DIGITAL ADVERTISING SYSTEM

Abstract

A digital advertising system includes an advertisements module that provides a number of digital advertisements for display on a digital display device. A digital sign module including the digital display device displays digital advertisements provided by the advertisements module, and captures video analytics data relating to previous viewers of the digital advertisements displayed by the digital sign module. A data mining module retrieves the video analytics data from the digital sign module and generates trained advertising models based thereon using a data mining algorithm. A content management system module coupled to the advertisements module and the data mining module receives the digital advertisements and the trained advertising models and generates a subset of the advertisements for display based on the trained advertising models.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Indian Patent Application No. 2646/DEL/2011 filed on Sep. 13, 2011, entitled “Intelligent Advertising Framework System”, the disclosure of which is incorporated herein by reference in its entirety for all purposes.

FIELD OF THE INVENTION

Embodiments of the invention relate to a system for selecting advertising to be displayed on a digital display device based on video analytics.

BACKGROUND OF THE INVENTION

Digital signage is the term that is often used to describe the use of an electronic display device, such as a Liquid Crystal Display (LCD), Light Emitting Diode (LED) display, plasma display, or a projected display to show news, advertisements, local announcements, and other multimedia content in public venues such as restaurants or shopping malls. In recent years, the digital signage industry has experienced tremendous growth, and it is now only second to the Internet advertising industry in terms of annual revenue growth.

The deployment of digital signs is growing rapidly for good reason. They enable advertisers to show advertising (ad) content that is more engaging and dynamic and to easily change the content in real time based on new promotions, time of day, even weather and other events. Currently there is no way to determine the effectiveness of the ad content. One of the main challenges in the digital signage industry has been providing digital signage operators with some clear return on investment (ROI) data. What is needed is a way for ad content to be targeted and adapted to the specific demographics of the people viewing it.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the invention, which, however, should not be taken to limit the invention to the specific embodiments, but are for explanation and understanding only.

FIG. 1 illustrates in functional block form an embodiment of the invention.

FIG. 2 is a flow chart of an embodiment of the invention.

FIG. 3 illustrates aspects of an embodiment of the invention.

FIG. 4 provides a block diagram of a content management system in accordance with an embodiment of the invention.

FIG. 5 provides a block diagram of a digital sign module in accordance with an embodiment of the invention.

FIG. 6A illustrates an aspect of an embodiment of the invention relating to obtaining video analytic data relating to individual viewers within an observable area of a digital sign.

FIG. 6B illustrates an aspect of an embodiment of the invention relating to obtaining video analytic data relating to individual viewers within an observable area of a digital sign.

FIG. 7 is a flow chart of an embodiment of the invention relating to obtaining anonymous video analytic data relating to individual viewers within an observable area of a digital sign.

DETAILED DESCRIPTION OF THE INVENTION

Anonymous Video Analytics (AVA) is a passive and automated audience or viewer measurement technology designed for digital signage networks that can be used to provide digital signage operators with quantitative viewership information and return on investment (ROI) data. Embodiments of the present invention are based on the concept of using AVA data and data mining techniques to achieve targeted advertising, which can be used to measure and improve the advertising ROI of a digital sign. Additionally, by correlating AVA viewership information with point-of-sale (POS) data, embodiments can also be used to establish a link between the response time to an advertisement by a certain demographic group and the effect on the sale of the advertised product.

Embodiments of the present invention make use of anonymous video analytics (AVA) in displaying advertising on a digital display device. By equipping digital signs with a sensor, such as one or more front-facing cameras proximate the digital display device, and AVA software coupled with powerful processors, such as Intel Core 15 and Intel Core 17 processors, digital signs according to an embodiment of the invention have the intelligence to anonymously detect the number of viewers, their gender, their age bracket, even their distance from the advertising display screen and then adapt ad content based on that information. For example, if a viewer is a teenage girl, then an embodiment of the invention may change the content to highlight a back to school shoe promotion a few stores down from where the digital display screen is located. If the viewer is a senior male, then an embodiment may cause the digital display screen to display an advertisement about a golf club sale at a nearby sporting goods store.

According to an embodiment of the invention, ads can be better targeted, more relevant, and ultimately more effective. The embodiment makes this possible by analyzing perhaps millions of pixels per second of video content in real time to determine if people are viewing the digital sign, and if they are, determining their demographic characteristics. These intelligent digital signs can do even more, according to embodiments of the invention. For example, they can determine dwell time, that is, how long viewers are watching the ads. These capabilities enable network operators, content providers, advertising agencies, and service providers to better deliver relevant messages and measure network and ad effectiveness with greater precision. By correlating sales data with the ad shown and the audiences' demographics, advertisers can target ads directly to their audience and measure their effectiveness. Additionally, for campaign planning and return-on-investment (ROI) measurement, AVA data can be correlated with “proof-of-play” data, that is data relating to what, where, and when an ad was displayed on the digital sign, to determine content-specific viewership metrics by demographics and time of day.

Embodiments of the invention are based on the concept of targeted advertising in which future viewers or customers belonging to the same or similar demographic as previous viewers are targeted based on the viewing behavior or patterns of the previous viewers. By analyzing AVA or viewership data collected from previous viewers positioned in front of a digital display, embodiments can discover viewing patterns and use this information to train advertising models that can be deployed to the digital sign. These advertising models can then be used to choose specific advertisements from the inventory of available advertising content to intelligently target future viewers.

The advertising models are based on data mining principles and can be built using tools such as Microsoft's SQL Server Analysis System (MS SSAS). The advertising models are created using well-known data mining algorithms such as Naïve Bayes, Decision Trees, and Association Rules, along with large scale clustering, all of which are available in MS SSAS.

The playback of multimedia content on a digital sign is accomplished through a content management system (CMS). A description follows of the architecture of the digital advertising system in accordance with an embodiment of the invention, in which advertising models are deployed in real time on the digital sign through the CMS, even if the CMS is located “in the cloud”. The CMS can then be used to generate a customized advertising list based on at least two parameters, the trained advertising model, and advertising data. According to an embodiment of the invention, the advertising data is combined with the trained advertising model to enable real-time content triggering.

Embodiments of the invention analyze the type of viewer information, such as age, in particular, an age range or age bracket, and gender, as well as weather and time information to select the most appropriate advertisement to be played on the digital sign display device. Further references herein to “age” shall be understood to include an age range, category or bracket. Real time video analytics data is collected and analyzed to predict the type of viewers for a future time slot, for example, the next time slot. Depending on the prediction, appropriate ads are played on the display device. The CMS generates a default play list by using advertising information and advertiser preference. If viewership information is not available or the prediction is for some reason not made or not reasonably accurate or for some reason the accuracy of the prediction is considered suspect, then an offline (default) play list provided to CMS by the advertisers may be played on the display device.

FIG. 1 illustrates a functional block diagram of an embodiment of the invention. With reference also to the flow chart 200 in FIG. 2, the process starts at 205 with digital sign module 105 displaying advertisements, processing video analytic data at 210, that is, capturing video analytic data, also referred to herein as viewership data, and sending the viewership data to a permanent data store, such as a database, where the data is optionally cleaned or filtered before being accessed at 215 by the data mining module 110 to determine viewing patterns of any individuals located in front of the digital sign and capable of viewing the same.

Importantly, at least for the purpose of maintaining privacy, the video analytic data can be made or maintained as anonymous video analytic data, as will be described further below, but essentially, the viewership data is based on census (defined as systematically and regularly acquiring and recording information about members of a given population), not on sampling, and no images of viewers are captured, stored, or transmitted. The video analytic data capture functionality may be embodied in software executed by the digital sign module, and in one embodiment of the invention, captures real time video analytic data that may be used by module 110 to make real time predictions and schedule a digital advertisement for display, and/or may be used as historical data for generating rules (training advertising models) in the data mining module at 220.

In the data mining module, the advertising models are generated and trained (that is, refined) at 220 using the video analytic data based on well-known data mining algorithms such as the Naïve Bayes algorithm, the Decision Trees algorithm and the Association Rules algorithm used in determining viewing patterns at 215. In addition to using the video analytic data, the data mining module may also consider weather conditions corresponding at the time the video analytic data was captured. Weather conditions data, or simply, weather data 135, may be maintained in a permanent store that can be accessed by data mining module 110. In one embodiment, the same permanent store may be used to store the video analytic data captured by the digital sign module 105 as well. Further, data mining module 110 receives as input a list of digital advertisements 125 available for display on the digital sign, and metadata associated the list of advertisements, such as the demographic characteristics of viewers to which advertisers wish to target their advertisements. Digital sign module 105 also supplies to the data mining module “proof-of-play” data, that is, advertising data indicating what ads were displayed by the digital sign, when those ads where displayed, and where those ads were displayed (e.g., by providing a deviceID for the digital sign that can be used as a basis for determining the location of the digital sign). In one embodiment of the invention, sales data 130, for example, from a Point-of-Sale terminal, may be input to data mining module 110. The sales data may be correlated with the AVA data to gauge the effectiveness of an advertisement on a certain demographic group in terms of the sale of products or services featured in the advertisement.

The data mining module 110 generates at 220 trained advertising models which according to an embodiment of the invention are used to predict suitable advertising categories as well as future viewer types based on previous viewer types (“passer pattern types”). Once a trained advertising model 115 is generated it is transmitted by the data mining module and received and stored by the content management system (CMS) 120 where along with advertising data, a customized advertising list is generated and stored at 225. (Indeed, the CMS stores all trained advertising models, advertisement lists, advertiser preferences, and advertising data.) CMS 120 transmits the customized advertising list at 140 to digital sign 105 for display. In one embodiment of the invention, digital sign module 105 comprises a digital signage media player module (digital player module) 145, which may be used to generate the advertising lists in real time. Module 145 operates as a condensed repository for information stored in the CMS.

The CMS obtains trained advertising models from the data mining module. In one embodiment, multiple digital sign modules 105, or multiple digital signage media players 145, or multiple digital display devices are installed. The CMS therefore will segregate the advertising models by digital sign module, or digital player, etc., as the case may be. The CMS generates segregated customized ad lists based on the advertising models and obtained advertising data. The CMS also generates offline ad lists, that is, default ad lists, based on advertiser preferences obtained from advertisers 125. These segregated models, customized ad lists, and default ad lists are sent to each digital sign module or digital player at 230 for display on the digital sign.

While FIG. 1 illustrates modules 110 and 120 as separate functional blocks, it is appreciated that these modules may co-operate on a single computer system, or may be distributed across multiple computer systems. The computer system(s) may reside in a private communications network, or may be accessible over the Internet “in the cloud”. The digital sign functional block, including AVA software and the digital signage media player 145 is typically implemented in or connected to one or more servers coupled to one or more digital display devices located in an area where advertisers desire to display digital advertisements on a digital sign, such as a retail store or shopping mall. One or more sensors such as sensor 103, for example, an optical device such as a video camera, are coupled to the digital sign 105 to capture the video or images of viewers used by digital sign 105 to generate the AVA data.

Capturing Viewers

Proof-of-Impression (POI) is a metric by which an advertiser measures effectiveness of a digital advertisement. Essentially, POI indicates whether individuals near a digital sign viewed or glanced or did not glance at the digital sign. In one embodiment of the invention, to obtain this metric, a number of events occur: 1) a glance is taken at the digital sign by a viewer while in an area from which the digital advertisement can reasonably be observed, 2) the observer, which in one embodiment is a video camera, is in a position to observe a glance, even a short glance, from a viewer, and 3) once a viewer has glanced at the digital advertisement, additional glances need not be counted while the viewer remains in the observable area. Measuring POI also involves differentiating, and uniquely tracking different, individual viewers while they are within the observable area, and associating each uniquely tracked individual viewer with a state variable relating to looking at the digital advertisement. Thus, a digital sign measuring POI monitors a viewer at locations from which the viewer can glance at the digital sign.

Beyond this basic functionality discussed above for measuring POI, an embodiment of the invention may measure viewing time, or glance duration, relate a particular glance to a particular rendition or display of a digital advertisement on the digital sign, determine locations and time when the digital sign gets a number of glances, such as a minimum number or a maximum number of glances. Further, demographic information may also be gathered according to one embodiment, such as age, gender, ethnicity, weight, etc., allowing the embodiment to further refine the business intelligence derived from measuring POI. In another embodiment, this metric can be correlated with sales data for products or services offered for sale in the digital advertisements to assess the effectiveness of the advertisements.

Embodiments may range from a single digital sign with one video camera to multiple digital signs each with one or more cameras. In any case, the video camera is used to track a viewer using sampling rates of, for example, 5-30 frames per second, and the viewer is assigned a unique, anonymous identity, while the camera looks for glance activity. The unique anonymous identity allows recording of glance activity for each individual viewer while in the observing area in front of a digital sign.

The ability of an embodiment to capture and track glances for each individual in the viewing area is based on the quality of the optical capture device, whether a video camera or digital camera, the sophistication of back-end computer vision and video and/or image processing functionality, and on the distance the individual is located from the video camera. With reference to FIG. 6A, an environment 600 is depicted in which a digital display screen 610 capable of displaying a digital sign in accordance with an embodiment of the invention is located. A front-facing video camera 610 (shown in top down view) is located near the digital display screen, for example, mounted on top of the screen. An overall viewing area is illustrated by the perimeter defined by line 630. Individuals 640a and 640b pass through this viewing area in the direction of travel indicated by respective dashed lines 641a and 641b. The video camera and associated back-end processing is capable of capturing video or still images and detecting unique individuals within an observable area 625. Thus, individual 640a is capable of being observed and isolated by video camera 610, while individual 640b is not. For video camera 610 to actually detect a gaze or glance by individual 640a in the direction of digital display screen 605, individual 640a must pass through or within a glance notice area 620. And for camera 610 to detect the length of time individual 640a glances at the digital display screen, that is, the glance dwell time, individual 640a must be inside area 615. Thus, the ability of video camera and associated back-end processing functionality to capture and isolate a unique viewer impacts the ability of digital sign module to gather video analytic data on a per viewer basis.

In one embodiment, the ability to track a viewer over a larger area is accomplished by employing multiple observers, that is, multiple cameras. FIG. 6B illustrates an environment 600 in which a digital display screen 610 is located. In this embodiment, three front-facing video cameras 610a, 610b, and 610c are mounted on top of the screen. An overall viewing area is illustrated by the perimeter defined by line 630. Individuals 640a and 640b pass through this viewing area in the direction of travel indicated by respective dashed lines 641a and 641b. However, the video cameras and associated back-end processing are capable of capturing video and detecting unique individuals only within observable area 625. Since there are multiple video cameras, and because of the different locations in which the video cameras 610a, 610b and 610c are placed, and the different directions in which the video cameras are pointed, a larger viewing area is established compared to the embodiment illustrated in FIG. 6A. Since camera observing areas are adjacent and perhaps even overlap, it is feasible to track a viewer over a greater area/over a longer period of time. Thus, both individual 640a and 640b are capable of being observed and isolated by the video cameras. The video cameras are also able to detect a gaze or glance by both individuals since they both pass through or within a glance notice area 620a and 620b. And the cameras are able to detect the length of time individual 640a glances at the digital display screen, that is, the glance dwell time while tracking individual 640a inside areas 615a and 615b. However, since individual 640b passes through area 615a but not 615b, the ability of the cameras to determine glance dwell time is limited to while individual 640b is only within area 615a.

It is contemplated that multiple digital signs, or multiple digital display screens, may be co-located, for example, in a department store or shopping mall that may be concurrently running distinct or different advertising campaigns. The different departments can deploy the multiple digital signs in adjacent or nearby digital sign zones. The signs and digital advertisements displayed thereon may be hosted by the same or different companies or advertisers, and each zone may want to derive distinct anonymous video analytics for their customers, or distinct data per advertisement per zone. It is also contemplated according to an embodiment of the invention that advertisements may cross multiple zones, for example, in order to measure effectiveness of storewide advertising, such as store branding, special offers, etc.

It is appreciated that the video cameras and back-end video processing functionality collect sufficient information that individuals may be personally identifiable, raising privacy concerns depending on the country, state, or context. An embodiment of the invention assigns an anonymous tag to each unique individual viewer while the viewer is within an observable area. The tag, then, rather than the individual, is associated with glance activity and demographic data, etc. When the viewer leaves an observable area, the tag may be kept active for a while in case the viewer re-enters the observable area and can be reliably identified as the same viewer. Within a reasonably short time, however, the tag is recycled and made available to use to track another individual who enters the observable area.

In one embodiment, face detection functionality is implemented in algorithms embodied in software executed in conjunction with age and gender classification algorithms also embodied in software. In one embodiment, the Viola-Jones object detection framework developed by Paul Viola and Michael Jones in 2001 is used for facial recognition. The features employed by the detection framework involve the sums of image pixels within rectangular areas. The Haar Classifier algorithm is used for age and gender classification in one embodiment of the invention. Software embodying these algorithms is executed in conjunction with software that gathers demographic information, as well as computer vision software, by computer processors, such as Intel Core 15 and Intel Core 17 processors and associated hardware.

In one embodiment, the computer vision software is based on the OpenCV. OpenCV is an open source computer vision library developed by Intel Corporation, the assignee of the present invention, and is free for commercial and research use under a Berkeley Software Distribution (BSD) license. The library is cross-platform, and runs on Apple's Mac OS X, as well as Microsoft's Windows operating system, and Linux-based operating systems. It focuses mainly towards real-time image processing. Generally, the OpenCV library supports real-time capture, video file import, basic image treatment (brightness, contrast, threshold, etc.), and object detection (face, body, etc.).

With reference to FIG. 7, an embodiment of the invention as it relates to capturing anonymous video analytic data is described. Flow chart 700 starts at 705 wherein screen shots of viewers are captured. One or more optical capture devices such as a still camera or a video camera are mounted on or near a digital sign and take screen shots (e.g, photos) at frequent intervals of time, say every 5 seconds. Thus, during one minute, 12 screen shots are captured. These digital images are collected for a particular period of time, say 1 minute, at 710. Each of the images in a particular collection is tagged with a unique tag and a number identifying each image is added to the unique tag, e.g., hour2-minute1-image1, hour2-minute1-image2 . . . hour2-minute1-image12, at 715. At 720, each individual in the first image, i.e., hour1-minute1-image1 is then identified. For example, the first individual identified may have a tag such as hour2-minute1-image1-individual-1 associated with him. The individual is then profiled for sex and age, and perhaps other personal characteristics. This same process is repeated for other individuals, if any, in the same photo, e.g., hour2-minute1-image1-individual-2, hour2-minute1-image1-individual-3, etc.

At 725, the individuals identified and tagged in the first photo at 720 are searched for and identified in the rest of the photos in the same collection, e.g., the remaining 11 images or photos in the hour2-minute1-image1 collection. For every photo in which the same individual is identified, a period of time is added to the dwell time parameter for that individual. For example, every time the same individual is recognized in one of 12 photos in the collection, 5 seconds is added to the dwell time parameter for that individual. At 735, each new individual, if any, that wasn't identified in the first photo of the collection but appears in the second photo is identified in the second photo, and the process described above in 720 and 725 repeats for the second photo in the collection, and the process repeats again for any unidentified individuals in the remaining sequence of photos in the collection.

Once the tagging and computations are performed for a particular collection, at box 740 in the flow chart, the tagging and computations are performed for the next collection, e.g., the next one minute of photos, and at box 745, the tagging and computations are performed for the next one hour of photos. At 730, the dwell time based on particular parameters or personal characteristics, such as age and gender is tabulated for every collection (e.g., every minute) into a consolidated list.

Targeted Advertising

The point of targeted advertising is to show a future audience certain advertisements that have in the past been viewed for a reasonable amount of time by a previous audience having the same or similar demographics as the future audience. The process of targeted advertising according to an embodiment of the invention can be characterized in three phases and corresponding components of the digital advertising system according to an embodiment of the invention: training advertising models in the data mining module 110, creating customized ad lists, or playlists, in the CMS 120, and playing the playlists with a digital sign module 105.

Training Advertising Models

Data mining technology involves exploring large amounts of data to find hidden patterns and relationship between different variables in the dataset. These findings can be validated against a new dataset. A typical usage of data mining is to use the discovered pattern in the historical data to make a prediction regarding new data. In embodiments of the invention, the data mining module 110 is responsible for training and querying advertising models. In particular, two types of advertising models are generated, an advertising category (ad category) model, and a passer pattern model. In the ad category model, a set of rules is correlated with the most appropriate ad category for particular audience or context (e.g., time, location, weather). As an example (in which deviceID is an identifier associated with a particular digital sign and can be used to indicate the location of the digital sign):

If deviceID=561 and time=morning and day=Friday and gender=female and age=young and weather=clear and targeted ads=outdoor, then potential target=very strong (e.f., at 80% confidence)

If deviceID=561 and time=morning and day=Friday and weather=clear and targeted ads=Ad list 1, then potential target=likely (e.g., at 70% confidence)

In the above example, “Ad list 1” is a list from an ad category provided by an advertiser for a particular context, also stored in the CMS. In the passer pattern model, in which viewers pass by a digital sign, a set of rules assists in discovering audience behavior or predicting future audience based on previous audience. As an example:

If deviceID=561 and time=morning and day=Friday and weather=clear, then passer type=senior female (at 70% confidence)

If deviceID=561 and time=morning and day=Friday and IsWeekend=0 and weather=clear and WOPMaleSenior=0˜1 and WOPFemaleSenior=0˜1 and WOPMaleAdult=0˜1 and WOPFemaleAdult=0˜1 and WOPMaleYoungAdult=0˜1 and WOPFemaleYoungAdult=0˜1 and WOPMaleChild=0˜1 and WOPFemaleChild=0˜1 then WONMaleSenior=0˜1 and WONFemaleSenior=0˜1 and WONMaleAdult=0˜1 and WONFemaleAdult=0˜1 and WONMaleYoungAdult=0˜1 and WONFemaleYoungAdult=0˜1 and WONMaleChild=0˜1 and WONFemaleChild=0˜1.

FIG. 3 provides an illustration 300 of the video analytic data 305 gathered by the digital sign module 105 and provided as input to the data mining module 110 along with advertising data 310, and weather data 315 also provided as input to the data mining module. At 325, the data mining module, in one embodiment, generates and trains, that is, refines, models on a regular basis, whether daily, weekly, monthly, or quarterly, depending on the context and data characteristics, the basic principle being that if the patterns/rules derived from historical data don't change, there is no need to train or regenerate models.

Video analytic data 305, according to one embodiment of the invention, comprises the date and time a particular digital advertisement was displayed on the digital sign, as well the day the ad was displayed, a deviceID or alternatively a displayID that indicates a location at which the ad was displayed. Sensor input also provides the amount of time that the digital ad was viewed while being displayed on the digital display device. Finally, an indication of the potential target viewership based on characteristics such as age and gender is included.

Advertising data 310, received by data mining module 110 from the advertisements repository 125, includes the date and time a particular digital advertisement was scheduled for display on the digital sign, as well a deviceID or alternatively a displayID that indicates a location at which the ad was scheduled to be displayed, and a duration or length of the digital advertisement, in seconds. Weather data 315 includes the date, temperature, and conditions on or around the date and time the digital advertising was displayed on the digital sign.

2. Creating Advertising List

After the advertising models are generated by data mining module 110, the models are transferred to the Content Management System (CMS) 120. The CMS then extracts the ad categories from the ad category models and creates an ad category list. The advertising data corresponding to these ad categories are then retrieved from a permanent store, such as a database, accessible to CMS 120. Based on the ad category list, CMS 120 also creates advertisement lists. In one embodiment of the invention, a generated ad list may be modified based on advertiser input at 125. In one embodiment, each advertiser is assigned a certain priority that can be used as a basis for rearranging the ad list.

FIG. 4 illustrates the flow of events and information 400 in the CMS 120. The CMS probes the data mining module 110. The frequency of probing in one embodiment of the invention is once a day. The CMS gets all the current rules and predictive lists generated by the data mining module and stores the information in a permanent store. Advertisements corresponding to particular categories are obtained from the tentative playlist based on advertiser preferences, the ad list generator, and advertisement repository 125. In “offline mode” the tentative playlist is used as the default playlist. A data store, such as the Structured Query Language (SQL) server database depicted in FIG. 4, is associated with the advertisements repository 125, according to one embodiment. From that data store various information is retrieved including advertising data for the particular categories such as the advertising name, the advertising type, and a path in a file directory of the ad repository that holds the files for the actual advertisements. The CMS connects to the advertising repository to get the advertisements located at the given paths. All the models and the corresponding advertising lists generated so far get stored at the CMS. A digital sign module will only contain a subset of these models and advertising lists that are suitable for the digital sign module's targeted audience. The CMS connects to the digital sign module and pushes to it the models and advertising lists suitable for it.

Referring again to FIG. 4, the Player Specific Model Extractor 435 connects to the data mining module 110, and obtains both the passer pattern and ad category models. These models are segregated per player and sent to digital sign module (digital player) 105. Data mining module 110 provides models that are suitable for the current day and date as well as the current weather, for example, the current day is Friday Sep. 9, 2011, with a forecasted clear morning and a rainy evening. The model extractor 415 extracts the ad categories from ad category models and sends such to the ad(vertising) list generator 420 for each digital sign. The models are parsed and an advertisement is selected for each time slot. For example, assuming that the average advertisement duration is 10 seconds, 360 advertisements are selected for each hour.

The ad list generator 420 fetches ads for the categories that are scheduled for a particular day, along with the advertising data. The tentative play list generator module analyzes the ad list and generates a tentative play list that is sent to the advertiser input scheduler. Generator 420 compiles a play list based on arranged advertising categories, and an advertising list. The selection of advertisements is based on the roulette-wheel selection, according to one embodiment, where each advertisement is randomly picked based on a probability. The advertiser input scheduler module 420 fetches advertiser input and incorporates advertiser preferences in the tentative play list to generate the default play list which is sent to the digital sign module.

The ad refresh module 405 checks for new advertisements by comparing the versions maintained in a permanent store, e.g., a database, accessible to the CMS against versions obtained from the advertisements repository. If a new version of an advertisement is found then the actual advertisements (video files) are transferred to the digital sign module. If new ads (ads which were not present earlier in the ad repository) are present then module 405 fetches advertising data from SQL server DB 440 and sends such to the digital sign module 110.

3. Playing Playlist with Digital Sign Module

CMS 120 transfers the ad list at 140 to the digital sign module 105. In one embodiment, digital sign module generates a default playlist by extracting file directory path information from the ad list and then retrieving the corresponding advertisements from an advertisements repository 125 that holds the advertisement files. The digital sign module operates in both an online and an offline mode. In the offline mode, the default playlist is played to the digital sign. The playlist for the online mode is generated using the real time VA data described below with reference to FIG. 5 which illustrates the flow of events and information 500 in the digital sign module (digital player) 105.

The video analytic (VA) analyzer (predictor) module 510 fetches real time VA data and retrieves passer pattern models from CMS 120 to predict VA data. The predicted VA data is sent to model analyzer module 515. The model analyzer module 515 receives the predicted VA data as input and retrieves ad category models from CMS 120 and extracts an advertising category based on the predicted VA data. In one embodiment, confidence values of the passer pattern model and the ad category model are multiplied to generate a multiplied confidence value. If the multiplied confidence value is greater than a threshold, then an advertisement for the extracted advertising category is sent to the tentative play list generator 520, otherwise the digital sign module continues in an offline mode. The tentative play list generator module 520 retrieves an advertising list from CMS 120 and generates the tentative play list by considering the advertising category from the model analyzer and sends the tentative play list to online mode.

Scheduler module 525 contains following three sub modules, an online sub-module that selects an advertisement based on a probability distribution and associates it with an actual advertisement that is then scheduled and sent to display at 545, an offline sub-module that selects an advertisement from a default play list based on the scheduling time and associates it with an actual advertisement that is then scheduled and sent to display at 545, and a preference sub-module that checks for an advertiser preference and schedules an advertiser preferred advertisement for display at 545.

Real Time Content Triggering

According to an embodiment of the invention, viewers are targeted in real time. The real time processing takes place at the digital sign module. Each digital sign module receives both advertising category as well as passer pattern models from the CMS. Broadly speaking, according to one embodiment, a plurality of viewers are detected, the demographics of those viewers are analyzed, and viewing patterns for those viewers is collected. Based thereon, advertisements are targeted to the digital sign module. In one embodiment, the passer pattern model has a parameter referred to as the confidence value that indicates whether to play digital advertisements in online mode or offline mode. Thus, when the AVA data is analyzed in real time mode, the rules from the passer pattern model are chosen and the confidence value attached to these rules is compared with a threshold value. If the confidence value falls short of the threshold, then the default playlist is played, but if the value is the same or greater than the threshold, then the advertisements list is modified and advertisements targeting current viewers are played. After the current advertisement is played, either the digital sign module can return to playing the default playlist or could continue playing targeted advertisements.

CONCLUSION

In this description, numerous details have been set forth to provide a more thorough explanation of embodiments of the present invention. It should be apparent, however, to one skilled in the art, that embodiments of the present invention may be practiced without these specific details. In other instances, well-known structures and devices have been shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.

Some portions of this detailed description are presented in terms of algorithms and symbolic representations of operations on data within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, as apparent from this discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of present invention also relate to apparatuses for performing the operations herein. Some apparatuses may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, DVD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, NVRAMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems appear from the description herein. In addition, embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.

A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine-readable medium includes read only memory (“ROM”); random access memory (“RAM”); magnetic disk storage media; optical storage media; flash memory devices; etc.

Whereas many alterations and modifications of the embodiment of the present invention will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that any particular embodiment shown and described by way of illustration is in no way intended to be considered limiting. Therefore, references to details of various embodiments are not intended to limit the scope of the claims that recite only those features regarded as essential to the invention.

Claims

1. (canceled)

2. A method for selecting digital content to be displayed on a digital display screen, the method comprising:

identifying a digital display screen;

gathering analytics data associated with one or more viewers of the digital display screen, the analytics data obtained from a sensor that is at least one of included in, proximate to, or communicatively coupled to the digital display screen;

identifying an attribute of the one or more viewers of the digital display screen;

requesting an output to be displayed on the digital display screen from a content information system, the content information system using a trained model to determine an effectiveness of the output to be displayed on the digital display screen based on the identified attribute of the one or more viewers and selecting the output based on the effectiveness of the output for the one or more viewers;

receiving the output to be displayed on the digital display screen from the content information system; and

displaying the output on the digital display screen.

3. The method of claim 2 wherein gathering analytics data includes detecting a length of time that the one or more viewers of the digital display screen glances at the digital display screen.

4. The method of claim 2, wherein the sensor is a camera located proximate to the digital display screen.

5. The method of claim 2, further comprising:

anonymizing the analytics data to create anonymous analytics data.

6. The method of claim 2, wherein the analytics data includes a viewing pattern of the one or more viewers of the digital display screen.

7. The method of claim 2, wherein the analytics data is a demographic characteristic of the one or more viewers of the digital display screen.

8. The method of claim 7, wherein the demographic characteristic of the one or more viewers is at least one physical characteristic of the one or more viewers.

9. The method of claim 7, wherein the effectiveness of the output is based on an impact of the output on one or more previous viewers of the digital display screen making up a demographic group, wherein the one or more previous viewers have at least one similar demographic characteristic as the one or more viewers of the digital display screen, and wherein the impact of the output on the demographic group is determined by correlating sales data from a point of sale terminal with analytics data of the demographic group.

10. The method of claim 2, wherein the output is an advertisement selected by the trained model, and wherein the advertisement is provided from a server to the digital display screen.

11. At least one non-transitory machine-readable medium containing instructions that, when executed by one or more processors of a computer, cause the computer to:

gather, using a sensor that is at least one of included in, proximate to, or communicatively coupled to a digital display screen, analytics data associated with one or more viewers of the digital display screen;

identify an attribute of the one or more viewers of the digital display screen;

request an output to be displayed on the digital display screen from a content information system, the content information system using a trained model to determine an effectiveness of the output to be displayed on the digital display screen based on the identified attribute of the one or more viewers and selecting the output based on the effectiveness of the output for the one or more viewers;

receive the output to be displayed on the digital display screen from the content information system; and

display the output on the digital display screen.

12. The at least one non-transitory machine-readable medium of claim 11, wherein gathering analytics data includes detecting a length of time the one or more viewers of the digital display screen glances at the digital display screen.

13. The at least one non-transitory machine-readable medium of claim 11, wherein the sensor is a camera located proximate to the digital display screen, wherein the output is an advertisement selected by the trained model, and wherein the advertisement is provided from a server to the digital display screen.

14. The at least one non-transitory machine-readable medium of claim 11, wherein the instructions further cause the processor to:

anonymize the analytics data to create anonymous analytics data.

15. The at least one non-transitory machine-readable medium of claim 11, wherein the analytics data includes a viewing pattern of the one or more viewers of the digital display screen.

16. The at least one non-transitory machine-readable medium of claim 11, wherein the analytics data is a demographic characteristic of the one or more viewers of the digital display screen.

17. The at least one non-transitory machine-readable medium of claim 16, wherein the demographic characteristic of the one or more viewers is at least one physical characteristic of the one or more viewers.

18. The at least one non-transitory machine-readable medium of claim 16, wherein the effectiveness of the output is based on an impact of the output on one or more previous viewers of the digital display screen making up a demographic group, wherein the one or more previous viewers have at least one similar demographic characteristic as the one or more viewers of the digital display screen, and wherein the impact of the output on the demographic group is determined by correlating sales data from a point of sale terminal with analytics data of the demographic group.

19. A digital display device comprising:

a display screen;

processor circuitry;

communication circuitry to communicate with a content information system; and

memory, including instructions that, when executed by the processor, cause the processor circuitry to: gather, using a sensor that is at least one of included in, proximate to, or communicatively coupled to the digital display screen, analytics data associated with one or more viewers of the digital display screen; identify an attribute of the one or more viewers of the digital display screen; request, via the communication circuitry, an output to be displayed on the display screen from the content information system, the content information system using a trained model to determine an effectiveness of the output to be displayed on the display screen based on the identified attribute of the one or more viewers and selecting the output based on the effectiveness of the output for the one or more viewers; receive, via the communication circuitry, the output to be displayed on the display screen from the content information system; and display the output on the display screen.

20. The digital display device of claim 19, wherein gathering analytics data includes detecting a length of time the one or more viewers of the display screen glances at the display screen.

21. The digital display device of claim 19, wherein the sensor is a camera located proximate to the display screen, wherein the output is an advertisement selected by the trained model, and wherein the advertisement is provided from a server to the display screen.

22. The digital display device of claim 19, wherein the instructions further cause the processor circuitry to:

anonymize the analytics data to create anonymous analytics data.

23. The digital display device of claim 19, wherein the analytics data is a demographic characteristic of the one or more viewers of the display screen.

24. The digital display screen of claim 23, wherein the demographic characteristic of the one or more viewers is at least one physical characteristic of the one or more viewers.

25. The digital display screen of claim 23, wherein the effectiveness of the output is based on an impact of the output on one or more previous viewers of the display screen making up a demographic group, wherein the one or more previous viewers have at least one similar demographic characteristic as the one or more viewers of the display screen, and wherein the impact of the output on the demographic group is determined by correlating sales data from a point of sale terminal with analytics data of the demographic group.