Patent Publication Number: US-2015081442-A1

Title: Use of personal preferences to control presentation of advertisements

Description:
RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 14/081,411, filed on Sep. 4, 2013, titled USE OF PERSONAL PREFERENCES TO CONTROL PRESENTATION OF ADVERTISEMENTS, which claims the benefit of U.S. Provisional Application Ser. No. 61/696,679, filed on Sep. 4, 2012, titled USE OF PERSONAL PREFERENCES TO CONTROL PRESENTATION OF ADVERTISEMENTS. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates to a software-based system and method for management and display of advertisements based on personal preference information. 
     BACKGROUND 
     Brands and advertisers communicate to consumers by pushing ads at them in a multitude of ways. Advertising is served to consumers through multiple networks, on various platforms, and experienced through various form factors. Advertising is everywhere the average consumer is: at home, on the road, on the phone, on the Internet, at work—wherever consumers are, advertisers are trying to push their offers and/or value proposition to them, and they are becoming cleverer about how to reach the right target, at the right time with the right message. 
     Brands and advertising agencies project their messages based on differing degrees of demographic data and, increasingly in the digital space, on behavioral data along with loyalty programs. In some instances, consumers can opt-out of unwanted and noisy advertising through channels like the National Do Not Call Registry or AdChoices. 
     Unfortunately, these are often difficult to navigate and not easy for the average consumer to understand. Furthermore, simply opting into or out of marketing is not an ideal solution. Advertising is a beneficial activity for both brands and consumers when done correctly. No solution currently exists to allow for advertising to flow to consumers, with consumers having control to choose the advertisements that are relevant to them based on their own personal criteria. 
     SUMMARY 
     Consumer driven, controlled and requested advertising puts the consumer in the driver&#39;s seat of the advertising to which they are exposed, across multiple platforms (advertising and content networks) as well as multiple form factors, for example, the Internet, mobile web browsers and applications, and television. Consumers opt in to this service and leverage the technology to receive relevant and personalized advertisements, thereby reducing some of the noise. Advertisers will benefit by leveraging the technology to direct their marketing messages and promotions to consumers who are “asking” for them, versus hoping they “touch” some relevant consumers through mass targeting. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an example system. 
         FIG. 2  is a flow chart illustrating an example of where and how users will interact with the consumer front-end of the experience controlling their advertisements in one embodiment of the present invention. 
         FIG. 3  is a flowchart illustrating an example operation performed by a Consumer Controlled Advertising API in one embodiment of the present invention. 
         FIGS. 4 and 4   a  describe an example graphical user interface for the Preference Center in one embodiment of the present invention. 
         FIG. 5  is a flow chart showing the Meta Data Enrichment process in one embodiment of the present invention. 
         FIG. 6  is a flow chart illustrating the Consumer Controlled Advertising API interface in one embodiment of the present invention. 
         FIG. 7  illustrates an example of a user&#39;s dashboard on a graphical user interface in one embodiment of the present invention. 
         FIG. 8  illustrates an example of categories and subcategories available for selection by the user on a graphical user interface in one embodiment of the present invention. 
         FIG. 9  illustrates an example of a selected category and corresponding advertisement on a graphical user interface in one embodiment of the present invention. 
         FIG. 10  illustrates an example of a displayed advertisement on a graphical user interface while using a web service in one embodiment of the present invention. 
         FIG. 11  illustrates a second example of a displayed advertisement on a graphical user interface while using a web service in one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram illustrating an example system  100 . As illustrated in the example of  FIG. 1 , the system  100  comprises a set of a Preference Center  101 , Meta Data Enrichment  102 , Ad Warehouse  103 , Consumer Controlled Advertising API  104 , Business Intelligence  105 , End User Devices  106 , Ad Networks or Publishers  107 , Advertisers  108 , User ID (UID)  109 , User Preferences (Pref)  110 , Advertising Assets (Asset)  111 , Advertisement (Ad)  112 , Publisher Context (Context)  113 , Reporting Data (Data)  114 , Meta Data (MD)  116 , and Network  115 . Preference Center  101 , Meta Data Enrichment  102 , Ad Warehouse  103 , Consumer Controlled Advertising API  104 , Business Intelligence  105 , End User Devices  106 , and Ad Networks or Publishers  107  are computing systems. 
     The Network  115  facilitates communication among the Preference Center  101 , Meta Data Enrichment  102 , Ad Warehouse  103 , Consumer Controlled Advertising API  104 , Business Intelligence  105 , End User Devices  106 , Ad Networks or Publishers  107 , and Advertisers  108 . In various embodiments, the Network  115  can be various types of networks. For example, the Network  115  can be a wide area network, such as the Internet. In another example, the Network  115  can be a local area network, a virtual private network, or another type of communications network. The Network  115  can include wired and/or wireless communication links. 
     The Preference Center  101  is a system of computing devices that contains a collection of User Preferences  110  for each User  117  of the system. User Preferences  110  might include, but are not limited to, personal needs, wants, wishes and desires for each content category in the system. Users  117  can create accounts, and submit and modify their User Preferences  110  through a user interface over the Network  115 . 
     The Meta Data Enrichment  102  is a system of computing devices that allows Advertisers  108  to submit combinations of Advertisements  112  and Meta Data  116 . Advertisers  108  submit the information through a user interface over the Network  115 . The Meta Data Enrichment  102  facilitates the input by detecting key attributes of the Advertisements  112  and repurposing previously entered information to help Advertisers  108  complete the combinations of Advertisements  112  and Meta Data  116 . 
     The Ad Warehouse  103  is a system of computing devices that stores combinations of Advertisements  112  and Meta Data  116  as Advertisements  112 . 
     The Consumer Controlled Advertising API  104  is a system of computing devices that determines which Advertisements  112  should be presented to a User ID  109 , considering User Preferences  110  and Publisher Context  113 . 
     The Business Intelligence  105  is a system of computing devices that collects and organizes Reporting Data  114  and presents it to Advertisers  108  for decision-making purposes. 
     As illustrated in the example of  FIG. 1 , the system  100  also comprises a set of Users  117 . The Users  117  use the End User Devices  106  to access the Preference Center  101 . The End User Devices  106  can be a variety of different types of computing devices. For example, the End User Devices  106  can be desktop computers, workstation computers, video game consoles, television set top boxes, network-connected televisions, or other types of computing devices. Furthermore, the End User Devices  106  can be mobile computing devices, such as smart phones (e.g., Apple iPhones, Motorola Droid phones), tablet computers (e.g., Apple iPads), personal media players (e.g., Apple iPods, Microsoft Zune players), in-vehicle computing systems, laptop computers, netbooks, or any other mobile computing devices. 
     The Ad Networks or Publishers  107  is a system of computing devices that publish Advertisements  112  to Users  117  on various properties, both online and offline. Ad Networks can include but are not limited to demand and supply side platforms, ad exchanges, data providers, agency trading desks, ad servers, DMP&#39;s, aggregators, etc.; Publishers  107  can include, but are not limited to, content destinations and networks like CNN, CNN.com, AOL, NBC, Time Warner, Verizon, Yahoo, Comcast, PRN, The Weather Channel, etc.; commerce publishers like Walmart.com, Amazon, eBay, Home Depot, Groupon, etc.; and social networks like Facebook, LinkedIn, Twitter, FourSquare, etc. 
     The Advertisers  108  are users that create Advertisements  112  that are intended to be published to Users  117 . Advertisers  108  can include brands and their respective agencies. Examples include Ford Motor Company, American Express, United Airlines, Proctor and Gamble brands, Sara Lee Corp, Wal-Mart, Macy&#39;s, Costco, Dell, Coca Cola, and Bank of America, among others. 
     Users  117  interact with the Preference Center  101  by submitting and modifying User Preferences  110 . The User Preferences  110  could include various products or services the User  117  needs, wants, wishes, and/or desires. Furthermore, User Preferences  110  could include, among other things, other settings including calendar planning and timing, price ranges, discount ranges, geographic locations, brand and category affinities and dislikes, communications preferences, tracking options, etc. Products and services are those offered by Advertisers  108  through their respective line of business. 
     Depending on user choice during interaction with the Preference Center  101 , users&#39; specificity and sensitivity to the choices is known and can be utilized to present the User  117  with ads. For example, if User  117  says Want=Sedan and Brand=Toyota, Honda, Infinity . . . etc., the User  117  is more specific about the Model he/she wants to buy and not so sensitive to the brand itself This can also be used to determine or develop brand analytics and present the brands with data/report on how end users are loyal to the brands as a score. 
     For examples, when someone chooses BMW but they do not choose others, that implies high loyalty. But, when someone chooses Infiniti, and they also choose Toyota, Honda and Chevrolet, their loyalty factor is low. With information like this, the system can filter the advertisements to reflect, among other things, user preference, viewing context (time, place, etc . . . ) and viewability on a particular User device. 
     Further, by extracting attributes of “customer&#39;s have&#39;s in past,” a customer&#39;s liking to certain attributes can be determined by various algorithms, one of which can be an overlap of a set containing attributes of items (choices, options), which User  117  “had” or made in the past. 
     When Users  117  have decided that they are no longer looking for a certain type of product or service that they were previously interested in, they can declare that they are “out of market,” and, therefore, opt out of certain Advertisements  112  they are currently receiving. Similarly, if Users  117  have decided they are now looking for a certain product or service, they can opt in to certain Advertisements  112  by indicating they are “in market.” 
     Advertisers  108  currently create Advertisements  112  and route them to Ad Networks or Publishers  107  to be published to Users  117 . Ad Networks or Publishers  107  typically choose which Advertisements  112  to publish to a User  117  based on previous behavior of the User  117  and/or Publisher Context  113 . Publisher Context  113  typically includes keywords on the publisher property. 
     In System  100 , Advertisers  108  will route Advertisements  112  to Meta Data Enrichment  102 . They will also add Meta Data  116  for each of the Advertisements  112 . Meta Data Enrichment  102  will also facilitate the addition of Meta Data  116  in various ways, including keywords and attachment phrases describing the content and purpose of the advertisement. Meta Data  116  includes, but is not limited to, related data sets, creative copy, available detailed product or service descriptions, offer period and description, embedded contextual content, etc. The Meta Data could be as simple as a cloud of tags or richer key/value pairs indicating users&#39; psychological needs and wants. 
     Meta Data Enrichment  102  submits the Advertisements  112  to the Ad Warehouse  103  to be stored, and to be referenced by the Consumer Controlled Advertising API  104 . Advertisements  112  are stored in a structured format, so that User Preferences  110  and Publisher Context  113  can be used as search parameters to find relevant Advertisements  112  in the Ad Warehouse  103  by the Consumer Controlled Advertising API  104 . 
     Ad Networks or Publishers  107  submit a request to Consumer Controlled Advertising API  104  each time a User  117  visits their property. If the User  117  is anonymous and cannot be identified, the request contains Publisher Context  113 . If the User  117  can be identified, the request contains Publisher Context  113  and User ID  109 . 
     The Consumer Controlled Advertising API  104  uses the Publisher Context  113  and, if available, the User ID  109 , and references the Preference Center  101  and the Ad Warehouse  103  to identify the best Advertisements  112  to be published. The Consumer Controlled Advertising API  104  considers several factors when providing a recommendation back to the Ad Networks or Publishers  107 , including but not limited to Publisher Context  113  and User Preferences  110 . 
       FIG. 2  is a flow chart illustrating where and how Users  201  will interact with the consumer front-end of the experience controlling their advertisements  200 , including but not limited to a preference center interface  203  viewed through web sites, Digital TV apps, mobile and tablet applications, personal computer and web-based applications  202 . The disclosed system is designed to work across all electronic form factors and mediums of advertising that a User  117  may be exposed to (i.e., the disclosed system is not device specific). For example, once a User  117  opts in to the service, the User  117  can work across a platform such as, but not limited to, a laptop, desktop, television, smartphone, tablet, etc. Further, the User  117  can interact with advertisements in the wild. Therefore, the User  117  is interacting at the ad and publisher levels. 
     As Users share timely and relevant information about themselves  204 , their purchase behaviors, and their product and service needs, wants, desires and wishes  209 , as well as likes and dislikes  210 , they will be directed through a psychological, intuitive experience to assist in selecting categories  205  and subcategories  206  and brands of interest to them and enable timeliness by including personal calendar planning  207  and location relevance by including location preference information  208 . A user ID is then issued for matching as illustrated in  FIG. 1 . 
     One incarnation of a matching algorithm captures a subset match on cloud of tags. Another incarnation of a matching algorithm treats key value pairs like rules for specifying matching criteria. Further, another incarnation of the matching process can utilize the feedback of individuals on the ad to prioritize the ad content 
       FIG. 3  is a flowchart illustrating an example operation  300  performed by Consumer Controlled Advertising API  104 . As illustrated in the example of  FIG. 3 , the operation  300  begins when the Ad Network or Publisher  107  submits a query  301  to Consumer Controlled Advertising API  104  containing Publisher Context  113  and User ID  109 , if available,  302 . The system then retrieves advertisements from the Ad Warehouse  303 , calculates an optimal advertisement to be published  304  and then returns advertisements to the Ad Network or Publisher  305 . 
     As discussed above, Publisher Context  113  describes the property that the Advertisement  112  will be displayed on. For example, the Publisher Context  113  can include audience data, registered shopper/visitor data, opt-in badge, button or widget, brand content and attributes, etc. In this example, the Consumer Controlled Advertising API  104  uses the Publisher Context  113  and the User ID  109  to find relevant Advertisements  112  that contain Meta Data  116  in the Ad Warehouse  103  that match the Publisher Context  113  and User ID  109 . 
     In other embodiments, either the Publisher Context  113  or the User ID  109  is not available. In these cases, the Consumer Controlled Advertising API  104  uses any information that is submitted to find relevant Advertisements  112  in the Ad Warehouse  103 . 
     To find relevant Advertisements  112  in the Ad Warehouse  103  based on Publisher Context  113  and User ID  109 , the Consumer Controlled Advertising API  104  will scroll and then match indexed data attributes associated with both consumer preferences and identifiers; and advertisement data attributes and identifiers through real-time, learning algorithmic function and integration. 
       FIGS. 4 and 4   a  describe an example graphical user interface, or GUI  400 , for the Preference Center  101 . In this example, the user is presented with their existing preferred categories  401 . The user can interact with any one of the Needs, Wants, Wishes, or Desires buttons  402 ,  403  to configure their preferences for that particular preference type. For example, if the user clicks Needs  402 , then the category tree  404  appears. The user then selects or de-selects categories, which adds or removes them from their “Needs” preferences. Additionally, more settings may be available for the user when choosing a category in this context, including location or time. 
       FIG. 5  shows the Meta Data Enrichment  102  process. This process  500  is used to attach Meta Data  116  to an Asset  111  (such as an ad). This Meta Data is later evaluated and used by the system to provide recommendations to Ad Networks and Publishers  107 . When an Asset  111  is submitted to Meta Data Enrichment  102  by an Advertiser  108 ,  501 , the Advertiser  108  can include Meta Data  116  with the Asset  111  in the electronic submission  502 . Additionally, the Advertiser  108  will gain access to a GUI that allows them to see what Meta Data  116  has been added  503 , and add additional Meta Data  116  manually to the Asset  111 ,  504 . Finally, the Advertiser  108  can approve the Asset  111  with its associated Meta Data  116 . Once approved  505 , the data is moved into the Ad Warehouse  103 . 
     In furtherance of the foregoing, the system could use machine learning to add meta data. For example, the process could use image recognition technology to infer attributes of the image (e.g., color, text, shapes, of an ad) and map these image attributes to human produced Meta Data. Over a large number of samples, this could reinforce the mapping of attributes to automatically infer human produced meta data. Modifying the meta-data enrichment process by vetting automatically-generated Meta Data instead of tagging could speed up the Meta Data Enrichment Process. 
       FIG. 6  depicts a sample process  600  by which an ad would be selected to display to a particular user. In this example, the Ad Network or Publisher  601  makes a system call to Consumer Controlled Advertising API  602 . The call will contain Publisher Context  113  including, but not limited to, User ID, Publisher ID, and keywords that describe the context of the property that the ad will be placed within. The Consumer Controlled Advertising API  602  passes the information to the Consumer Controlled Advertising Engine  603 . The Consumer Controlled Advertising Engine  603  matches user preferences with the appropriate ads in the Ad Warehouse  604 . The Consumer Controlled Advertising Engine  603  imports Preferences  110  from the Preference Center  605  that match the User ID. The Consumer Controlled Advertising Engine  603  imports Ads from the Ad Warehouse  604  that contain Meta Data  116  that matches the Preferences  110  and the Publisher Context  113 . The Consumer Controlled Advertising Engine  603  then processes the data to determine which ad is to be returned back to the Ad Network or Publisher  601 . 
       FIGS. 7 through 11  illustrate select portions of a possible digital and web user experience to illustrate an example of the interface and user experience produced by the technological ecosystem, the consumer driving the experience and the matching of ads to individuals. The consumer is guided through an experience that takes a social/psychological journey to ascertain areas of interest to them from the perspectives and ranking of needs, wants, desires and wishes as defined by the consumer. Then the consumer enjoys and is informed by advertisements across some or all of their personal and social digital devices and networks. The consumer&#39;s profile is secured, in sync with other devices and digital content and communications. Digital advertisements include, but are not limited to, web advertising, TV advertising, mobile advertising, tablets and other electronic advertising platforms. 
       FIG. 7  depicts an exemplar graphical user interface of a user dashboard of needs, wants, wishes and desires  700  following the preference process described in connection with  FIGS. 4 and 4   a . An example category box  701  is for “needs,” and includes an image for an insurance service sought by the user. From this screen, the user may navigate back to the preference selection screen as described by reference to  FIGS. 4 and 4A . 
       FIG. 8  is an exemplar graphical user interface  800  depicting the categories and subcategories of a user&#39;s defined “needs. ” The user could then click on a category button  801  and further edit sub-categories of needs such as “grocery”  802 . 
       FIG. 9  is an exemplar graphical user interface  900  depicting the user&#39;s categories of interest, such as “Dining,”  901  and exemplar advertisements  902 ,  903  generated by such user choices. In this way, a user can receive visual feedback and information on any match or mismatch between personal preferences and advertisements and make corrections as necessary. 
       FIG. 10  depicts an exemplar graphical user interface  1000  of a web service, such as AOL, in which an advertisement  1001  is presented to a user. In this case,  FIG. 10  depicts an advertisement  1001  for a truck rental service, which a particular user may or may not be interested in.  FIG. 11  shows the identical generated page  1100  but with a depicting a cruise ship offering  1101 , which, in this example, reflects a user&#39;s preferred interest in travel advertisements. Use of the systems described herein allows for the presentation of such preferred advertisements. 
     The disclosed system involves technology that uses a computing system. The system includes at least one computing device. In some embodiments the computing system further includes a communication network and one or more additional computing devices (such as a server). 
     The computing device can be, for example, located in the office of an advertiser or publisher or any other place of business or can be a computing device located in a consumer&#39;s or user&#39;s home. The computing device can be a stand-alone computing device or a networked computing device that communicates with one or more other computing devices across a network. The additional computing device(s) can be, for example, located remotely from the first computing device, but configured for data communication with the first computing device across a network. 
     In some examples, the computing devices include at least one processor or processing unit and system memory. Depending on the exact configuration and type of computing device, the system memory may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. System memory typically includes an operating system suitable for controlling the operation of the computing device, such as the WINDOWS® operating systems from Microsoft Corporation of Redmond, Wash. or a server, such as Windows SharePoint Server, also from Microsoft Corporation. The system memory may also include one or more software applications and may include program data. 
     The computing device may have additional features or functionality. For example, the device may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory, removable storage, and non-removable storage are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device. An example of computer storage media is non-transitory media. 
     In some examples, one or more of the computing devices can be located in an establishment, such as an office or place of business. In other examples, the computing device can be a personal computing device that is networked to allow the user to access the system disclosed herein from a remote location, such as in a user&#39;s home or other location. In some embodiments, the computing device is a smart phone or other mobile device. In some embodiments the application is stored as data instructions for a smart phone application. A network facilitates communication between the computing device and one or more servers, such as an additional computing device, that host the application. The network may be a wide variety of different types of electronic communication networks. For example, the network may be a wide-area network, such as the Internet, a local-area network, a metropolitan-area network, or another type of electronic communication network. The network may include wired and/or wireless data links. A variety of communications protocols may be used in the network including, but not limited to, Ethernet, Transport Control Protocol (TCP), Internet Protocol (IP), Hypertext Transfer Protocol (HTTP), SOAP, remote procedure call protocols, and/or other types of communications protocols. 
     In some examples, the additional computing device is a Web server. In this example, the first computing device includes a Web browser that communicates with the Web server to request and retrieve data. The data is then displayed to the user, such as by using a Web browser software application. In some embodiments, the various operations, methods, and rules disclosed herein are implemented by instructions stored in memory. When the instructions are executed by the processor of one or more of the computing devices, the instructions cause the processor to perform one or more of the operations or methods disclosed herein. Examples of operations include submitting and modifying User Preferences and identifying Advertisements to be published, among other operations and functions.