Abstract:
The present disclosure relates to systems and methods for gathering analytical information on TV viewers to provide targeted ads on mobile devices. SDKs are provided that allows for the capture of viewers&#39; TV watching habits through their personal electronic devices. The TV watching habits are analyzed and stored to create user profiles. Based on these user profiles, highly relevant and targeted ads are matched and delivered to individual users.

Description:
TECHNICAL FIELD 
       [0001]    The present disclosure relates generally to systems and methods that provide for delivery of targeted mobile ad delivery based on TV viewing habits of individual consumers. 
       BACKGROUND 
       [0002]    Even though consumer electronics and the face of entertainment have changed significantly over the last 10 years, consumers are still watching a lot of television. According to Nielsen, at least 83% of people in over 50 countries watch TV regularly and spend an average of 34 hours per week in front of TV. What has changed is how consumers are watching TV. Again according to Nielsen, 85% of tablet and smartphone owners use their device as a second screen while watching TV at least once a month with 40% of them doing it daily. 
         [0003]    In this new environment, advertisers are losing eyes during commercial breaks as people turn to their mobile device or hit the 30 second skip on the remote control. They are seeking innovative and effective ways to reach consumers. 
         [0004]    Identifying the TV program and the associated demographic significantly increases the mobile ad revenue. This benefits everyone in the mobile advertising chain with the publisher getting more income for their inventory, the ad network getting more revenue without increasing share percentage, the advertiser reaching their target audience more efficiently, and the consumer by providing a better user experience by way of receiving ads that are relevant. 
       BRIEF SUMMARY 
       [0005]    The solution reaches consumers on their mobile device while they are watching television and targets ads based on the programs being watched. The solution does the following:
       Provides ubiquitous SDKs that allow app and mobile website developers and owners to easily monetize mobile ad inventory at high value   Captures a fingerprint of the program being watched in background and identifies program without disrupting user experience   Identifies demographic and target user group based on current program being viewed as well as viewing history of individual users   Combines learning with complex data algorithms including user profiling and sentiment analysis   Ascertains advertiser demand in real-time   Delivers targeted advertising messages to the mobile devices utilizing Real-Time Bidding and other modern mobile ad matching and delivery mechanisms.       
 
         [0012]    The solution provides an effective way for advertisers to reach their target audience while they are engaged with the mobile device. It also facilitates an environment where consumers are receiving mobile ads that are relevant to them. For example, someone watching a football game might get an ad for $10 off an order for a pizza. A mobile user who has children&#39;s programming in the background might get an ad for the toy sale. Someone that watches daytime programming and fashion related TV might get an ad for the latest women&#39;s shoes.  FIG. 2  provides a high level view of how mobile ads are delivered based on whether or not the solution is employed. 
         [0013]    The solution is not application specific. It can be implemented in nearly any mobile app or website and works on any smart device that has a microphone. It works while the user is watching live or recorded programming and it runs in the background, thus not disrupting the user experience. 
         [0014]    The solution provides the end to end solution including:
       The SDK Installed in the app or mobile website   Integration into the comprehensive mobile ad network ecosystem   The data analysis and sentiment engine       
 
         [0018]    By anonymously storing the viewing history of a user, the solution allows for the delivery of targeted advertising even when the user is not watching television programming at the time a mobile ad is delivered. The solution continually builds on and analyzes the programs a user watches to deliver the best ad at the right time. The solution can even be combined with geo location applications to provide even greater targeting and value. For example, if the user has been identified as someone that watches golf instruction shows on TV, a mobile ad may be displayed for a local golf store in the area of the user. 
         [0019]    Data and sentiment analysis is at the core of the solution. The metadata from the television programs is continually analyzed along with viewers&#39; habits to provide the best targeting possible. The program title, genre, description, cast, theme, etc. is used to determine which ad is the most appropriate for an individual user. For example, if someone watches programs in which a specific actress stars as well as a talk show in which she appears, they may get presented an ad to pre-purchase tickets to a new actress&#39;s movie. 
         [0020]    User habits are also analyzed to not only deliver the best ad but also do it at the right time. This is done based on data such as what type of ads in which a user is likely to engage or if someone is more likely to click on an ad early or late in a program or during a commercial break. Additional information is incorporated as well such as the ideal number of times and period a user is shown an ad to achieve engagement. 
         [0021]    Sentiment analysis around the main topics of the program being watched is a key component to the solution. Using the sentiment engine a determination is made whether the sentiment concerning the topic is positive or negative. If the sentiment is positive then an ad related to that specific topic is more likely to be delivered. Conversely, if the sentiment is negative toward a certain topic then an ad related to that topic is more likely to be avoided. 
         [0022]    The solution also provides valuable feedback to advertisers with comprehensive reporting and results from the sentiment analysis engine. For example, the solution continually monitors key websites including blogs and social networks to determine the current and trending sentiment towards an advertiser, product, or specific campaign. Advertisers can use this information to tailor their campaigns and ad spending to their best interests and those of their consumers. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0023]      FIG. 1  is a block diagram illustrating the system at a high level. 
           [0024]      FIG. 2  is a flow chart illustrating the standard flow through the system. 
       
    
    
     DETAILED DESCRIPTION 
       [0025]    For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numbers indicate like features. 
       FIG.  1   
       [0026]      FIG. 1  is a high level diagram of the system as a whole, the main components and how they interact with each other. 
         [0027]    One way to implement the described embodiments is to provide an SDK (piece or pieces of code) to an app developer to be incorporated into an existing app. Thus, an app running the presently described approach can insert the SDK and run on a ‘smart’ mobile device and will work on any smart device that has a microphone. The app that contains the SDK could be something directly related to TV related material or be completely unrelated (e.g., a game such as Angry Birds). While the app is being used for its intended activity, in the background a small sound fingerprint is recorded and sent as described below via an Application Programming Interface (“API”) for identification against a database of recorded TV Broadcasts (movies and music as well). If the fingerprint is identified then the broadcast related data is analyzed along with existing user data to create a profile for a targeted mobile ad to be delivered to the mobile device. The program data is also stored with the existing user data to enhance the user profile. 
         [0028]    Devices  110  represent consumer mobile devices including but not limited to mobile phones, tablets, laptops, and personal smart devices. 
         [0029]    App Server  104  is a server that is related directly to the specific app or website in use on the mobile device. The App Server  104  in this context represents functions directly related to the app such downloading the app for the first time, accessing additional features and functions, registration, and it can also act as a gatekeeper for activities such as ad delivery. The App Server  104  may or may not be involved in the mobile ad delivery process but is represented in  FIG. 1  as it will sometimes be the first point of access. 
         [0030]    Main Application Server  102  is at the core of the solution. It manages the main processes including data analysis, user profiling, database storage management, interaction with devices  110 , Ad Server  130 , and Merchant Server  120 . The Main Application Server  102  communicates with the devices  110  to receive an audio fingerprint and gather anonymous user identifiers and specific viewing data. The user and viewing data is stored in the User Database  108 . The Main Application Server  102  also does a Sentiment Analysis on the main topics of the program during the time of user viewing. This sentiment is used to help determine if an ad related to the main topic should be sent or avoided. The Main Application Server  102  analyzes the newly acquired data along with the existing user and program data to build a profile for an ad request. The ad request with the profile is subsequently sent to the Ad Server  130 , sometimes via the Merchant Server  120 . Once the ad is received from the Merchant Server  120 , it logs the ad details in the User Database  108  and delivers the ad to the devices  110 . The Main Application Server  102  receives subsequent User Ad Activity data based on the user&#39;s behavior with the ad and stores it in the User Database  108 . The User Ad Activity includes data such as if the user clicked on the ad, when they clicked on the ad, and what they did after clicking on the ad. 
         [0031]    Media Database  150  is the database that stores the TV program information which includes sound matching files as well as program metadata. The Media Database  150  receives the sound fingerprint from the Main Application Server  102  and looks for a match against the sound files in the database. If a match is found it returns the program name and additional program information including the original broadcast channel, the time at which the broadcast originally took place, and other descriptive details including genre, program description, main topics at time of broadcast and specific spoken words around the topics. The fingerprint matching files and technology as well as program related data could be generated in-house, licensed and accessed from a third party, or any combination thereof. 
         [0032]    User Database  108  stores user information including identifiers and profiles. The user information is stored under an anonymous ID associated with the specific device or application. The user data includes raw data as well as analyzed data from TV programming viewing habits, apps used, interaction with served ads, device type, and other relevant data. The users do not have access to their user data. 
         [0033]    Program Memory  103  provides all the logic and programs used for data analysis, sentiment analysis, user profile building, ad-to-profile matching, etc. The logic and programs are stored as computer-readable instructions that can be accessed by the Main Application Server  102  to execute the functions described in the disclosed embodiments. 
         [0034]    Merchant Server  120  is the entity behind the app or mobile website utilizing the solution. This may or may not interact directly with the Main Application Server  102 . The Merchant Server  120  is owned and maintained by an outside party and in most cases manages many components of the app or mobile website such as game scores, multi-player sessions, etc. Oftentimes, the Merchant Server  120  also manages ad selection and delivery and in this case the Main Application Server  102  would communicate directly with the Merchant Server  120 . In other cases where the ad component is not handled by the Merchant Server  120 , there may be no requirement for direct communication from the Main Application Server  102 . 
         [0035]    Ad Server  130  is where the ads that are ultimately delivered to the devices are located. This could include mobile ad network, mobile advertising platform, a real-time bidding system, or other ad delivery mechanism. When the Main Application Server  102  generates a profile for an ad it sends it to the Ad Server  130  for a match to that profile. The request could include a request based on a specific demographic such as a male, age 21-30, interested in sports. The Ad Server  130  would then find a match, ideally at the highest paying advertiser for that demographic, and then send the ad back to the Main Application Server  102  for ultimate delivery to the devices  110 . Additional examples of profiles could include a golf ad for zip code xxxxx or movie tickets for the upcoming release of the movie starring a specific actress. 
         [0036]    Media Server  140  encompasses the TV programming that is delivered to users. The variety of delivery mechanisms is vast and may include traditional broadcast television to a standard television set, a streaming solution such as Netflix to a laptop, a DVD being watched on a game console, as well as a number of other solutions. The creation and source of the broadcast content is independent of the solution. When a user is using an app with the SDK component of the solution embedded it can recognize the program being broadcast regardless of the delivery source of that broadcast. 
       FIG.  2   
       [0037]      FIG. 2  shows the standard flow through the system. 
         [0038]    Step  201 : The app or mobile website developer incorporates the SDK code into their source code before distribution to the users. The SDK includes all the code that facilitates the capturing of the sound fingerprint as well as all the communication between the devices  110  and the Main Application Server  102 . The SDK can be made available to the developers in a number of different ways. It could be downloaded from a website or provided directly by a mobile ad network, a mobile carrier or other third party. 
         [0039]    Step  202 : The user downloads the app that contains the SDK and installs on their device  110 . The app can be downloaded from any location authorized to provide mobile apps to users. The app could also have been pre-installed on the device  110 . The user then launches the app and uses it for its intended purpose. The app may or may not be related to television. 
         [0040]    Step  203 : At a pre-established interval, a fingerprint is taken of the background sound while the user is within the app. The fingerprint is captured without any disruption of the user experience. 
         [0041]    Step  204 : The sound fingerprint is sent to the Main Application Server  102  for identification against the Media Database  150 . 
         [0042]    Step  205 : The Media Database  150  determines if the fingerprint matches any program within the database as described earlier in the document. 
         [0043]    Step  206 : If there is a match from Step  205 , the data available for the TV program is analyzed by the Main Application Server  102 . 
         [0044]    Step  207 : Once the data from the TV program is analyzed it is combined with the analyzed User Profile Data from the User Database  108  and a profile for the ad(s) to be requested is created by the Main Application Server  102 . 
         [0045]    Step  208 : If there is not a match from Step  205 , the existing User Profile Data from the User Database  108  is analyzed by the Main Application Server  102  and a profile for the ad(s) to be requested is created for submission to the Ad Server  130 . 
         [0046]    Step  209 : A request for a mobile ad based on the created profile is submitted by the Main Application Server  102  to the Ad Server  130  and an ad is returned from the Ad Server  130  with relevant details. Details could include elements like dimension of the ad, structure of the ad (e.g. static, rich media, video), how many components of the profile were a match, cost and required action (e.g. view, click, sign up). 
         [0047]    Step  210 : The ad is delivered to the device  110  so it can be displayed at the time stipulated by the app. Some examples of the proper time to display an app would include during a commercial break in the TV program or in between levels of a game. The ad could be audio, video, or web banner. 
         [0048]    Step  211 : The data from the user interaction with the ad is captured and sent to the Main Application Server  102  for analysis and subsequent storage in the User Database  108 .