials in the virtual reality environment representation.

21. The system of claim 20, wherein the effectiveness of the marketing materials is based on neuro-response data gathered from one or more subjects exposed to the marketing materials in the virtual reality environment representation.

22. The system of claim 20, wherein the processor is to determine an effectiveness of the virtual reality environment representation.

23. The system of claim 22, wherein the effectiveness of the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to the virtual reality environment representation.

24. A system comprising:

an interface to receive marketing materials and a layout for the marketing materials for presentation in a virtual reality environment; and
a processor to: generate the virtual reality environment; integrate the marketing materials and the virtual reality environment into a virtual reality environment representation; introduce imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material; determine an effectiveness of the marketing materials in the virtual reality environment representation; and determine an effectiveness of the virtual reality environment representation, wherein the effectiveness of one or more of the marketing materials or the virtual reality environment representation is based on neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation, and
wherein neuro-response data is gathered using a plurality of modalities including Electronencephalography and Electrooculography.

25. A system comprising:

an interface to receive marketing materials and a layout for the marketing materials for presentation in a virtual reality environment; and
a processor to: generate the virtual reality environment; integrate the marketing materials and the virtual reality environment into a virtual reality environment representation; introduce imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material; obtain neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation; obtain event related potential measurements from the neuro-response data at multiple regions of the brain; determine an effectiveness of the marketing materials in the virtual reality environment representation; and determine an effectiveness of the virtual reality environment representation, wherein one or more of the effectiveness of the marketing materials or the effectiveness of the virtual reality environment representation is based on the event related potential measurements.

26. A system comprising:

an interface to receive marketing materials and a layout for the marketing materials for presentation in a virtual reality environment; and
a processor to: generate the virtual reality environment; integrate the marketing materials and the virtual reality environment into a virtual reality environment representation; introduce imperfections into the virtual reality environment representation, wherein the imperfections include one or more of a marketing material placed slightly askew or a misaligned marketing material; obtain neuro-response data gathered from one or more subjects exposed to one or more of the virtual reality environment representation or the marketing materials in the virtual reality environment representation; obtain event related time-frequency analysis of the neuro-response data to assess one or more of attention, emotion or memory retention across multiple frequency bands; determine an effectiveness of the marketing materials in the virtual reality environment representation; and determining an effectiveness of the virtual reality environment representation, wherein the effectiveness of one or more of the marketing materials or the virtual reality environment representation is determined based on the event related time-frequency analysis.
Referenced Cited
U.S. Patent Documents
2549836 April 1951 McIntyre et al.
3490439 January 1970 Rolston
3572322 March 1971 Wade
3735753 May 1973 Pisarski
3901215 August 1975 John
3998213 December 21, 1976 Price
4149716 April 17, 1979 Scudder
4279258 July 21, 1981 John
4411273 October 25, 1983 John
4417592 November 29, 1983 John
4537198 August 27, 1985 Corbett
4557270 December 10, 1985 John
4610259 September 9, 1986 Cohen et al.
4632122 December 30, 1986 Johansson et al.
4683892 August 4, 1987 Johansson et al.
4695879 September 22, 1987 Weinblatt
4736751 April 12, 1988 Gevins et al.
4800888 January 31, 1989 Itil et al.
4802484 February 7, 1989 Friedman et al.
4846190 July 11, 1989 John
4885687 December 5, 1989 Carey
4894777 January 16, 1990 Negishi et al.
4913160 April 3, 1990 John
4955388 September 11, 1990 Silberstein
4967038 October 30, 1990 Gevins et al.
4987903 January 29, 1991 Keppel et al.
5003986 April 2, 1991 Finitzo et al.
5038782 August 13, 1991 Gevins et al.
5083571 January 28, 1992 Prichep
RE34015 August 4, 1992 Duffy
5137027 August 11, 1992 Rosenfeld
5213338 May 25, 1993 Brotz
5226177 July 6, 1993 Nickerson
5243517 September 7, 1993 Schmidt et al.
5291888 March 8, 1994 Tucker
5293867 March 15, 1994 Oommen
5295491 March 22, 1994 Gevins
5357957 October 25, 1994 Itil et al.
5363858 November 15, 1994 Farwell
5392788 February 28, 1995 Hudspeth
5406956 April 18, 1995 Farwell
5447166 September 5, 1995 Gevins
5474082 December 12, 1995 Junker
5479934 January 2, 1996 Imran
5518007 May 21, 1996 Becker
5537618 July 16, 1996 Boulton et al.
5655534 August 12, 1997 Ilmoniemi
5676138 October 14, 1997 Zawilinski
5720619 February 24, 1998 Fisslinger
5724987 March 10, 1998 Gevins et al.
5729205 March 17, 1998 Kwon
5762611 June 9, 1998 Lewis et al.
5771897 June 30, 1998 Zufrin
5787187 July 28, 1998 Bouchard et al.
5800351 September 1, 1998 Mann
5812642 September 22, 1998 Leroy
5817029 October 6, 1998 Gevins et al.
5848399 December 8, 1998 Burke
5961332 October 5, 1999 Joao
5983129 November 9, 1999 Cowan et al.
6001065 December 14, 1999 DeVito
6052619 April 18, 2000 John
6099319 August 8, 2000 Zaltman
6120440 September 19, 2000 Goknar
6128521 October 3, 2000 Marro et al.
6154669 November 28, 2000 Hunter et al.
6161030 December 12, 2000 Levendowski et al.
6173260 January 9, 2001 Slaney
6175753 January 16, 2001 Menkes et al.
6228038 May 8, 2001 Claessens
6236885 May 22, 2001 Hunter et al.
6254536 July 3, 2001 DeVito
6280198 August 28, 2001 Calhoun et al.
6286005 September 4, 2001 Cannon
6289234 September 11, 2001 Mueller
6292688 September 18, 2001 Patton
6301493 October 9, 2001 Marro et al.
6315569 November 13, 2001 Zaltman
6330470 December 11, 2001 Tucker et al.
6334778 January 1, 2002 Brown
6374143 April 16, 2002 Berrang et al.
6381481 April 30, 2002 Levendowski et al.
6398643 June 4, 2002 Knowles et al.
6422999 July 23, 2002 Hill
6434419 August 13, 2002 Gevins et al.
6453194 September 17, 2002 Hill
6487444 November 26, 2002 Mimura
6488617 December 3, 2002 Katz
6510340 January 21, 2003 Jordan
6520905 February 18, 2003 Surve et al.
6545685 April 8, 2003 Dorbie
6577329 June 10, 2003 Flickner et al.
6585521 July 1, 2003 Obrador
6594521 July 15, 2003 Tucker
6598006 July 22, 2003 Honda et al.
6654626 November 25, 2003 Devlin et al.
6662052 December 9, 2003 Sarwal et al.
6665560 December 16, 2003 Becker et al.
6688890 February 10, 2004 von Buegner
6708051 March 16, 2004 Durousseau
6712468 March 30, 2004 Edwards
6754524 June 22, 2004 Johnson, Jr.
6757556 June 29, 2004 Gopinathan et al.
6788882 September 7, 2004 Geer et al.
6792304 September 14, 2004 Silberstein
6842877 January 11, 2005 Robarts et al.
6904408 June 7, 2005 McCarthy et al.
6950698 September 27, 2005 Sarkela et al.
6973342 December 6, 2005 Swanson
6993380 January 31, 2006 Modarres
7120880 October 10, 2006 Dryer et al.
7130673 October 31, 2006 Tolvanen-Laakso et al.
7150715 December 19, 2006 Collura et al.
7164967 January 16, 2007 Etienne-Cummings et al.
7177675 February 13, 2007 Suffin et al.
7286871 October 23, 2007 Cohen
7340060 March 4, 2008 Tomkins et al.
7496400 February 24, 2009 Hoskonen et al.
7548774 June 16, 2009 Kurtz et al.
7551952 June 23, 2009 Gevins et al.
7623823 November 24, 2009 Zito et al.
7636456 December 22, 2009 Collins et al.
7689272 March 30, 2010 Farwell
7697979 April 13, 2010 Martinerie et al.
7698238 April 13, 2010 Barletta et al.
7720351 May 18, 2010 Levitan
7729755 June 1, 2010 Laken
7809420 October 5, 2010 Hannula et al.
7840248 November 23, 2010 Fuchs et al.
7840250 November 23, 2010 Tucker
7865394 January 4, 2011 Calloway
7917366 March 29, 2011 Levanon et al.
7988557 August 2, 2011 Soderland
8014847 September 6, 2011 Shastri et al.
8055722 November 8, 2011 Hille
8069125 November 29, 2011 Jung et al.
8082215 December 20, 2011 Jung et al.
8086563 December 27, 2011 Jung et al.
8103328 January 24, 2012 Turner et al.
20010020236 September 6, 2001 Cannon
20010056225 December 27, 2001 DeVito
20020065826 May 30, 2002 Bell et al.
20020072952 June 13, 2002 Hamzey et al.
20020077534 June 20, 2002 DuRousseau
20020155878 October 24, 2002 Lert, Jr. et al.
20020156842 October 24, 2002 Signes et al.
20020188217 December 12, 2002 Farwell
20020193670 December 19, 2002 Garfield et al.
20030013981 January 16, 2003 Gevins et al.
20030036955 February 20, 2003 Tanaka et al.
20030059750 March 27, 2003 Bindler et al.
20030100998 May 29, 2003 Brunner et al.
20030104865 June 5, 2003 Itkis et al.
20030165270 September 4, 2003 Endrikhovski et al.
20030233278 December 18, 2003 Marshall
20040005143 January 8, 2004 Tsuru et al.
20040015608 January 22, 2004 Ellis et al.
20040073129 April 15, 2004 Caldwell et al.
20040092809 May 13, 2004 DeCharms
20040098298 May 20, 2004 Yin
20040187167 September 23, 2004 Maguire et al.
20040210159 October 21, 2004 Kibar et al.
20040220483 November 4, 2004 Yeo et al.
20050010475 January 13, 2005 Perkowski et al.
20050076359 April 7, 2005 Pierson et al.
20050079474 April 14, 2005 Lowe
20050097594 May 5, 2005 O'Donnell et al.
20050107716 May 19, 2005 Eaton et al.
20050143629 June 30, 2005 Farwell
20050154290 July 14, 2005 Langleben
20050177058 August 11, 2005 Sobell
20050197590 September 8, 2005 Osorio et al.
20050223237 October 6, 2005 Barletta et al.
20050227233 October 13, 2005 Buxton et al.
20050240956 October 27, 2005 Smith et al.
20050273017 December 8, 2005 Gordon
20050288954 December 29, 2005 McCarthy et al.
20050289582 December 29, 2005 Tavares et al.
20060035707 February 16, 2006 Nguyen et al.
20060093998 May 4, 2006 Vertegaal
20060111644 May 25, 2006 Guttag et al.
20060129458 June 15, 2006 Maggio
20060167376 July 27, 2006 Viirre et al.
20060168630 July 27, 2006 Davies
20060256133 November 16, 2006 Rosenberg
20060257834 November 16, 2006 Lee et al.
20060259360 November 16, 2006 Flinn et al.
20060293921 December 28, 2006 McCarthy et al.
20070055169 March 8, 2007 Lee et al.
20070066874 March 22, 2007 Cook
20070066915 March 22, 2007 Frei et al.
20070066916 March 22, 2007 Lemos
20070078706 April 5, 2007 Datta et al.
20070079331 April 5, 2007 Datta et al.
20070106170 May 10, 2007 Dunseath, Jr. et al.
20070135727 June 14, 2007 Virtanen et al.
20070225585 September 27, 2007 Washbon et al.
20070235716 October 11, 2007 Delic et al.
20070238945 October 11, 2007 Delic et al.
20070250846 October 25, 2007 Swix et al.
20070265507 November 15, 2007 de Lemos
20080001600 January 3, 2008 deCharms
20080027345 January 31, 2008 Kumada et al.
20080040740 February 14, 2008 Plotnick et al.
20080059997 March 6, 2008 Plotnick et al.
20080065468 March 13, 2008 Berg et al.
20080081961 April 3, 2008 Westbrook et al.
20080082019 April 3, 2008 Ludving et al.
20080091512 April 17, 2008 Marci et al.
20080097854 April 24, 2008 Young
20080109840 May 8, 2008 Walter et al.
20080125110 May 29, 2008 Ritter
20080147488 June 19, 2008 Tunick et al.
20080152300 June 26, 2008 Knee et al.
20080208072 August 28, 2008 Fadem et al.
20080214902 September 4, 2008 Lee et al.
20080221400 September 11, 2008 Lee et al.
20080221472 September 11, 2008 Lee et al.
20080221969 September 11, 2008 Lee et al.
20080222670 September 11, 2008 Lee et al.
20080222671 September 11, 2008 Lee et al.
20080228077 September 18, 2008 Wilk et al.
20080255949 October 16, 2008 Genco et al.
20080295126 November 27, 2008 Lee et al.
20090024049 January 22, 2009 Pradeep et al.
20090024447 January 22, 2009 Pradeep et al.
20090024448 January 22, 2009 Pradeep et al.
20090024449 January 22, 2009 Pradeep et al.
20090024475 January 22, 2009 Pradeep et al.
20090025023 January 22, 2009 Pradeep et al.
20090025024 January 22, 2009 Beser et al.
20090030287 January 29, 2009 Pradeep et al.
20090030303 January 29, 2009 Pradeep et al.
20090030717 January 29, 2009 Pradeep et al.
20090030762 January 29, 2009 Lee et al.
20090030930 January 29, 2009 Pradeep et al.
20090036755 February 5, 2009 Pradeep et al.
20090036756 February 5, 2009 Pradeep et al.
20090060240 March 5, 2009 Coughlan et al.
20090062629 March 5, 2009 Pradeep et al.
20090062679 March 5, 2009 Tan et al.
20090062681 March 5, 2009 Pradeep et al.
20090063255 March 5, 2009 Pradeep et al.
20090063256 March 5, 2009 Pradeep et al.
20090069652 March 12, 2009 Lee et al.
20090070798 March 12, 2009 Lee et al.
20090082643 March 26, 2009 Pradeep et al.
20090083129 March 26, 2009 Pradeep et al.
20090088610 April 2, 2009 Lee et al.
20090094286 April 9, 2009 Lee et al.
20090094627 April 9, 2009 Lee et al.
20090094628 April 9, 2009 Lee et al.
20090094629 April 9, 2009 Lee et al.
20090097689 April 16, 2009 Prest et al.
20090112077 April 30, 2009 Nguyen et al.
20090131764 May 21, 2009 Lee et al.
20090133047 May 21, 2009 Lee et al.
20090150919 June 11, 2009 Lee et al.
20090158308 June 18, 2009 Weitzenfeld et al.
20090195392 August 6, 2009 Zalewski
20090214060 August 27, 2009 Chuang et al.
20090248496 October 1, 2009 Hueter et al.
20090253996 October 8, 2009 Lee et al.
20090259137 October 15, 2009 Delic et al.
20090318773 December 24, 2009 Jung et al.
20090318826 December 24, 2009 Green et al.
20090327068 December 31, 2009 Pradeep et al.
20090328089 December 31, 2009 Pradeep et al.
20100004977 January 7, 2010 Marci et al.
20100022821 January 28, 2010 Dubi et al.
20100041962 February 18, 2010 Causevic et al.
20100060300 March 11, 2010 Mueller et al.
20100125219 May 20, 2010 Harris et al.
20100145176 June 10, 2010 Himes
20100145215 June 10, 2010 Pradeep et al.
20100145217 June 10, 2010 Otto et al.
20100183279 July 22, 2010 Pradeep et al.
20100186031 July 22, 2010 Pradeep et al.
20100186032 July 22, 2010 Pradeep et al.
20100198042 August 5, 2010 Popescu et al.
20100214318 August 26, 2010 Pradeep et al.
20100215289 August 26, 2010 Pradeep et al.
20100249538 September 30, 2010 Pradeep et al.
20100249636 September 30, 2010 Pradeep et al.
20100250325 September 30, 2010 Pradeep et al.
20100250458 September 30, 2010 Ho
20100257052 October 7, 2010 Zito et al.
20100274152 October 28, 2010 McPeck et al.
20100274153 October 28, 2010 Tucker et al.
20100331661 December 30, 2010 Nakagawa
20110004089 January 6, 2011 Chou
20110015503 January 20, 2011 Joffe et al.
20110040202 February 17, 2011 Luo et al.
20110046473 February 24, 2011 Pradeep et al.
20110046502 February 24, 2011 Pradeep et al.
20110046503 February 24, 2011 Pradeep et al.
20110046504 February 24, 2011 Pradeep et al.
20110047121 February 24, 2011 Pradeep et al.
20110059422 March 10, 2011 Masaoka
20110085700 April 14, 2011 Lee
20110098593 April 28, 2011 Low et al.
20110105937 May 5, 2011 Pradeep et al.
20110106621 May 5, 2011 Pradeep et al.
20110106750 May 5, 2011 Pradeep et al.
20110119124 May 19, 2011 Pradeep et al.
20110119129 May 19, 2011 Pradeep et al.
20110131274 June 2, 2011 Hille
20110144519 June 16, 2011 Causevic
20110224569 September 15, 2011 Isenhart et al.
20110237923 September 29, 2011 Picht et al.
20110237971 September 29, 2011 Pradeep et al.
20110248729 October 13, 2011 Mueller et al.
20110257502 October 20, 2011 Lee
20110257937 October 20, 2011 Lee
20110270620 November 3, 2011 Pradeep et al.
20110276504 November 10, 2011 Pradeep et al.
20110282231 November 17, 2011 Pradeep et al.
20110282232 November 17, 2011 Pradeep et al.
20110282749 November 17, 2011 Pradeep et al.
20110298706 December 8, 2011 Mann
20110319975 December 29, 2011 Ho et al.
20120022391 January 26, 2012 Leuthardt
20120036004 February 9, 2012 Pradeep et al.
20120036005 February 9, 2012 Pradeep et al.
20120072289 March 22, 2012 Pradeep et al.
Foreign Patent Documents
1374658 November 1974 GB
2221759 February 1990 GB
97/17774 May 1997 WO
97/40745 November 1997 WO
97/41673 November 1997 WO
2004/049225 June 2004 WO
2008-109694 September 2008 WO
2008-109699 September 2008 WO
2008/121651 October 2008 WO
2008/137579 November 2008 WO
2008/154410 December 2008 WO
2009/018374 February 2009 WO
2009/052833 April 2009 WO
2011-055291 May 2011 WO
2011-056679 May 2011 WO
Other references
  • Definition of “virtual reality”, downloaded Feb. 25, 2012 from the Merriam Webster online dictionary: http://www.merriam-webster.com/dictionary/virtual%20reality.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,211, on Jul. 8, 2011, 16 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,211, on Jan. 7, 2011, 19 pages.
  • Office Action issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,221, on Apr. 15, 2011, 24 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/113,863, on Jun. 9, 2011, 12 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/113,863, on Dec. 27, 2010, 15 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/113,870, on Apr. 21, 2011, 10 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/113,870, on Dec. 3, 2010, 12 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,240, on Jun. 10, 2011, 12 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,262, on May 26, 2011, 15 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,262, on Dec. 9, 2010, 13 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,066, on Jan. 21, 2011, 16 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,066, on Oct. 28, 2010, 14 pages.
  • Notice of Panel Decision from Pre-Appeal Brief Review, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,066, on May 31, 2011, 2 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,074, on Dec. 23, 2010, 14 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,074, on Jun. 9, 2011, 10 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/182,874, on Jul. 7, 2011, 14 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/182,874, on Dec. 27, 2010, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,557, on Dec. 27, 2010, 14 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,557, on Jun. 9, 2011, 12 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,583, on Jun. 21, 2011, 14 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,583, on Dec. 27, 2010, 17 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,596, on Jun. 14, 2011, 13 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,596, on Dec. 27, 2010, 17 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/200,813, on Jul. 6, 2011, 13 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/200,813, on Dec. 27, 2010, 14 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/234,372, on Jun. 7, 2011, 10 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,069, on Feb. 17, 2011, 32 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,069, on Oct. 29, 2010, 21 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,315, on May 4, 2011, 9 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/410,380, on Jun. 7, 2011, 9 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/413,297, on Jul. 18, 2011, 9 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/608,685,on Jul. 12, 2011, 15 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,190,on Aug. 10, 2011, 28 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,322,on Aug. 23, 2011, 12 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,069,on Aug. 26, 2011, 33 pages.
  • Restriction Requirement, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,253,on Sep. 2, 2011, 7 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/410,372, on Sep. 12, 2011, 12 pages.
  • Restriction Requirement, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/182,851, on Sep. 12, 2011, 7 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,066, on Sep. 29, 2011, 37 pages.
  • Restriction Requirement, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,225, on Oct. 3, 2011, 6 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/234,388, on Oct. 12, 2011, 27 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/234,372, on Oct. 13, 2011, 22 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/410,380, on Oct. 19, 2011, 21 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/058264, on Sep. 29, 2009, 1 page.
  • International Search Report, issued by the International Searching Authority in connection with International Application No. PCT/US08/058264, on Aug. 1, 2008, 2 pages.
  • Written Opinion, issued by the International Searching Authority in connection with International Application No. PCT/US08/058264, on Aug. 1, 2008, 5 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/062273, on Nov. 3, 2009, 1 page.
  • International Search Report, issued by the International Searching Authority in connection with International Application No. PCT/US08/062273, on Sep. 5, 2008, 2 pages.
  • Written Opinion, issued by the International Searching Authority in connection with International Application No. PCT/US08/062273, on Sep. 5, 2008, 4 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/062275, on Nov. 3, 2009, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/062275, on Sep. 22, 2008, 2 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/062275, on Sep. 22, 2008, 6 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/063984, on Nov. 17, 2009, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/063984, on Sep. 29, 2008, 3 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/063984, on Sep. 29, 2008, 4 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/063989, on Nov. 17, 2009, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/063989, on Jul. 17, 2008, 2 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/063989, on Jul. 17, 2008, 4 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/066166, on Dec. 7, 2009, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/066166, on Aug. 25, 2008, 2 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/066166, on Aug. 25, 2008, 6 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/071639, on Feb. 2, 2010, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/071639, on Oct. 22, 2008, 3 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/071639, on Oct. 22, 2008, 4 pages.
  • International Preliminary Report on Patentability, issued by the International Bureau of WIPO in connection with International Application No. PCT/US08/074467, on Mar. 2, 2010, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US08/074467, on Nov. 17, 2008, 2 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US08/074467, on Nov. 17, 2008, 4 pages.
  • International Preliminary Report of Patentability, issued by the International Bureau in connection with International Application No. PCT/US10/021535, on Jul. 26, 2011, 1 page.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US10/021535, on Mar. 23, 2010, 3 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US10/021535, on Mar. 23, 2010, 4 pages.
  • International Preliminary Report of Patentability, issued by the International Bureau in connection with International Application No. PCT/US09/065368, on Jun. 23, 2011, 2 pages.
  • International Search Report, issued by the International Bureau in connection with International Application No. PCT/US09/065368, on Jan. 21, 2010, 3 pages.
  • Written Opinion, issued by the International Bureau in connection with International Application No. PCT/US09/065368, on Jan. 21, 2010, 7 pages.
  • Extended European Search Report, issued by the European Patent Office in connection with European Application No. 08744383.4-2221/2130146, on Jul. 27, 2011, 6 pages.
  • Extended European Search Report, issued by the European Patent Office in connection with European Application No. 10173095.0-2221, on Dec. 17, 2010, 3 pages.
  • Extended European Search Report, issued by the European Patent Office in connection with European Application No. 10189294.1-2221, on Mar. 21, 2011, 7 pages.
  • First Office Action, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 200880104982.1, on Jan. 25, 2011, 15 pages.
  • First Office Action, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 2008801015007, on May 25, 2011, 8 pages.
  • First Office Action, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 200880019166.0, on Jul. 22, 2011, 16 pages.
  • Decision of Rejection, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 200880104982.1, on Sep. 23, 2011, 10 pages.
  • Edgar, et al., “Digital Filters in ERP Research,” in Event-Related Potentials: A Methods Handbook pp. 85-113, (Todd C. Handy, ed., 2005), 15 pages.
  • Simon-Thomas, et al, “Behavioral and Electrophysiological Evidence of a Right Hemisphere Bias for the Influence of Negative Emotion on Higher Cognition,” Journal of Cognitive Neuroscience, pp. 518-529, Massachusetts Institute of Technology (2005), 12 pages.
  • Flinker, A. et al, “Sub-centimeter language organization in the human temporal lobe,” Brain and Language, Elsevier Inc., (2010), doi.org/10.1016/j.band1.2010.09.009, 7 pages.
  • Friedman, et al., “Event-Related Potential (ERP) Studies of Memory Encoding and Retrieval: A Selective Review,” Microscopy Research and Technique 51:6-26, Wiley-Less, Inc. (2000), 23 pages.
  • Gaillard, “Problems and Paradigms in ERP Research,” Biological Psychology, Elsevier Science Publisher B.V. (1988), 10 pages.
  • Hopf, et al., “Neural Sources of Focused Attention in Visual Search,” Cerebral Cortex, 10:1233-1241, Oxford University Press, (Dec. 2000), 9 pages.
  • Swick, et al., “Contributions of Prefrontal Cortex to Recognition Memory: Electrophysiological and Behavioral Evidence,” Neuropsychology, vol. 13, No. 2, pp. 155-170, American Psychological Association, Inc. (1999), 16 pages.
  • Luck, et al., “The sped of visual attention in schizophrenia: Electrophysiological and behavioral evidence,” Schizophrenia Research, pp. 174-195, Elsevier B.V. www.sciencedirect.com, (2006), 22 pages.
  • Makeig, et al., “Mining event-related brain dynamics,” TRENDS in Cognitive Sciences, vol. 8, No. 5, (May 2004), www.sciencedirect.com, 7 pages.
  • Herrmann, et al., “Mechanisms of human attention: event-related potentials and oscillations,” Neuroscience and Biobehavioral Reviews, pp. 465-476, Elsevier Science Ltd., www.elsvevier.com/locate/neubiorev, (2001), 12 pages.
  • Knight, “Consciousness Unchained: Ethical Issues and the Vegetative and minimally Conscious State,” The American Journal of Bioethics, 8:9, 1-2, http://dx.doi.org/10.1080/15265160802414524, (Sep. 1, 2008), 3 pages.
  • Kishiyama, et al., “Novelty Enhancements in Memory Are Dependent on Lateral Prefrontal Cortex,” The Journal of Neuroscience, pp. 8114-8118, Society for Neuroscience (Jun. 24, 2009), 5 pages.
  • Paller, et al., “Validating neural correlates of familiarity,” TRENDS in Cognitive Sciences, vol. 11, No. 6, www.sciencedirect.com, (May 2, 2007), 8 pages.
  • Picton, et al., “Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria,” Psychophysiology, pp. 127-152, Society for Psychophysiological Research, (2000), 26 pages.
  • Yamaguchi, et al., “Rapid-Prefrontal—Hippocampal Habituation to Novel Events,” The Journal of Neuroscience, pp. 5356-5363, Society for Neuroscience, (Apr. 29, 2004), 8 pages.
  • Rugg, et al., “Event-related potentials and recognition memory,” TRENDS in Cognitive Sciences, vol. 11, No. 6, www.sciencedirect.com (May 3, 2007), 7 pages.
  • Rugg, et al., “The ERP and cognitive psychology: conceptual issues,” (Sep. 1996), 7 pages.
  • Keren, et al., “Saccadic spike potentials in gamma-band EEG: Characterization, detection and suppression,” NeuroImage, http://dx.doi:10.1016/j.neuroimage.2009.10.057, (Oct. 2009), 16 pages.
  • Kishiyama, et al., “Socioeconomic Disparities Affect Prefrontal Function in Children,” Journal of Cognitive Neuroscience pp. 1106-1115, Massachusetts Institute of Technology, (2008), 10 pages.
  • Spencer, “Averaging, Detection, and Classification of Single-Trial ERPs,” in Event-Related Potentials: A Methods Handbook, pp. 209-227, (Todd C. Handy, ed., 2005), 10 pages.
  • Srinivasan, “High-Resolution EEG: Theory and Practice,” in Event-Related Potentials: A Methods Handbook, pp. 167-188, (Todd C. Handy, ed., 2005), 12 pages.
  • Taheri, et al., “A dry electrode for EEG recording,” Electroencephalography and clinical Neurophysiology, pp. 376-383, Elsevier Science Ireland Ltd. (1994), 8 pages.
  • Talsma, et al., “Methods for the Estimation and Removal of Artifacts and Overlap in ERP Waveforms,” in Event-Related Potentials: A Methods Handbook, pp. 115-148, (Todd C. Handy, ed., 2005), 22 pages.
  • Davidson, et al., “The functional neuroanatomy of emotion and affective style,” TRENDS in Cognitive Sciences, vol. 3, No. 1, (Jan. 1999), 11 pages.
  • Vogel, et al., “Electrophysiological Evidence for a Postperceptual Locus of Suppression During the Attentional Blink,” Journal of Experimental Psychology: Human Perception and Performance, vol. 24, No. 6, pp. 1656-1674, (1998), 19 pages.
  • Rizzolatti et al., “The Mirror-Neuron System,” Annu. Rev. Neurosci., vol. 27, pp. 169-192, (Mar. 5, 2004), 30 pages.
  • Voytek, et al., “Prefrontal cortex and basal ganglia contributions to visual working memory,” PNAS Early Edition, www.pnas.org/cgi/doi/10.1073/pnas.1007277107, (2010), 6 pages.
  • Voytek, et al., “Hemicraniectomy: A New Model for Human Electrophysiology with High Spatio-temporal Resolution,” Journal of Cognitive Neuroscience, vol. 22, No. 11, pp. 2491-2502, Massachusetts Institute of Technology, (Nov. 2009) 12 pages.
  • Wang, “Neurophysiological and Computational Principles of Cortical Rhythms in Cognition,” Physiol Rev 90: pp. 1195-1268, American Physiological Society, www.prv.org, (2010), 75 pages.
  • Woldorf, “Distortion of ERP averages due to overlap from temporally adjacent ERPs: Analysis and correction,” Psychophysiology, Society for Psychophysiological Research, Cambridge University Press (1993), 22 pages.
  • Woodman, et al., “Serial Deployment of Attention During Visual Search,” Journal of Experimental Psychology: Human Perception and Performance, vol. 29, No. 1, pp. 121-138, American Physiological Association (2003), 18 pages.
  • Filler, “MR Neurography and Diffusion Tensor Imaging: Origins, History & Clinical Impact of the first 50,000 Cases With an Assortment of Efficacy and Utility in a Prospective 5,000 Patent Study Group,” Institute for Nerve Medicine, (Nov. 7, 2008), 56 pages.
  • Yuval-Greenberg, et al., “Transient Induced Gamma-Bands Response in EEG as a Manifestation of Miniature Saccades,” Neuron, vol. 58, pp. 429-441, Elsevier Inc. (May 8, 2008), 13 pages.
  • Knight, et al., “Prefrontal cortex regulates inhibition and excitation in distributed neural networks,” Acta Psychologica vol. 101, pp. 159-178, Elsevier (1999), 20 pages.
  • Akam, et al., “Oscillations and Filtering Networks Support Flexible Routing of Information,” Neuron, vol. 67, pp. 308-320, Elsevier, (Jul. 29, 2010), 13 pages.
  • Gargiulo et al., “A Mobile EEG System With Dry Electrodes,” (Nov. 2008), 4 pages.
  • Badre, et al. “Frontal Cortex and the Discovery of Abstract Action Rules,” Neuron, vol. 66, pp. 315-326, Elsevier, (Apr. 29, 2010), 13 pages.
  • Buschman, et al., “Top-Down versus Bottom-Up Control of Attention in the Prefrontal and posterior Parietal Cortices,” Science, vol. 315, www.sciencemag.org/cgi/content/full/315/5820/1860, American Association for the Advancement of Science, (2007), 4 pages.
  • Buschman, et al., “Serial, Covert Shifts of Attention during Visual Search Are Reflected by the Frontal Eye Fields and Correlated with Population Oscillations,” Neuron, vol. 63, pp. 386-396, Elsevier, (Aug. 13, 2009), 11 pages.
  • Cheng, et al. “Gender Differences I the Mu Rhythm of the Human Mirror-Neuron System,” PLos ONE, vol. 3, Issue 5, www.plosone.org, (May 2008), 7 pages.
  • Fogelson, et al., “Prefrontal cortex is critical for contextual processing: evidence from brain lesions,” Brain: A Journal of Neurology, vol. 132, pp. 3002-3010, doi:10.1093/brain/awp230, (Aug. 27, 2009), 9 pages.
  • Fuster, “Cortex and Memory: Emergence of a New Paradigm,” Journal of Cognitive Neuroscience, vol. 21, No. 11, pp. 2047-2072, Massachusetts Institute of Technology, (Nov. 2009), 26 pages.
  • D'Esposito, “From cognitive to neural models of working memory,” Phil. Trans. R. Soc. B, doi: 10.1098/rstb.2007.2086, (Mar. 30, 2007), 12 pages.
  • Dien, et al., “Application of Repeated Measures ANOVA to High-Dens Dataset: A Review and Tutorial,” in Event-Related Potentials: A Methods Handbook pp. 57-82, (Todd C. Handy, ed., 2005), 14 pages.
  • Neurofocus—Neuroscientific Analysis for Audience Engagement, accessed on Jan. 8, 2010 at http://www.archive.org/web/20080621114525/www.neurofocus.com—/BrandImage.htm, (2008), 2 pages.
  • Ambler, “Salience and Choice: Neural Correlates of Shopping Decisions,” Psychology & Marketing, vol. 21, No. 4, p. 247-261, Wiley Periodicals, Inc., doi: 10.1002/mar20004, (Apr. 2004), 16 pages.
  • Hazlett, et al., “Emotional Response to Television Commercials: Facial EMG vs. Self-Report,” Journal of Advertising Research, (Apr. 1999), 17 pages.
  • Makeig, et al., “Dynamic Brain Sources of Visual Evoked Responses,” Science, vol. 295, www.sciencemag.org, (Jan. 25, 2002), 5 pages.
  • William, “Brain Signals to Control Movement of Computer Cursor,” Blog article: Brain Signals to Control Movement of Computer Cursor, Artificial Intelligence, retrieved from the Internet on Aug. 17, 2011, http://whatisartificialintelligence.com/899/brain-signals-to-control-movement-of-computer-cursor/, (Feb. 17, 2010), 3 pages.
  • Lewis et al., “Market Researchers make Increasing use of Brain Imaging,” ACNR, vol. 5, No. 3, pp. 36-37, (Jul./Aug. 2005), 2 pages.
  • Sutherland, “Neuromarketing: What's it all about?” Retrieved from Max Sutherland's Weblog on Aug. 23, 2011, http://www.sutherlandsurvey.com/Column—pages/Neuromarketing—whats—it—all—about.htm, (Mar. 2007), 5 pages.
  • Haq, “This Is Your Brain on Advertising,” BusinessWeek, Market Research, (Oct. 8, 2007), 3 pages.
  • EEG Protocols, “Protocols for EEG Recording,” retrieved from the Internet on Aug. 23, 2011, http://www.q-metrx.com/EEGrecordingProtocols.pdf, (Nov. 13, 2007), 3 pages.
  • Aaker et al., “Warmth in Advertising: Measurement, Impact, and Sequence Effects,” Journal of Consumer Research, vol. 12, No. 4, pp. 365-381, (Mar. 1986), 17 pages.
  • Belch et al., “Psychophysiological and cognitive Response to Sex in Advertising,” Advances in Consumer Research, vol. 9, pp. 424-427, (1982), 6 pages.
  • Ruchkin et al., “Modality-specific processing streams in verbal working memory: evidence from spatio-temporal patterns of brain activity,” Cognitive Brain Research, vol. 6, pp. 95-113, Elsevier, (1997), 19 pages.
  • Page et al., “Cognitive Neuroscience, Marketing and Research,” Congress 2006—Foresight—The Predictive Power of Research Conference Papers, ESOMAR Publications, (Sep. 17, 2006), 25 pages.
  • Braeutigam, “Neuroeconomics—From neural systems to economic behavior,” Brain Research Bulletin, vol. 67, pp. 355-360, Elsevier, (2005), 6 pages.
  • Lee et al., “What is ‘neuromarketing’? A discussion and agenda for future research,” International Journal of Psychophysiology, vol. 63, pp. 199-204, Elsevier (2006), 6 pages.
  • Crawford et al., “Self-generated happy and sad emotions in low and highly hypnotizable persons during waking and hypnosis: laterality and regional EEG activity differences,” International Journal of Psychophysiology, vol. 24, pp. 239-266, (Dec. 1996), 28 pages.
  • Desmet, “Measuring Emotion: Development and Application of an Instrument to Measure Emotional Responses to Products,” to be published in Funology: From Usability to Enjoyment, pp. 111-123, Kluwer Academic Publishers, (Blythe et al., eds., 2004), 13 pages.
  • Bagozzi et al., “The Role of Emotions in Marketing,” Journal of the Academy of Marketing Science, vol. 27, No. 2, pp. 184-206, Academy of Marketing Science (1999), 23 pages.
  • Blakeslee, “If You Have a ‘Buy Button’ in Your Brain, What Pushes It?” The New York Times, www.nytimes.com, (Oct. 19, 2004), 3 pages.
  • Kay et al., “Identifying natural images from human brain activity,” Nature, vol. 452, pp. 352-356, Nature Publishing Group, (Mar. 20, 2008), 5 pages.
  • Anonymous, “Functional magnetic resonance imaging,” retrieved online from Wikipedia, the Free Encyclopedia on Aug. 23, 2011, at http://en.wikipedia.org/w/index.php?title=Functional—magnetic—resonance—imaging&oldid=319601772, (Oct. 13, 2009), 8 pages.
  • Osborne, “Embedded Watermarking for image Verification in Telemedicine,” Thesis submitted for the degree of Doctor of Philosophy, Electrical and Electronic Engineering, University of Adelaide (2005), 219 pages.
  • Nielsen, “Neuroinformatics in Functional Neuroimaging,” Informatics and Mathematical Modeling, Technical University of Denmark, (Aug. 30, 2002), 241 pages.
  • Arousal in Sport, in Encyclopedia of Applied Psychology, vol. 1, p. 159, retrieved from Google Books, (Spielberger, ed., Elsevier Academic Press, 2004), 1 page.
  • Ziegenfuss, “Neuromarketing: Advertising Ethical & Medical Technology,” The Brownstone Journal, vol. XII, Boston University, pp. 69-73, (May 2005), 5 pages.
  • Zyga, “A Baseball Cap That Can Read Your Mind,” PhysOrg.com, located at www.physorg.com/news130152277.html, (May 16, 2008), 11 pages.
  • Lekakos, “Personalized Advertising Services Through Hybrid Recommendation Methods: The Case of Digital Interactive Television,” Department of Informatics, Cyprus University, (2004), 11 pages.
  • Yap et al., “TIMER: Tensor Image Morphing for Elastic Registration,” NeuroImage, vol. 47, (May 3, 2009), 15 pages.
  • Clifford, “Billboards That Look Back,” The New York Times, NYTimes.com, available at http://www.nytimes.com/2008/05/31/business/media/31billboard.html, (May 31, 2008), 4 pages.
  • Ambler et al., “Ads on the Brain; A Neuro-Imaging Comparison of Cognitive and Affective Advertising Stimuli,” London Business School, Centre for Marketing Working Paper, No. 00-902, (Mar. 2000), 23 pages.
  • U.S. Appl. No. 13/045,457, filed Mar. 10, 2011, (unpublished).
  • U.S. Appl. No. 12/778,810, filed May 12, 2010, (unpublished).
  • U.S. Appl. No. 12/778,828, filed May 12, 2010, (unpublished).
  • U.S. Appl. No. 13/104,821, filed May 10, 2011, (unpublished).
  • U.S. Appl. No. 13/104,840, filed May 10, 2011, (unpublished).
  • U.S. Appl. No. 12/853,197, filed Aug. 9, 2010, (unpublished).
  • U.S. Appl. No. 12/884,034, filed Sep. 16, 2010, (unpublished).
  • U.S. Appl. No. 12/913,102, filed Oct. 27, 2010, (unpublished).
  • U.S. Appl. No. 12/853,213, filed Aug. 9, 2010, (unpublished).
  • U.S. Appl. No. 13/105,774, filed May 11, 2011, (unpublished).
  • Lui, Tsz-Wai, et al., “Marketing Strategies in Virtual Worlds,” The Data Base for Advances in Information Systems, vol. 38, No. 4, Nov. 2007, pp. 77-80.
  • Barcelo, Francisco, et al., “Prefrontal Modulation of Visual Processing in Humans,” Nature Neuroscience, vol. 3, No. 4, Apr. 2000, pp. 399-403.
  • Canolty, R.T., et al., “High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex,” Science, vol. 313, Sep. 15, 2006, pp. 1626-1628.
  • Engel, Andreas, et al., “Dynamic Predictions: Oscillations and Synchrony in Top-Down Processing,” Macmillan Magazines Ltd, vol. 2, Oct. 2001, pp. 704-716.
  • Fries, Pascal, “A Mechanism for Cognitive Dynamics: Neuronal Communication Through Neuronal Coherence,” TRENDS in Cognitive Sciences, vol. 9, No. 10, Oct. 2005, p. 474-480.
  • Gazzaley, Adam, et al., “Top-down Enhancement and Suppression of the Magnitude and Speed of Neural Activity,” Journal of Cognitive Neuroscience, vol. 17, No. 3, pp. 507-517.
  • Hartikainen, Kaisa, et al., “Emotionally Arousing Stimuli Compete with Attention to Left Hemispace,” Editorial Manager(tm) for NeuroReport, Manuscipt Draft, Manuscript No. NR-D-07-5935R1, submitted Sep. 8, 2007, 26 pages.
  • Knight, Robert T., “Contribution of Human Hippocampal Region to Novelty Detection,” Nature, vol. 383, Sep. 19, 1996, p. 256-259.
  • Knight Robert T., “Decreased Response to Novel Stimuli After Prefrontal Lesions in Man,” Electroencephalography and Clinical Neurophysiology, vol. 59, 1984, pp. 9-20.
  • Miltner, Wolfgang H.R., et al., “Coherence of Gamma-band EEG Activity as a Basis for Associative Learning,” Nature, vol. 397, Feb. 4, 1999, pp. 434-436.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/182,851, on Mar. 14, 2012, 15 pages.
  • Final Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/608,685, on Mar. 29, 2012, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/846,242, on Mar. 29, 2012, 15 pages.
  • Final Office Action, issued by the United States Patent and Tracemark Office in connection with U.S. Appl. No. 12/234,388, on Apr. 6, 2012, 6 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,315, on Apr. 9, 2012, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/544,958 on May 2, 2012, 15 pages.
  • English Translation of Office Action, issued by the Israel Patent Office in connection with Patent Application No. 203176, on Feb. 21, 2012, 2 pages.
  • English Translation of Office Action, issued by the Israel Patent Office in connection with Patent Application No. 203177, on Mar. 1, 2012, 2 pages.
  • Second Office Action, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 200880101500.7, on Apr. 5, 2012, 5 pages.
  • Bimler et al., “Categorical perception of facial expressions of emotion: Evidence from multidimensional scaling,” Cognition and Emotion, vol. 15(5), pp. 633-658 (Sep. 2001), 26 pages.
  • de Gelder et al., “Categorical Perception of Facial Expressions: Categories and their Internal Structure,” Cognition and Emotion, vol. 11(1), pp. 1-23 (1997), 23 pages.
  • Griss et al., “Characterization of micromachined spiked biopotential electrodes”, Biomedical Engineering, IEEE Transactions (Jun. 2002), 8 pages.
  • Newell et al., “Categorical perception of familiar objects,” Cognition, vol. 85, Issue 2, pp. 113-143 (Sep. 2002), 31 pages.
  • Padgett et al., “Categorical Perception in Facial Emotion Classification,” In Proceedings of the 18th Annual Conference of the Cognitive Science Society, pp. 249-253 (1996), 5 pages.
  • “User Monitoring,” Sapien Systems, available at http://www.sapiensystems.com/eyetracking.html, accessed on Aug. 18, 2003, 1 page.
  • Sullivan et al., “A Brain-Machine Interface using Dry-Contact, Low-Noise EEG Sensors,” IEEE International Symposium on Circuits and Systems, 2008, 4 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,315, on Oct. 26, 2011, 41 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,240, on Oct. 27, 2011, 39 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,221, on Nov. 28, 2011, 44 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/608,660, on Dec. 7, 2011, 8 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/113,863, on Dec. 22, 2011, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,262, on Dec. 22, 2011, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,074, on Dec. 22, 2011, 16 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,557, on Dec. 22, 2011, 17 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,596, on Dec. 22, 2011, 15 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/200,813, on Dec. 22, 2011, 18 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/199,583, on Dec. 29, 2011, 18 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/410,372, on Jan. 3, 2012, 10 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/413,297, on Jan. 4, 2012, 10 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/544,921, on Jan. 9, 2012, 13 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,302, on Jan. 17, 2012, 11 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,225, on Jan. 20, 2012, 12 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,066, on Jan. 24, 2012, 12 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/546,586, on Feb. 1, 2012, 17 pages.
  • Restriction Requirement, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/544,958, on Feb. 10, 2012, 6 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/135,069, on Feb. 14, 2012, 35 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/357,322, on Feb. 14, 2012, 14 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,211, on Feb. 16, 2012, 15 pages.
  • Final Rejection, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/056,190, on Feb. 17, 2012, 22 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/122,253, on Feb. 17, 2012, 20 pages.
  • Office Action, issued by the United States Patent and Trademark Office in connection with U.S. Appl. No. 12/182,874, on Feb. 17, 2012, 15 pages.
  • Extended European Search Report, issued by the European Patent Office in connection with European Application No. 11006934.1-2221, on Oct. 25, 2011, 5 pages.
  • First Office Action, issued by the State Intellectual Property Office of P.R. China in connection with Patent Application No. 200880017883.X, on Nov. 30, 2011, 16 pages.
  • Meriam-Webster Online Dictionary definition for “tangible,” available at http://www.meriam-webster.com/dictionary/tangible, 1 page.
  • Mosby's Dictionary of Medicine, Nursing, & Health Professions, 2009, Mosby, Inc., Definition of Alpha Wave, 1 page.
  • Mosby's Dictionary of Medicine, Nursing, & Health Professions, 2009, Mosby, Inc., Definition of Beta Wave, 1 page.
  • U.S. Appl. No. 13/249,512, filed Sep. 30, 2011, (unpublished).
  • U.S. Appl. No. 13/249,525, filed Sep. 30, 2011, (unpublished).
  • U.S. Appl. No. 13/288,504, filed Nov. 3, 2011, (unpublished).
  • U.S. Appl. No. 13/288,571, filed Nov. 3, 2011, (unpublished).
  • U.S. Appl. No. 12/304,234, filed Nov. 3, 2011, (unpublished).
Patent History
Patent number: 8275660
Type: Grant
Filed: Aug 25, 2010
Date of Patent: Sep 25, 2012
Patent Publication Number: 20120054018
Assignee: The Nielsen Company (US), LLC (Schaumburg, IL)
Inventors: Anantha Pradeep (Berkeley, CA), Robert T. Knight (Berkeley, CA), Ramachandran Gurumoorthy (Berkeley, CA), Dev Ratnakar (Berkeley, CA)
Primary Examiner: Donald L. Champagne
Attorney: Hanley, Flight & Zimmerman, LLC
Application Number: 12/868,531
Classifications
Current U.S. Class: Determination Of Advertisement Effectiveness (705/14.41)
International Classification: G06Q 30/00 (20120101);