USE OF PERSONAL PREFERENCES TO CONTROL PRESENTATION OF ADVERTISEMENTS

This disclosure relates to a software-based system and method for management and display of advertisements based on personal preference information.

DETAILED DESCRIPTION

FIG. 1is a block diagram illustrating an example system100. As illustrated in the example ofFIG. 1, the system100comprises a set of a Preference Center101, Meta Data Enrichment102, Ad Warehouse103, Consumer Controlled Advertising API104, Business Intelligence105, End User Devices106, Ad Networks or Publishers107, Advertisers108, 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 Network115. Preference Center101, Meta Data Enrichment102, Ad Warehouse103, Consumer Controlled Advertising API104, Business Intelligence105, End User Devices106, and Ad Networks or Publishers107are computing systems.

The Network115facilitates communication among the Preference Center101, Meta Data Enrichment102, Ad Warehouse103, Consumer Controlled Advertising API104, Business Intelligence105, End User Devices106, Ad Networks or Publishers107, and Advertisers108. In various embodiments, the Network115can be various types of networks. For example, the Network115can be a wide area network, such as the Internet. In another example, the Network115can be a local area network, a virtual private network, or another type of communications network. The Network115can include wired and/or wireless communication links.

The Preference Center101is a system of computing devices that contains a collection of User Preferences110for each user of the system. User Preferences110might include, but are not limited to, personal needs, wants, wishes and desires for each content category in the system. Users can create accounts, and submit and modify their User Preferences110through a user interface over the Network115.

The Meta Data Enrichment102is a system of computing devices that allows Advertisers108to submit combinations of Advertisements112and Meta Data116. Advertisers108submit the information through a user interface over the Network115. The Meta Data Enrichment102facilitates the input by detecting key attributes of the Advertisements112and repurposing previously entered information to help Advertisers108complete the combinations of Advertisements112and Meta Data116.

The Ad Warehouse103is a system of computing devices that stores combinations of Advertisements112and Meta Data116as Advertisements112.

The Consumer Controlled Advertising API104is a system of computing devices that determines which Advertisements112should be presented to a User ID109, considering User Preferences110and Publisher Context113.

The Business Intelligence105is a system of computing devices that collects and organizes Reporting Data114and presents it to Advertisers108for decision-making purposes.

As illustrated in the example ofFIG. 1, the system100also comprises a set of Users117. The Users117use the End User Devices106to access the Preference Center101. The End User Devices106can be a variety of different types of computing devices. For example, the End User Devices106can 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 Devices106can 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 Publishers107is a system of computing devices that publish Advertisements112to Users117on 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's, aggregators, etc.; Publishers107can 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 Advertisers108are users that create Advertisements112that are intended to be published to Users117. Advertisers108can 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's, Costco, Dell, Coca Cola, and Bank of America, among others.

Users117interact with the Preference Center101by submitting and modifying User Preferences110. The User Preferences110could include various products or services the User117needs, wants, wishes, and/or desires. Furthermore, User Preferences110could 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 Advertisers108through their respective line of business.

Depending on user choice during interaction with the Preference Center101, users' specificity and sensitivity to the choices is known and can be utilized to present the user with ads. For example, if user says Want=Sedan and Brand=Toyota, Honda, Infinity . . . etc., the user 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 “customers have's in past,” a customers 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 “had” or made in the past.

Advertisers108currently create Advertisements112and route them to Ad Networks or Publishers107to be published to Users117. Ad Networks or Publishers107typically choose which Advertisements112to publish to a User117based on previous behavior of the User117and/or Publisher Context113. Publisher Context113typically includes keywords on the publisher property.

In System100, Advertisers108will route Advertisements112to Meta Data Enrichment102. They will also add Meta Data116for each of the Advertisements112. Meta Data Enrichment102will also facilitate the addition of Meta Data116in various ways, including keywords and attachment phrases describing the content and purpose of the advertisement. Meta Data116includes, 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' psychological needs and wants.

Meta Data Enrichment102submits the Advertisements112to the Ad Warehouse103to be stored, and to be referenced by the Consumer Controlled Advertising API104. Advertisements112are stored in a structured format, so that User Preferences110and Publisher Context113can be used as search parameters to find relevant Advertisements112in the Ad Warehouse103by the Consumer Controlled Advertising API104.

Ad Networks or Publishers107submit a request to Consumer Controlled Advertising API104each time a User117visits their property. If the User117is anonymous and cannot be identified, the request contains Publisher Context113. If the User117can be identified, the request contains Publisher Context113and User ID109.

The Consumer Controlled Advertising API104uses the Publisher Context113and, if available, the User ID109, and references the Preference Center101and the Ad Warehouse103to identify the best Advertisements112to be published. The Consumer Controlled Advertising API104considers several factors when providing a recommendation back to the Ad Networks or Publishers107, including but not limited to Publisher Context113and User Preferences110.

FIG. 2is a flow chart illustrating where and how users201will interact with the consumer front-end of the experience controlling their advertisements200, including but not limited to a preference center interface203viewed through web sites, Digital TV apps, mobile and tablet applications, personal computer and web-based applications202. As Users share timely and relevant information about themselves204, their purchase behaviors, and their product and service needs, wants, desires and wishes209, as well as likes and dislikes210, they will be directed through a psychological, intuitive experience to assist in selecting categories205and subcategories206and brands of interest to them and enable timeliness by including personal calendar planning207and location relevance by including location preference information208. A user ID is then issued for matching as illustrated inFIG. 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. 3is a flowchart illustrating an example operation300performed by Consumer Controlled Advertising API104. As illustrated in the example ofFIG. 3, the operation300begins when the Ad Network or Publisher107submits a query301to Consumer Controlled Advertising API104containing Publisher Context113and User ID109, if available,302. The system then retrieves advertisements from the Ad Warehouse303, calculates an optimal advertisement to be published304and then returns advertisements to the Ad Network or Publisher305.

As discussed above, Publisher Context113describes the property that the Advertisement112will be displayed on. For example, the Publisher Context113can 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 API104uses the Publisher Context113and the User ID109to find relevant Advertisements112that contain Meta Data116in the Ad Warehouse103that match the Publisher Context113and User ID109.

In other embodiments, either the Publisher Context113or the User ID109is not available. In these cases, the Consumer Controlled Advertising API104uses any information that is submitted to find relevant Advertisements112in the Ad Warehouse103.

To find relevant Advertisements112in the Ad Warehouse103based on Publisher Context113and User ID109, the Consumer Controlled Advertising API104will 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 4adescribe an example graphical user interface, or GUI400, for the Preference Center101. In this example, the user is presented with their existing preferred categories401. The user can interact with any one of the Needs, Wants, Wishes, or Desires buttons402,403to configure their preferences for that particular preference type. For example, if the user clicks Needs402, then the category tree404appears. 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. 5shows the Meta Data Enrichment102process. This process500is used to attach Meta Data116to an Asset111(such as an ad). This Meta Data is later evaluated and used by the system to provide recommendations to Ad Networks and Publishers107. When an Asset111is submitted to Meta Data Enrichment102by an Advertiser108,501, the Advertiser108can include Meta Data116with the Asset111in the electronic submission502. Additionally, the Advertiser108will gain access to a GUI that allows them to see what Meta Data116has been added503, and add additional Meta Data116manually to the Asset111,504. Finally, the Advertiser108can approve the Asset111with its associated Meta Data116. Once approved505, the data is moved into the Ad Warehouse103.

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. 6depicts a sample process600by which an ad would be selected to display to a particular user. In this example, the Ad Network or Publisher601makes a system call to Consumer Controlled Advertising API602. The call will contain Publisher Context113including, 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 API602passes the information to the Consumer Controlled Advertising Engine603. The Consumer Controlled Advertising Engine603matches user preferences with the appropriate ads in the Ad Warehouse604. The Consumer Controlled Advertising Engine603imports Preferences110from the Preference Center605that match the User ID. The Consumer Controlled Advertising Engine603imports Ads from the Ad Warehouse604that contain Meta Data116that matches the Preferences110and the Publisher Context113. The Consumer Controlled Advertising Engine603then processes the data to determine which ad is to be returned back to the Ad Network or Publisher601.

FIGS. 7 through 11illustrate 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'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. 7depicts an exemplar graphical user interface of a user dashboard of needs, wants, wishes and desires700following the preference process described in connection withFIGS. 4 and 4a. An example category box701is 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 toFIGS. 4 and 4A.

FIG. 8is an exemplar graphical user interface800depicting the categories and subcategories of a user's defined “needs. ” The user could then click on a category button801and further edit sub-categories of needs such as “grocery”802.

FIG. 9is an exemplar graphical user interface900depicting the user's categories of interest, such as “Dining,”901and exemplar advertisements902,903generated 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.

FIGS. 10depicts an exemplar graphical user interface1000of a web service, such as AOL, in which an advertisement1001is presented to a user. In this case,FIG. 10depicts an advertisement1001for a truck rental service, which a particular user may or may not be interested in.FIG. 11shows the identical generated page1100but with a depicting a cruise ship offering1101, which, in this example, reflects a user'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's or user'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'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.