ADVERTISEMENT EXCHANGE USING NEURO-RESPONSE DATA
Example methods, systems and machine readable instructions are disclosed for assessing advertising effectiveness based on neurological data. An example method includes determining advertisement slot characteristics for a plurality of advertisement slots. The example advertisement slot characteristics include subject resonance measured by determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject, determining a second event related potential from the neuro-response data and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement. The example method also includes matching the plurality of advertisement slots with a plurality of advertisements based on the advertisement slot characteristics.
This patent arises from a continuation of U.S. patent application Ser. No. 12/622,292, entitled “Advertisement Exchange Using Neuro-Response Data,” which was filed on Nov. 19, 2009, and a continuation of U.S. patent application Ser. No. 12/622,312, entitled “Multimedia Advertisement Exchange,” which was filed on Nov. 19, 2009, both of which are incorporated herein by reference in their entireties.
FIELD OF THE DISCLOSUREThe present disclosure relates to an advertisement exchange using neuro-response data.
BACKGROUNDConventional systems for selection, purchase, exchange, and placement of advertisements such as commercials, print banner advertisements, radio commercials, etc., are limited or non-existent. Some conventional systems allow selection of slots based on perceived value. Analysis conducted to place advertising may involve evaluation of demographic information and statistical data. However, conventional systems are subject to semantic, syntactic, metaphorical, cultural, and interpretive errors.
Consequently, it is desirable to provide improved methods and apparatus for selection, purchase, exchange, and placement of advertising using neuro-response data.
The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular example embodiments.
Reference will now be made in detail to some specific examples of the disclosure including the best modes contemplated by the inventors for carrying out the systems and methods of the disclosure. Examples of these specific embodiments are illustrated in the accompanying drawings. While the systems and methods of the disclosure are described in conjunction with these specific embodiments, it will be understood that it is not intended to limit the disclosure 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 disclosure as defined by the appended claims.
For example, the techniques and mechanisms of the present disclosure will be described in the context of an advertisement exchange using neuro-response data. However, it should be noted that some of the techniques and mechanisms for implementing an advertisement exchange may be applied using other data such as survey data and demographic data even without the use of neuro-response data. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. Particular example embodiments of the present disclosure 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 disclosure.
Various techniques and mechanisms of the present disclosure 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 disclosure unless otherwise noted. Furthermore, the techniques and mechanisms of the present disclosure 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
An advertisement exchange determines characteristics associated with advertisement slots such as slots in a commercial pod, locations on a printed page, banners in a video, billboards, etc. Characteristics may include demographic information, advertisement type, and neuro-response characteristics such as priming, attention, engagement, and retention. Advertisement slots are matched with advertisements and may be selected, purchased, exchanged, and analyzed by advertisers, individuals, corporations, and firms. In some examples, bids and offers are made for advertisement slots based on advertisement slot characteristics. Advertisement slot characteristics may be changed in real-time as placement of advertisements in surrounding slots changes the characteristics of a particular slot.
Example EmbodimentsConventional mechanisms for managing advertisement slots including selecting, purchasing, exchanging, analyzing, and selling advertisement slots are limited. One problem with conventional mechanisms for managing advertisement slots and advertising is that they do not allow efficient selection and purchase of advertisement slots based on characteristics of various advertisement slots. For example, an insurance company may be able to select and buy advertisement slots based on programming demographic, but the insurance company does not have efficient access to information such as priming and retention characteristics for insurance company advertisements at particular advertisement slots. The insurance company also may not fully appreciate the advertisement slot characteristics of different media such as print, video, audio, banner, etc. Conventional mechanisms may allow for experience based selection of advertisement slots for various products, services, and offerings that are attributable to the stimulus. However, they are also prone to semantic, syntactic, metaphorical, cultural, and interpretive errors thereby preventing the accurate and repeatable targeting of audiences.
Conventional systems do not use neuro-behavioral and neuro-physiological response blended manifestations in assessing the user response and do not elicit an individual customized neuro-physiological and/or neuro-behavioral response to the stimulus. Conventional systems also fail to blend multiple datasets, and blended manifestations of multi-modal responses, across multiple datasets, individuals and modalities, to reveal and validate the elicited measures of resonance and priming to allow for intelligent selection of personalized content.
In these respects, the advertisement exchange according to the present disclosure substantially departs from the conventional concepts and designs of the prior art. According to various embodiments, it is recognized that a subject commercial or advertisement for particular products, services, and offerings may be particularly effective when a user is primed for the particular products, services, and offerings by other commercials and advertisements in close proximity to the subject commercial or advertisements. For example, an advertisement for cleaning supplies may be particularly effective for viewers who have viewed an advertisement on antibacterial soap, or an advertisement for a sports car may be particularly effective for viewers who have recently viewed a commercial for an auto racing program in the same commercial pod. In still other examples, an audio advertisement for packaged salads may be more effective after listening to an audio advertisement for a weight loss program in the same audio advertisement cluster.
It is also recognized that user attention, engagement, and retention levels at various points in a program or at various slots in a commercial pod may vary. The techniques and mechanisms of the present disclosure allow advertisement slot purchasers to account for these differences during advertisement slot purchases. For example, it may be determined that the first slot during the first commercial break of a particular program has the highest attention and engagement levels but medium retention levels based on neuro-response data. Advertisement slot purchasers may bid for the particular slot based on increased information about advertisement slot characteristics and typical subject neuro-response levels during the particular slot. Different advertisement slots in a commercial pod for the same program may vary widely in effectiveness levels, yet many advertisement slots are sold based on flat rate or tiered type pricing structures.
The techniques and mechanisms of the present disclosure provide more individualized and/or auction based pricing for particular slots. In particular embodiments, purchasers are provided with cost per retention gain or cost per engagement level scores for various slots in different media.
Consequently, the techniques and mechanisms of the present disclosure determine characteristics of advertisement slots. In particular embodiments, characteristics of advertisements are also determined. Neuro-response characteristics such as priming, attention, engagement, and retention along with demographic data can be used to match advertisement slots with advertisement purchasers and advertisements. In some examples, advertisement slots and advertisements are labeled and tagged to allow for improved selection and arrangement.
Advertisers can assess the value of particular slots such as a slot in a commercial pod, a line of text on an advertisement page, a location on a store shelf, etc., based on characteristics of the slots and ability to access to preferred audiences.
According to various embodiments, the techniques and mechanisms of the present disclosure may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve advertisement slot management. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). 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 Functional Magnetic Resonance Imaging (FMRI), Electrocardiograms (EKG), pupillary dilation, etc. Effector measurement mechanisms include Electrooculography (EOG), eye tracking, facial emotion encoding, reaction time etc.
According to various embodiments, the techniques and mechanisms of the present disclosure intelligently blend multiple modes and manifestations of precognitive neural signatures with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately perform advertisement slot management. 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 advertisement slot management and an advertisement exchange.
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 disclosure 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 disclosure 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, FMRI, 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 to effectively characterize advertisement slots for an advertisement exchange.
According to various embodiments, an advertisement slot neuro-response database 128 is also associated with the advertisement exchange 112. The advertisement slot neuro-response database 128 may be integrated with the advertisement slot characteristics database 126 or maintained separately. The advertisement slot neuro-response database 128 includes characteristics such as attention, priming, retention, and engagement levels for a particular advertisement slot. For example, priming levels for cleanser commercials during a documentary about infections may be high. Retention levels for an advertisement slot during a particular action sequence may be high. Neuro-response metrics are determined for various advertisement slots in commercial pods, printed banners, billboards, etc. using neuro-response data obtained from subjects exposed to stimulus material. The neuro-response database 128 provides advertisement slot purchasers with additional insight useful in assessing the value of particular advertisement slots.
In particular embodiments, an advertisement characteristics database 106 is also associated with an advertisement exchange 112. The advertisement characteristics database 106 may be preloaded with advertisements that advertisers 102 and corporations/firms 104 provide to the advertisement exchange 112. The advertisement exchange may place advertisements in suitable slots based on characteristics of the advertisement. According to various embodiments, the advertisement characteristics database 106 may indicate that a particular commercial could best be placed in a slot with a high priming metric for food. Advertisers may provide advertisements to an advertisement exchange 112 to automatically place the advertisements in advertisement slots that meet advertising criteria such as target audience exposure levels and retention metrics in a cost effective manner. In other examples, advertisers 102, corporations and firms 104 may bid for particular advertisement slots or particular types of advertisements slots. In particular embodiments, commercials that reach a certain number of people in a particular demographic while having specified priming and retention levels are provided for auction. Neuro-response characteristics such as priming, attention, engagement, and retention levels for particular categories, demographics, and types can be obtained using a neuro-response data collection system.
It should be noted that although the advertisement exchange may use neuro-response data to enhance advertisement as well as advertisement slot evaluation, some advertisement exchanges can be implemented without the use of neuro-response data.
According to various embodiments, stimulus presentation device 101 could include devices such as televisions, cable consoles, computers and monitors, projection systems, display devices, speakers, tactile surfaces, etc., for presenting the stimuli including but not limited to advertising and entertainment from different networks, local networks, cable channels, syndicated sources, websites, internet content aggregators, portals, service providers, etc.
According to various embodiments, the subjects 103 are connected to data collection devices 105. The data collection devices 105 may include a variety of neuro-response measurement mechanisms including neurological and neurophysiological measurements systems such as EEG, EOG, FMRI, EKG, 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 105 include EEG 111, EOG 113, and FMRI 115. In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision.
The data collection device 105 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (FMRI, 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 advertisement exchange includes EEG 111 measurements made using scalp level electrodes, EOG 113 measurements made using shielded electrodes to track eye data, FMRI 115 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 stimulus presentation device 101. In particular embodiments, the data collection devices 105 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 105 may be synchronized with a set-top box to monitor channel changes. In other examples, data collection devices 105 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 105 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 advertisement exchange also includes a data cleanser device 121. In particular embodiments, the data cleanser device 121 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 121 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 121 is shown located after a data collection device 105 and before content characteristics integration 133, the data cleanser device 121 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, an optional survey and interview system collects and integrates user survey and interview responses to combine with neuro-response data to more effectively select content for delivery. 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. The survey and interview system can also be used to obtain user responses about particular pieces of stimulus material.
According to various embodiments, the priming repository system 131 associates meta-tags with various temporal and spatial locations in program content and provides these meta-tags to an advertisement characteristics database associated with an advertisement exchange. In some examples, commercial or advertisement breaks are provided with a set of meta-tags that identify commercial or advertising content that would be most suitable for a particular advertisement slot. The slot may be a particular position in a commercial pod or a particular location on a page.
Each slot may identify categories of products and services that are primed at a particular point in a cluster. The content may also specify the level of priming associated with each category of product or service. For example, a first commercial may show an old house and buildings. Meta-tags may be manually or automatically generated to indicate that commercials for home improvement products would be suitable for a particular advertisement slot or slots following the first commercial.
In some instances, meta-tags may include spatial and temporal information indicating where and when particular advertisements should be placed. For example, a page that includes advertisements about pet adoptions may indicate that a banner advertisement for pet care related products may be suitable. The advertisements may be separate from a program or integrated into a program. According to various embodiments, the priming repository system 131 also identifies scenes eliciting significant audience resonance to particular products and services as well as the level and intensity of resonance. The information in the priming repository system 131 may be manually or automatically generated and may be associated with other characteristics such as retention, attention, and engagement characteristics. In some examples, the priming repository system 131 has data generated by determining resonance characteristics for temporal and spatial locations in various programs, games, commercial pods, pages, etc.
The information from a priming, attention, engagement, and retention repository system 131 may be combined along with type, demographic, time, and modality information using a content characteristics integration system 133. According to various embodiments, the content characteristics integration system weighs and combines components of priming, attention, engagement, retention, personalization, demographics, etc. to allow selection, purchase, and placement of advertising in effective advertisement slots. The material may be marketing, entertainment, informational, etc.
In particular embodiments, neuro-response preferences are blended with conscious, indicated, and/or inferred user preferences to select neurologically effective advertising for presentation to the user. In one particular example, neuro-response data may indicate that beverage advertisements would be suitable for a particular advertisement break. User preferences may indicate that a particular viewer prefers diet sodas. An advertisement for a low calorie beverage may be selected and provided to the particular user. According to various embodiments, a set of weights and functions use a combination of rule based and fuzzy logic based decision making to determine the areas of maximal overlap between the priming repository system and the personalization repository system. Clustering analysis may be performed to determine clustering of priming based preferences and personalization based preferences along a common normalized dimension, such as a subset or group of individuals. In particular embodiments, a set of weights and algorithms are used to map preferences in the personalization repository to identified maxima for priming.
According to various embodiments, the advertisement exchange includes a data analyzer associated with the data cleanser 121. The data analyzer uses a variety of mechanisms to analyze underlying data in the system to determine resonance. According to various embodiments, the data analyzer 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 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 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 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 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 sources including survey based data 137 may be blended and passed to an advertisement exchange 135. In some examples, survey based data 137 and demographic data may be used without neuro-response data. According to various embodiments, the advertisement exchange 135 manages advertisements such as commercials and print banners and identifies slots having characteristics appropriate for the advertisements. Appropriateness may be based on advertisement type, neuro-response characteristics of advertisements, neuro-response characteristics of advertisement slots, demographic information, etc. Advertisement slots in a commercial pod may be offered to a variety of advertisers, companies, firms, and individuals. In some examples, advertisement slots may be auctioned using variety of bid mechanisms. Characteristics of a slot in a particular commercial pod may be modified as other slots in the pod are sold on a real-time basis. It is recognized that the programming as well as other advertisements surrounding an advertisement slot affect priming, attention, engagement, and retention characteristics of the advertisement slot.
Commercials in a pod may be ordered in a particular manner to optimize effectiveness. Advertisements on a page may be rearranged to improve viewer response. According to various embodiments, the advertisement exchange 135 receives bids, selects, and assembles in a real time, a near real time, or a time delayed manner advertisements for placement in advertisement slots by associating neuro-response characteristics of slots with characteristics of advertisements.
According to various embodiments, another stimulus attributes data model 221 includes creation attributes 223, ownership attributes 225, broadcast attributes 227, and statistical, demographic and/or survey based identifiers 229 for automatically integrating the neuro-physiological and neuro-behavioral response with other attributes and meta-information associated with the stimulus.
According to various embodiments, a stimulus priming data model 231 includes fields for identifying advertisement breaks 233 and scenes 235 that can be associated with various priming levels 237 and audience resonance measurements 239. In particular embodiments, the data model 231 provides temporal and spatial information for ads, scenes, events, locations, etc. that may be associated with priming levels and audience resonance measurements. In some examples, priming levels for a variety of products, services, offerings, etc. are correlated with temporal and spatial information in source material such as a movie, billboard, advertisement, commercial, store shelf, etc. In some examples, the data model associates with each second of a show a set of meta-tags for pre-break content indicating categories of products and services that are primed. The level of priming associated with each category of product or service at various insertions points may also be provided. Audience resonance measurements and maximal audience resonance measurements for various scenes and advertisement breaks may be maintained and correlated with sets of products, services, offerings, etc.
The priming and resonance information may be used to select advertisements suited for particular levels of priming and resonance corresponding to identified advertisement slots.
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 subject preferences 323 such as 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 product and service attributes and preferences may also be included. 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.
According to various embodiments, data models for neuro-feedback association 325 identify experimental protocols 327, modalities included 329 such as EEG, EOG, FMRI, surveys conducted, and experiment design parameters 333 such as segments and segment attributes. Other fields may include experiment presentation scripts, segment length, segment details like stimulus material used, inter-subject variations, intra-subject variations, instructions, presentation order, survey questions used, etc. 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 such as station and location identifiers, the data and time of recording, and operator details. 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.
Other queries may retrieve stimulus material 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. Experimental design based queries 425 may obtain data from a neuro-informatics repository based on experiment protocols 427, product category 429, surveys included 431, and stimulus provided 433. Other fields that may be used include the number of protocol repetitions used, combination of protocols used, and usage configuration of surveys.
Client and industry based queries may obtain data based on the types of industries included in testing, specific categories tested, client companies involved, and brands being tested. 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.
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.
According to various embodiments, 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 response synthesis. In other examples, intra-modality response synthesis may be performed without cross-modality response synthesis.
A variety of mechanisms can be used to perform data analysis. 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 disclosure 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 magnetoencephalography) can be used in inverse model-based enhancement of the frequency responses to the stimuli.
Various embodiments of the present disclosure 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, FMRI, 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 disclosure 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 FMRI 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 disclosure 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.
According to various embodiments, post-stimulus versus pre-stimulus differential measurements of ERP time domain components in multiple regions of the brain (DERP) are measured at 607. The differential measures give a mechanism for eliciting responses attributable to the stimulus. For example the messaging response attributable to an advertisement or the brand response attributable to multiple brands is determined using pre-resonance and post-resonance estimates
At 609, target versus distracter stimulus differential responses are determined for different regions of the brain (DERP). At 611, event related time-frequency analysis of the differential response (DERPSPs) are used to assess the attention, emotion and memory retention measures across multiple frequency bands. According to various embodiments, the multiple frequency bands include theta, alpha, beta, gamma and high gamma or kappa. At 613, priming levels and resonance for various products, services, and offerings are determined at different locations in the stimulus material. In some examples, priming levels and resonance are manually determined. In other examples, priming levels and resonance are automatically determined using neuro-response measurements. According to various embodiments, video streams are modified with different inserted advertisements for various products and services to determine the effectiveness of the inserted advertisements based on priming levels and resonance of the source material.
At 617, multiple trials are performed to enhance priming and resonance measures. In particular embodiments, the priming and resonance measures are sent to a priming repository 619. The priming repository 619 may be used to automatically select and place advertising suited for particular slots in a cluster. Advertisements may be automatically selected and arranged in advertisement slots to increase effectiveness.
Any temporal or spatial location suitable for advertising may be identified at 703. According to various embodiments, neuro-response data is obtained for the various advertisement slots at 705. Obtaining neuro-response data may involve evaluating stimulus material to determine attention, engagement, retention, and priming characteristics for various temporal and spatial locations in the stimulus material. The attention, engagement, retention, and priming characteristics may be evaluated for various types of products, services, offers, goods, etc. In some examples, only priming characteristics are determined for various advertisement slots. In other examples, a variety of neuro-response characteristics including mirroring characteristics, propensity to act, propensity to reach, emotional levels, etc., may be determined.
According to various embodiments, advertisement slot characteristics are associated with advertisement slots at 707. Advertisement slot characteristics may include not only neuro-response characteristics but may also include demographic information, media type, duration, length, cost, audience exposure, etc. At 709, advertisement slots are provided to advertisers and advertisement slot offers are received. In some examples, advertisement slot offers are received based on an auction, reverse auction, Dutch auction, or anonymous bid process. In other examples, advertisement slots are sold on a tiered scale based on advertisement characteristics and perceived desirability to advertisers.
At 711, advertisements are matched with advertisement slots. At 713, advertisement slot characteristics may be updated for surrounding slots based on sold advertisements. It is recognized that an advertisement may affect priming, engagement, and other characteristics for other advertisement slots. According to various embodiments, advertisement slot characteristics are dynamically adjusted as advertisements are sold.
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.
According to particular example embodiments, a system 800 suitable for implementing particular embodiments of the present disclosure includes a processor 801, a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor 801 is responsible for tasks such as pattern generation. Various specially configured devices can also be used in place of a processor 801 or in addition to processor 801. The complete implementation can also be done in custom hardware. The interface 811 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.
According to particular example embodiments, the system 800 uses memory 803 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 disclosure 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 disclosure 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 disclosure 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:
- determining advertisement slot characteristics for a plurality of advertisement slots, the advertisement slot characteristics including subject resonance measured by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement; and
- matching the plurality of advertisement slots with a plurality of advertisements based on the advertisement slot characteristics.
2. The method of claim 1, wherein the neuro-response data is representative of one or more of retention, engagement, attention, or a priming level.
3. The method of claim 1, wherein the advertisement slots are commercials in a commercial pod.
4. A method, comprising:
- determining advertisement slot characteristics for a plurality of advertisement slots associated with media materials, the advertisement slot characteristics determined by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement;
- matching a first advertisement slot with a first advertisement based on at least one of the advertisement slot characteristics; and
- revising one or more of the advertisement slot characteristics for the plurality of remaining advertisement slots based on the first advertisement.
5. The method of claim 4, wherein the advertisement slot characteristics comprise priming characteristics.
6-20. (canceled)
21. A system, comprising:
- a neuro-response data evaluator to determine advertisement slot characteristics for a plurality of advertisement slots, the advertisement slot characteristics including subject resonance measured by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement; and
- a processor to match the plurality of advertisement slots with a plurality of advertisements based on the advertisement slot characteristics.
22. The system of claim 21, wherein the neuro-response data is representative of one or more of retention, engagement, attention, or a priming level.
23. The system of claim 21, wherein the advertisement slots are commercials in a commercial pod.
24. A system, comprising:
- a processor to: determine advertisement slot characteristics for a plurality of advertisement slots associated with media materials, the advertisement slot characteristics determined by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement; match a first advertisement slot with a first advertisement based on the advertisement slot characteristics; and revise one or more of the advertisement slot characteristics for the plurality of remaining advertisement slots based on the first advertisement.
25. The system of claim 22, wherein the advertisement slot characteristics comprise priming characteristics.
26-40. (canceled)
41. A tangible machine readable storage medium comprising instructions, which when executed, cause a machine to at least:
- determine advertisement slot characteristics for a plurality of advertisement slots, the advertisement slot characteristics including subject resonance measured by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement; and
- match the plurality of advertisement slots with a plurality of advertisements based on the advertisement slot characteristics.
42. The medium of claim 41, wherein the neuro-response data is representative of one or more of retention, engagement, attention, or a priming level.
43. The medium of claim 41, wherein the advertisement slots are commercials in a commercial pod.
44. A tangible machine readable storage medium comprising instructions, which when executed, cause a machine to at least:
- determine advertisement slot characteristics for a plurality of advertisement slots associated with media materials, the advertisement slot characteristics determined by: determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject; determining a second event related potential from the neuro-response data; and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement;
- match a first advertisement slot with a first advertisement based on the advertisement slot characteristics; and
- revise one or more of the advertisement slot characteristics for the plurality of remaining advertisement slots based on the first advertisement.
45. The medium of claim 44, wherein the advertisement slot characteristics comprise priming characteristics.
46-96. (canceled)
Type: Application
Filed: Oct 31, 2012
Publication Date: Mar 21, 2013
Inventors: Anantha Pradeep (Berkeley, CA), Robert T. Knight (Berkeley, CA), Ramachandran Gurumoorthy (Berkeley, CA)
Application Number: 13/665,354
International Classification: G06Q 30/02 (20120101);
