WO2014043804A1 - Système et procédé de génération d'un profil d'utilisateur - Google Patents

Système et procédé de génération d'un profil d'utilisateur Download PDF

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Publication number
WO2014043804A1
WO2014043804A1 PCT/CA2013/050710 CA2013050710W WO2014043804A1 WO 2014043804 A1 WO2014043804 A1 WO 2014043804A1 CA 2013050710 W CA2013050710 W CA 2013050710W WO 2014043804 A1 WO2014043804 A1 WO 2014043804A1
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WIPO (PCT)
Prior art keywords
user
users
predictions
social
questions
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PCT/CA2013/050710
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English (en)
Inventor
Robert HALPERN
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Halpern Robert
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Publication of WO2014043804A1 publication Critical patent/WO2014043804A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations
    • H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
    • H04L12/1859—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast adapted to provide push services, e.g. data channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/21—Monitoring or handling of messages
    • H04L51/214—Monitoring or handling of messages using selective forwarding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services

Definitions

  • Online advertising targeting generally relies on tracking user behavior, such as by profiling a user based on tracking the websites the user has visited. Due to the ever increasing popularity of social networking, there is now an additional layer of online advertising targeting whereby one user is sent advertisements based on behaviour of a socially connected other user. For example, users are alerted if a friend "likes" a retailer. Additionally, some advertisers simply pay celebrities and other influential endorsers to disseminate messages to socially connected users on their behalf.
  • a method for generating a set of user profiles corresponding to a plurality of users of a social network comprising: (a) obtaining from said social network a set of social relationships for each said user, each said social relationship being to another of said users; (b) providing to each said user a plurality of questions, said plurality of questions relating to said user and at least a subset of said other users; (c) generating, by one or more processors, user attributes for each said user based on responses provided by said user and said other users in relation to questions relating to said user; and (d) generating a user profile for said user from said user attributes.
  • a system for generating a set of user profiles corresponding to a plurality of users of a social network comprising: (a) a server linked to said social network, said server operable to obtain from said social network a set of social relationships for each said user, each said social relationship being to another of said users; (b) a campaign database configured with a plurality of questions, said server operable to provide to each said user said plurality of questions, said plurality of questions relating to said user and at least a subset of said other users; (c) an analytic module operable to generate user attributes for each said user based on responses provided by said user and said other users in relation to questions relating to said user and to generate a user profile for said user from said user attributes; and (d) a user database configured to store said user profiles.
  • a system for generating a user profile comprising an analytic module operable to generate a weighted score comprising a user's relationships, knowledge, preferences, and perspectives.
  • a system for generating a user profile comprising a gaming module operable to obtain from a user a plurality of responses indicative of the user's relationships, knowledge, preferences and perspectives.
  • a system for generating a user profile for a first user comprising an analytic module operable to generate a score established at least partly on the basis of a second user's guess of a preference of the first user.
  • Methods of generating a user profile are further provided herein.
  • FIG. 1 is a block diagram of a system
  • FIG. 2 is a user interface for selecting a campaign
  • FIG. 3 is a user interface for selecting a level of a game within campaign
  • FIG. 4 is a user interface for making picks
  • FIG. 5 is a user interface for making predictions
  • FIG. 6 is a flowchart for two users making picks and making predictions
  • FIGS. 7 to 12 are exemplary user interfaces of a gaming module
  • FIG. 15 is an illustration of factors contributing to a measure of social influence.
  • FIGS. 16 to 32 are exemplary user interfaces of a specific mobile application embodiment for making picks and predictions.
  • any module, unit, component, server, computer, terminal or device exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or 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 non-volatile, 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.
  • Examples of computer storage media include 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 an application, module, or both. Any such computer storage media may be part of the device or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media.
  • Social relationships may be indicative of user attributes comprising knowledge, relationships and preferences. Relationships may further indicate influence, and preferences may further include perspectives.
  • One-way relationships may be of different value to advertisers than two-way relationships and mutual relationships (i.e., second or higher degree relationships connected through one or more friends, including based on the number of such indirect connections) and, further, the quality of the relationships may be highly relevant.
  • Particular advertisers may value particular preferences and knowledge differently from others. It has further been recognized that advertisers may benefit by targeting advertising and rewards at individuals that exert influence over others, are knowledgeable in a particular field and/or are hyper-connected by having a high number of relationships.
  • a system and method for generating a user profile comprises a plurality of user attributes, comprising, for example, user preferences, relationships, influence, knowledge, and perspectives.
  • Advertising campaigns can be targeted to users based on user profiles in order for the advertiser to optimally spread the campaign.
  • the generated user profiles may enable the advertiser to better understand the marketplace (by customer preferences), determine which users are knowledgeable about the marketplace and the advertiser's products or determine which users are not knowledgeable and may be potential customers.
  • the user profiles can be used to generate goodwill through
  • a system and method for obtaining information to generate a user profile is provided.
  • a system and method for advertising targeting is provided.
  • a viral subscription process is provided.
  • a server 100 comprises an analytic module 101 and is linked to a user database 102 and a campaign database 104.
  • the server 100 is linked by a network 106, such as the Internet, to one or more social networks 108.
  • a social network may not be a strict social network in the commercial sense, so long as a plurality of even unrelated user identities can be obtained and relationship information can be generated by the server in accordance with the following.
  • a user may access the server 100 via a network connected client device 110.
  • the client device 110 is a computing device that may be embodied by a computer, laptop, tablet, smartphone or other device.
  • the client device 1 10 is a smartphone with a location module 1 12, such as a GPS module.
  • the client device 1 10 generally comprises a processor 1 14, memory 116, I/O apparatus 1 18 and network module 120 wherein the network module 120 links the client device to the network 106.
  • the memory 1 16 has stored thereon computer instructions implementing a gaming module that, when executed by the processor 114, presents an interactive game to the user via the I/O apparatus 1 18.
  • the gaming module is in communication with the server 100 which provides the gaming module with a plurality of questions that are configured as a campaign in the campaign database 104.
  • the client device 1 10 presents the questions to the user and obtains corresponding responses from the user via its I/O apparatus 118.
  • the gaming module forwards the responses to the server, which records the responses in the user's profile in the user database 102.
  • the server 100 and client device 1 10 are typically in communication over the network 106. However, since the network connection may be interrupted or unavailable from time to time, the server 100 and client device 1 10 may each comprise a synchronization module 122, 124.
  • the synchronization module enables data to be cached on the memory 116 of the client device 110 to enable the functionality of the client device, as described herein, to continue despite loss of network connectivity.
  • the server 100 and client device 1 10 may each comprise a synchronization module 122, 124.
  • the synchronization module enables data to be cached on the memory 116 of the client device 110 to enable the functionality of the client device, as described herein, to continue despite loss of network connectivity.
  • synchronization modules 122, 124 synchronize data stored on the cache of the client device 1 10 to the server 100.
  • the synchronization modules may implement a known synchronization process.
  • Advertisers may access the server 100 via a network connected administrator device 125.
  • the administrator device 125 is a computing device that is preferably a computer comprising a processor 126, memory 128, I/O apparatus 130 and network module 132 wherein the network module 132 links the administrator device to the network 106.
  • the memory 128 has stored thereon computer instructions implementing an advertiser portal that, when executed by the processor 126, enables the advertiser to configure a campaign.
  • Advertisers may disseminate advertisements in accordance with a campaign. Advertisements may be messages, notifications, publications, coupons, prizes or links to other campaigns. Advertisements can be disseminated to users based on score, reach, location and any combination of one or more user attribute.
  • a "user” refers to an individual that has configured a user profile on the user profile database; and a non-user refers to an individual that has not configured a user profile on the user profile database.
  • “Reach” refers to a virtual currency that is awarded to users based on incoming and outgoing activity.
  • Relationship is measured by two users' various types of activities or functions performed in relation to each other in the application.
  • “Knowledge” refers to a measure of the accuracy of a user's predictions of another user's preferences.
  • Preference refers to a measure of a user's answers to questions (picks), both individually and in aggregate.
  • Perspective refers to measure of a user's perspective of self and socially related users relative to a norm, which may be measured by differentially comparing a user's preferences to that user's predictions of the preferences and predictions of a majority of users, explained more fully below.
  • the server 100 is operable to associate each user profile with at least one social networking profile on the one or more social networks 108.
  • the server 100 is operable to obtain relationship information (e.g. a list of the user's friends, followers or those the user is following) from the social networks 108.
  • relationship information e.g. a list of the user's friends, followers or those the user is following
  • Such functionality may, for example, be provided via an application programming interface (API) provided by each of the social networks 108.
  • API application programming interface
  • An advertiser accesses the advertiser portal to configure a campaign which is stored in the campaign database 104.
  • the campaign comprises campaign rules and a plurality of questions, which may be configured to be answerable by a free-form response or by one or more predefined responses.
  • An advertiser may also create and target questions based on historical data on users' responses. Predefined responses are also stored in the campaign database 104.
  • the campaign rules may be configured to disseminate rewards in accordance with a plurality of rules or conditions.
  • Campaign rules are typically configured based on an inference of particular user attributes based upon the user's response to questions.
  • the campaign rules enable an advertiser to selectively make campaigns, questions and rewards available to users that are of interest to the advertiser.
  • Particular campaigns and questions may be presented to or hidden from a user based on user profile attributes, a score, proximity to a location, relationships, knowledge, preferences, user demographics, the number of times the user has previously answered the question, and/or the number of times the user has received predictions based on others, including a rating (e.g. high or low).
  • proximity based questions and/or rewards can be made available based on the user being within or outside a predefined area, such as a shop or a city.
  • a reward may be made available to a user that has responded to a question while being within or outside a range of a particular location, such as a retail store location.
  • Each question may comprise a plurality of parameters, including, for example:
  • campanhas may further associate a difficultly rating with a campaign or question.
  • the advertiser can also establish rewards, such as a voucher for a tangible good, to be provided to users that have a particular user profile.
  • Question types may request a response to a scenario comprising, for example: free form response (e.g., a response to "What is your favorite X?”); image upload (e.g., a response to "Take a picture of your favorite X"); single image select (e.g., a response to "Which X have you seen before?”); multiple image select (e.g., "Which X's have you seen before?”); text select (e.g., "Which X best describes you?”); person select (e.g., "Which friend has X characteristic?”); single preference ordering (e.g., "Organize the following X items into an order of preference”); and full preference ordering (e.g., “Organize the following X items into groupings of ones you like and ones you do not like”).
  • free form response e.g., a response to "What is your favorite X?”
  • image upload e.g., a response to "Take a picture of your favorite X
  • Each of these question types can have a prediction variant (e.g., "Predict user Y's response to 'What is your favorite X?'"), or when receiving a prediction from other user(s), to them rate on a scale/ranking system - which can gauge both relationship and perceived expected prediction confidence based on different users who made predictions on said user, or allowing 3 rd degree users (outside of the user who made or received the prediction) to crowd-source their rankings collectively to further infer a user-profile
  • a measure of the social stance of a user can be generated based upon collecting responses and analyzing the responses to these questions from a plurality of users over time.
  • An individual launches the gaming module using the I/O apparatus.
  • the gaming module may request the user to configure a user profile including by establishing log in credentials and linking one or more social networking profiles to the user profile.
  • a logos 7000 for the social networks such as an icon for FacebookTM, TwitterTM, etc.
  • a command 7002 to register the user profile and/or connect the user profile to the social networking profiles.
  • Each social networking profile generally comprises a plurality of one-way and/or two- way social relationships with other users on the respective social network.
  • the individual may log in to the gaming module using a log in process facilitated by one or more of the social networks, such as by using an "open login" API, in which case the user profile may automatically be linked with the corresponding social networking profile.
  • the user may log in to the gaming module using a log in process previously established.
  • the gaming module may present a user with a plurality of advertisers from which they may make a selection.
  • the gaming module may determine the user's location by use of the location module 112.
  • the advertisers shown are those that have relevance in or proximate to the particular location of the user at that time.
  • Identifiers such as branding, may be presented to the user in association with each such advertiser.
  • a map 2000 may be presented to the user in association with each such advertiser.
  • a command 2002 may also be provided for enabling the user to activate or deactivate the location module 112. Additional advertisements 2004 may be displayed on the screen. The user may select a campaign for the advertiser.
  • the campaign is presented as a game by the gaming module.
  • a user is required to play the game in a number of "levels" and can pass those levels based on providing responses to questions.
  • the server 100 obtains questions and, when appropriate, predefined responses from the campaign database corresponding to the selected campaign.
  • the server 100 configures the gaming module to present the user with each question, one at a time, and request that the user enter a response.
  • the responses can be categorized into "picks” and “predictions", wherein "picks” relate to preferences of that user while “predictions” relate to guesses of other users' picks and predictions.
  • the gaming module accepts each response from the user and forwards the response to the server.
  • the server stores each response and may store the time and/or location that the pick was made in the user's profile in the user database.
  • the gaming module may configure a screen presenting a plurality of levels 3000 to the user organized as a map 3002.
  • Levels correspond to other users which the playing user is socially connected to or interested in.
  • the user can unlock additional levels by successfully completing levels.
  • Rewards can also be unlocked by completing levels or receiving a particular number of predictions from others pertaining to a campaign. Completion of levels may be based upon providing threshold amounts of questions, or picks.
  • FIG. 4 An exemplary pick interface is shown in Fig. 4 wherein the user is requested to select a particular option for a scenario.
  • An example topic may be to select or prioritize hamburger toppings.
  • a pick question may be for a scenario such as "Do you prefer X or do you prefer Y" to which the predefined responses are "X" and "Y".
  • the user's response is sent to the server which stores the response and may store the time and/or location that the pick was made in the user's profile in the user database.
  • a plurality of such questions may be provided to a user over time and the user may provide a sufficient number of responses to enable the user's profile in the user database to be sufficiently populated.
  • An advertiser can use the user profile to configure campaign rules to target the user in relevant campaigns.
  • a particular advertiser may want to disseminate an advertisement (or question or reward) to all users that prefer "X" but not to any users that prefer ⁇ ", and the server can consequently disseminate the advertisement to those users.
  • the targeting, by campaign rules can be as complex as enabled by the depth of the user profiles that are generated over time.
  • the fact the user selected the campaign and responded to a question may be indicative that the user has an interest in the subject of the campaign and/or based on predictions received.
  • skipping a question may indicate reduction of interest in a campaign. Therefore, if the campaign is directed to a product, for example, it can be inferred that the user is interested in the product or the field of the product.
  • the server 100 may obtain from the one or more social networks a set of social relationships for the user.
  • An exemplary prediction interface is shown in Fig. 5 wherein the user is requested to place predictions on what another user would select in a scenario. For example, what would a socially connected friend select as a hamburger topping.
  • the server may present prediction questions to a user that is interacting with the gaming module.
  • prediction questions may relate to scenarios of another user that is related socially to the interacting user.
  • an interacting user A is related to an individual B (who may or may not be a user) and may be asked "Would B prefer X or would B prefer Y" to which the gaming module may accept a user response "X" or ⁇ ".
  • user A is not providing direct information about her own preferences, but rather information about her guess of B's preferences. Effectively, user A is making a prediction on her knowledge of B. User A may subsequently make a pick to validate user B's prediction of A, which helps measure whether B knows A, that A has a 2-way relationship with B. User A may also be presented to rank or rate what they think User B's prediction of User A was, to gauge whether B should or should not know the answer (based on relationship knowledge expected value)
  • the fact that user A was willing to provide an opinion of B's preference may be indicative that user A knows B relatively well; that B is popular; and that user A does or does not believe she is similar to B (for example, if user A would pick X and also predicts B would pick X, user A may believe she is similar to B).
  • a has an interest in the subject of the question e.g., a product
  • the analytic module collects data to generate user profiles by obtaining third party information relating to an individual through predictions by that individual's socially connected friends.
  • a particular game may require a certain number of correct predictions by a user in order to advance. The requirement may be configured to progress dynamically based on the number of individuals for whom a particular user is able to make predictions. For these games, the available individuals are those who have answered the questions a participating user wants to make questions on.
  • the required number grows linearly by 5 correct predictions with each additional friend who has played, such that with the first friend the number of required correct predictions is 5, with the second friend 10 (being 5 from friend 1 and 5 from friend 2), with the third friend being 15 (3 friends x 3 correct predictions each), and with the fourth friend being 20, the total data points generated by 5 friends who have made predictions on each other is 250, which is 10 times the former case of simply requiring a total of 5 correct predictions, regardless of whether there was 1 or an infinite number for which to make prediction.
  • a prediction question can be about an interacting user but through the lens of another socially related user.
  • interacting user A is related to individual B (who may or may not have ever interacted with the gaming module) and may be asked a scenario of A from the lens of B, such as "Would B believe that you (user A) prefer X or would B believe that you (user A) prefer Y" to which the gaming module may accept a response "X" or ⁇ ".
  • user A is not providing information about her own preferences, but rather information about A's guess of B's prediction of A's preferences.
  • the fact that user A entered a response may be indicative that user A would believe B would know user A relatively well, and therefore that A may be popular; and that user A does or does not believe she is similar to B; that A and B may each have an interest in the subject of the product; and likely that A and B are socially connected by a two-way relationship.
  • a particular user that commonly responds correctly to questions with a high difficulty rating can be considered particular knowledgeable in the field of the question.
  • the server 100 may obtain from the one or more social networks a set of social relationships for the user.
  • An exemplary prediction interface is shown in Fig. X wherein the user is requested to place predictions on what the majority of other users picked for that question.
  • the server may present prediction questions to a user that is interacting with the gaming module.
  • an interacting user A is related to the all of users who have indicated their preferences through picking responses and user A may be asked "What did the majority pick?" to which the gaming module may accept a user response "X" or "Y".
  • user A is not providing direct information about her own preferences, but rather information about her guess of the majority's preferences. Effectively, user A is making a prediction on her knowledge of the picks of the majority.
  • third party information i.e. predictions
  • user A's prediction of what the majority picked when compared to that user's own picks as well as the picks of socially related users may be indicative of the user's perspective on where that user stands with relation to the majority or "the norm".
  • the fact that user A was willing to provide an opinion of the majority's preference may be indicative that user A is confident of knowing the majority relatively well; if accuracy of such predictions is demonstrated, it may be inferred that user A is influential and has high social knowledge on the basis of knowing the majority's preferences.
  • the strength of such an indication may increase as the number of predictions A places upon B accumulate over time.
  • a prediction question can involve an interacting user predicting what the majority predicted about another user's pick.
  • interacting user A is related to the entirety of the pool of users who have made predictions about particular users' preferences and may be asked "What did the majority predict user A would pick?" to which the gaming module may accept a response "X" or ⁇ ".
  • user A is not providing information about her own preferences, but rather information about A's guess of the pool's collective prediction of A's preferences.
  • advertisements are disseminated to users based on their respective user profile attributes. For example, if a plurality of users make predictions for individual B (whether or not B is a user or non-user) for questions related to a particular topic (e.g., sports or travel), then upon B launching the gaming module he will preferably be presented with an
  • advertisement related to that particular topic Therefore, user profiles can be generated and advertisement can be disseminated based on socially inferred knowledge, knowledge derived from the predictions of socially related users on each other.
  • advertisements can be targeted to users based on the time of day, or day of week, that they most commonly interact with the gaming module. For example, a user that primarily interacts with the gaming module late at night is likely to be an individual that frequents nightclubs and the like. Consequently, advertisements related to parties, alcohol and music may be targeted to the user.
  • advertisements can be targeted to users based on user profile attributes common among the user's socially related friends, rather than merely attributes directly of that user.
  • the gaming module may enable users to create questions.
  • Content from the questions can be used to infer user profile attributes. For example, if a user creates a question about sports, it can be inferred that the user is interested in sports. Furthermore, other users responding to the question may imply a relationship between the users.
  • User created questions may be linked to campaigns and advertisers, for example using social network tagging, further enabling an advertiser to target the creating user.
  • the gaming module may ask the user (user A) a plurality of questions.
  • the first question may be directed to a pick of the user's preference for a particular product or service, such as "Do you prefer X or Y" to which the user may respond at block 204 with "X” or "Y”.
  • the second question may be directed to the user's guess of another individual's prediction of the user's preference, such as "Would B predict you prefer X or Y" to which the user may respond at block 208 with "X” or ⁇ ".
  • the third question may be directed to the user's prediction of another individual's preference, such as "Would B prefer X or Y" to which the user may respond at block 212 with "X" or ⁇ ".
  • each pick and prediction is stored in user A's profile and user B's profile in the user database along with the time and/or location that the pick was made.
  • the server may send B a notification that a user (i.e., user A) has involved them using the gaming module. If B is a user, he may log in to and interact with the gaming module. Otherwise, B may create a user profile and then log in to and interact with the gaming module.
  • B has logged in to the gaming module.
  • the gaming module may present user B with the same three questions, addressed to user B this time, as were posed to user A.
  • user B answers the three questions at blocks 222, 226 and 230, respectively, at block 232, user B's responses are sent to the server and stored in user B's profile along with the time and/or location that the pick was made.
  • the server is then able to validate the knowledge and preferences of user A and user B in the product (or service) and of each other.
  • both user A and user B provide valuable information relating to their preferences as between "X" and ⁇ ". Furthermore, the preference of each user, in response to the first question, can be used to validate the other user's response to the third question (each user's prediction of the other's preference), which can be indicative that the predicting user is knowledgeable about the other user.
  • the analytic module may infer information about the users. For example, when user B completes the second question and enters his guess of user A's prediction of user B's preference, the analytic module can validate the response with reference to user A's third question to determine whether user B knows user A relatively well. Similarly, the reverse is available once user B responds to his third question.
  • the analytic module may infer that user A does not believe user B would bother to enter a selection, or that such selection is irrelevant, which may be indicative that user B does not know user A well, even though user A knows user B well.
  • the analytic module may infer that user A does not know user B well.
  • picks and predictions may illustrate other behavioral attributes of a user. For example, if user A has social relationships with a plurality of other users and frequently believes these other users do (or do not) know what user A would pick, inferences can be made about social status and knowledge of user A. Moreover, it is possible to make inferences about non-registered users. If, for example, users A, B, and C make predictions that user D, who is not registered, likes X, it may be inferred that user D does in fact like X.
  • predictions made by user A upon B are stored in B's user profile even if B is not yet a user. Since there may be several users (i.e., user A and others) making predictions on B, and B may register to become a user based upon a particular one of those predictions, it can be inferred that the user who placed that particular prediction has some influence, or is closely related socially, to user B.
  • the analytic module is, therefore, able to generate the following information from two or more users responding to, or skipping, the three questions: the preferences of the users in respect of the product or service and whether or not they have knowledge about the product or service or field thereof, the knowledge of each user in the other users, the likelihood there is a one-way or two-way relationship between the users and the degree of similarity between the users (based on similarity in their preferences and relationships, for example).
  • a user that rarely interacts with the gaming module is relatively socially inactive.
  • An advertiser may wish to target campaigns to the more active user.
  • the gaming module has a user interface configured to display advertisements to a user.
  • the gaming module initiates a "pick flow" for the user.
  • the server obtains for the user all unsettled predictions (predictions for the user made by other users for questions the user has not yet made a corresponding pick) and orders them in a relationship queue, described more fully below.
  • the gaming module determines whether or not an advertisement is displayed and consequently whether to display an advertisement.
  • the user is presented with a first pick question.
  • the gaming module determines whether the user has made the pick and, if not, the user makes a pick at block 370.
  • the gaming module determines whether the user is permitted to make a prediction (which may be withheld based on campaign rules and/or reach) and, if so, the gaming module moves to "prediction flow”. Otherwise, the gaming module determines at block 395 whether to display an advertisement or provide a reward to the user. If so, it is shown. The user is then shown another pick question in the "pick flow".
  • the "prediction flow” begins at block 410 by providing a user with a prediction question which, may, for example, be either of the four types of prediction questions described earlier.
  • the server determines whether or not the predicting user has made a pick for the particular question, which may be a prerequisite to making any predictions for that question. If the user has made a pick, the server configures a relationship queue for presentation to the user.
  • the server determines the prediction the user made and whether the user correctly responded to the question. If the user is incorrect, at block 480 they may be provided with a predetermined number of additional chances to make the correct response.
  • the server determines whether or not the individual is a user and, if not, at block 420A provides the user with an invitation or other process by which to become a user. The server then places preconfigured responses into an order based on relationship targeting at block 420B and enables the user to make a prediction. The server may then display an advertisement or reward to the user at block 430. The user can then move on to the next prediction. Alternatively, the user may be able to view predictions without making picks by performing an alternate action, such as posting a particular message on a social network.
  • the analytic module may establish, for a particular campaign, a score as one user profile attribute.
  • the analytic module may configure score rules to determine a user's score.
  • Score rules may comprise parameters related to the activity of the user and the user's profile, including any of one or more factors comprising: responding to particular questions of the campaign, responding to particular types of question, the number of social relationships for the user, the number of one-way incoming social relationships for the user, the number of one-way outgoing social relationships for the user, the number of two-way social relationships for the user, number of correct predictions made, number of predictions made, number of predictions received, number of correct predictions received, number of "likes" for a pick, number of times another user has made a predicted for the user.
  • Score rules may further comprise correctness of responses as validated by the server, as explained earlier.
  • a user's score is preferably a weighted determination of the user's relationships, knowledge, preferences, and perspectives. Relationships measure a user's prediction frequency; how often a user engages others, and based on different interests/categories. There can be one-way relationships wherein user A predicts a preference for a user that does not play back; or two-way relationships wherein user A predicts a preference for a user that does play back, showing a level of mutual relevancy. A user's influence may be a subset of relationship status.
  • Influence increases with the following factors: the user has many two-way relationships; the user has fewer outgoing than incoming one-way relationships; other users make predictions for the user more than he predicts for others; other users make predictions for the user more than upon more the average user; the user is often the subject of a response; or the user follows another user that falls into the foregoing categories.
  • Knowledge measures prediction accuracy; how often a user knows what others have picked and predicted for them. Certain picks may be considered exceptions and deemed irrelevant if they have no true relevance into knowledge. Knowledge may be measured from prediction questions and not pick questions. User A can either have a low or high level of knowledge of user B. Knowledge can be measured based on percentage of correct predictions and/or net number of correct predictions.
  • Preferences of a user are inferred on the basis of that user's picks, which can in turn be used to measure pick matches.
  • Pick matches are determined on the basis of how often users share the same preferences and tastes based on their pick responses.
  • the score rules are preferably configured in view of the following factors: if two users select each other more often than other pairs of users, it may be indicative that they have a strong relationship (e.g., friends or family); if a user predicts another user's preference incorrectly, it may be indicative that the predicting user has low self-confidence on predicting or low knowledge and thus weak relationship; if a user has a decreasing rate of predictions on another particular user, it may be further indicative that the predicting user has low self- confidence on predicting; if a user repeatedly predicts a particular way regardless of the question or the other user (e.g., always selecting "yes” rather than "no"), it may be indicative that the user is passively interacting with the gaming module. Based on such patterns, this may be indicative that the user is more or less interested in other users, resulting in a stronger or weaker relationship for ad targeting, and that the user's action, if fast, may illustrative
  • Such users that bet aggressively overall, or on specific users, may illustrate the need for more risk-seeking ad's/products such as extreme-sports, gambling, etc. rather than a user who plays slower.
  • users are incentivized to interact with the gaming module by being awarded "reach", which is a virtual currency. Reach may or may not be linked to real-world currency. Reach may be awarded based on increased interaction with the gaming module; accurate predictions; number of predictions; users configuring user profiles in response to a user's predictions, etc..
  • users may be incentivized to interact with the gaming module in order to increase a relevance quotient (RQ).
  • RQ may be a normalized score (e.g. on a scale of 100) that reflects social knowledge, the ability to know friends and to make relevant predictions about their tastes and tendencies, and having the contextual understanding of how one's knowledge of others compares to friends' knowledge of that person. A higher RQ implies higher social relevance. Higher social relevance implies more social knowledge.
  • RQ in this example, is a measure of a user's social knowledge.
  • users may wager reach and/or real-world currency with guesses in respect of other users.
  • the analytic module may make inferences from wagers. If a user A places a wager on her guess of user B's preference, it may be indicative that user A likely has high knowledge of user B. If user A places a wager on her guess of user B's guess of user A's preference, it may be indicative that user A and user B have a two-way relationship and that each is knowledgeable about the other. Furthermore, if the guess is the same response as user A's actual preference, it may be indicative that the two users share similar tastes. These inferences are reinforced if the prediction is correct but can be lessened if the prediction is incorrect.
  • the server may be configured to prevent gaming of reach and/or currency. For example, two users may wish to assist each other in accumulating a high reach by agreeing to make repeated consecutive correct predictions of one another.
  • the server may reduce the weighting of repeated predicting between two or more users to prevent such gaming.
  • the server may configure the gaming module to facilitate a viral subscription process wherein users are subscribers and non-users are targeted to become subscribers.
  • a non-user may be attracted to become a userby requesting an existing user to identify the non-user from a set of the user's social contacts and disseminating a notification to the non-user to invite the non-user to be a user.
  • the server may initiate notifications to non-users based on users' picks and predictions. For example, user A may place a plurality of picks in the gaming module.
  • the server may obtain a list of social relationships for user A and publish a notification to non-users socially related to user A inviting them to make predictions on user A. Additionally, the server may prioritize non-users when presenting prediction questions to user A. For example, user A can be asked to predict a non-user's preferences.
  • the server can then publish a notification to the non-user that user A has made the prediction and inviting the non-user to become a user, validate user A's pick and make additional predictions in relation to the new user's friends.
  • the server can notify one, all or a subset of the user's socially connected relationships. Notifications can be sent through the gaming module, via the social network or through a third party messaging platform, such as email or SMS, for example. As non-users become users and place further picks and predictions, still others will be invited.
  • a third party messaging platform such as email or SMS
  • User profile attributes can be inferred based upon how a user responds to notifications. For example, if a user A commonly ignores notifications that other users have made predictions about user A, but does respond to notifications initiated by predictions made on user A from user B, it is likely a particular relationship exists between users A and B.
  • the speed by which user A responds to the notifications initiated by user B can be used to infer relationship attributes between users A and B. Additionally, if user A responds quickly for many other users, then user profile attributes can be inferred, such as high social engagement, risky, aggressive, etc.
  • the server may further configure the gaming module to requisition picks and predictions from a particular user in an optimal sequence to increase the rate upon which the viral subscription process disseminates. For example, a particular user A may be notified of several picks and predictions made by others who are socially related. User A may launch the gaming module and navigate to an interface to settle predictions. However, the gaming module also has access to user A's social networks and can determine a certain number of user A's socially related friends are non-users.
  • the gaming module may establish a relationship queue for user A.
  • the relationship queue is a queue of the user's friends comprising other users as well as non-users.
  • the queue can be presented to the user to settle predictions for users and to initiate
  • notifications to non-users For example, for a particular question, the user may have ten unsettled predictions (initiated, for example, by ten other users making predictions on that user for that question). By introducing non-users into the relationship queue, the user may initiate new unsettled predictions for the non-users, resulting in the server initiating notifications to those non-users.
  • the server may establish the sequence of individuals in the relationship queue to maintain user attention. For example, there may be intrinsic value in providing immediate feedback to a user of whether or not a prediction was correct, as opposed to requiring the user to check back later. For example, if all the predictions are for non-users, there will be no immediate feedback. In a particular example, a user may have twenty friends, ten of whom are users and ten of whom are non-users. Of the users, five may have made predictions on a particular question while five have not. In this example, the server may establish the relationship queue to distribute the users that have made the predictions, so that when the user settles the predictions he will receive immediate feedback frequently. For example, the queue will comprise twenty individuals. Every odd position (1 , 3, 5, 7, 9, 1 1 , 13, 15, 17) presents a user
  • every fourth position from the second (2, 6, 10, 14, 18) presents a user who has yet to make a prediction, resulting in a notification to return to the gaming module to settle the prediction; and every fourth position from the fourth (4, 8, 12, 16) presents a non-user, resulting in a notification to establish a user profile to settle the prediction.
  • This positioning may yield a greater amount of viral dissemination and interactivity with the gaming module.
  • the ordering of individuals in a relationship queue may also be dependent upon users' locations. For example, if two users often interact with the gaming module while in close geographical proximity to one another, it is likely that each user desires instant feedback in relation to the other. Consequently, each user may be placed early in the relationship queue for the other user.
  • the ordering of questions themselves can be made based on user profile attributes. For example, a user having shown an interest in a particular topic can be presented with an ordering of questions that prioritizes questions of that topic, or that spreads questions of that topic across other questions unrelated to the topic, in order to increase user satisfaction and thereby elicit more information from the user.
  • the viral subscription process enables the server to disseminate invitations to non-users based on a plurality of person-select questions completed by a plurality of users. For example, user A may be asked "Select a friend who has X attribute" to which the gaming module presents user A with a predetermined number of options selected from users and non-users. Upon user A placing a pick, the selected individual (user or non-user) is notified that such a pick has been made, but not who made the pick. The selected individual, who is either a user or must become a user to respond, is then asked to guess who made the pick and presented with a predetermined number of options selected from users and non-users.
  • the gaming module may allow the selected user additional predetermined number of chances to guess who made the pick, resulting in the possibility of several new notifications to non-users.
  • the server may be configured to lock a game or a series of questions for a user until that user invites either registered socially related users or non- registered socially related users to that game or series of questions.
  • the requirement to invite friends in order to be able to interact with a certain component of the gaming module e.g. level of game
  • the server may be configured to incentivize users to interact with the gaming module.
  • the campaign database may be configured to provide incentives and rewards to users for placing picks and predictions.
  • Rewards may be provided to users based on any combination of one or more of: number of predictions received; number of predictions received correctly; being selected as the response of one or more other users' pick; number of correct predictions made; having similar user profile attributes as the advertiser's target consumer; being influential; being socially related to an influential user; successfully making predictions of other individuals that later become users in response to such predictions; and having a high reach.
  • the advertiser may configure joint rewards that are dependent upon the participation of two or more users. For example, a particular reward may be offered to one user that the server has determined is knowledgeable and closely socially related to another user who likely desires the reward. In this scenario, an advertiser may expect that the rewarded user would gift the reward to the other user. Such a reward scenario is particularly useful for rewarding gift cards.
  • the rewarding of joint rewards may further include the consideration of location and time.
  • an advertiser may configure a joint reward to only be rewarded to a user that is in a particular proximity of the other user and/or is interacting with the gaming module within a particular time threshold of the other user.
  • a particular example of a reward is one provided to the user upon whom the most predictions are made in a particular time period. This user is likely to be a celebrity, for example. Once the user is provided with the reward, others may access that user's reward history and view the reward. The other users may then place a purchase corresponding to the reward. For example, if the reward is a retail good, the other users can purchase the same retail good.
  • Another example of a reward is for a limited quantity reward. For example, an advertiser providing a limited quantity reward (for example, 10 cars) may wish to allocate the rewards strategically. The advertiser can configure the campaign rules to provide the rewards over a particular amount of time. The advertiser can further configure the campaign rules to provide the rewards to be spread geographically, based on the location of the users who qualify for the reward.
  • a user interface for a smartphone is presented to an individual. It is assumed that the individual has configured a user profile, is a user and has logged into the gaming module. The user is presented with a landing interface presenting the user with a plurality of options for proceeding, comprising making predictions and making picks.
  • the user may select making predictions and is presented with a prediction interface, shown in Fig. 8.
  • the prediction interface summarizes unsettled predictions initiated by the user (which would have to be settled by another user), unsettled predictions initiated by another user (which can be settled by the current user), a prediction history, advertiser campaigns from which new predictions can be made, and other users for which new predictions can be made.
  • the user may select the unsettled predictions initiated by another user and is presented with a prediction settling interface, shown in Fig. 9.
  • the prediction settling interface may present to the user a graphical depiction of the previously described relationship queue, and sequentially present the user with a question to settle each other individual's predictions (which may comprise initiating new predictions with users and non-users, as previously described).
  • the user may alternatively select advertiser campaigns or other users, which presents the user with questions in accordance with configured campaign.
  • the landing interface may further comprise a places option enabling the user to determine locations for an advertiser and/or to find campaigns related to a specific category of advertiser; a people option to make picks and making predictions on socially related users and one or more messaging options to disseminate messages on a social network, email or text message.
  • the landing interface may further comprise a reward option to explore rewards.
  • the user may select the rewards option, which displays a reward interface to the user, as shown in Fig. 10.
  • the reward interface displays to the user the rewards that the user has received and the rewards currently in progress, with a depiction of how far away the user is from achieving the reward.
  • the reward interface may display to the user information about the reward, including, if appropriate, a code (such as a barcode or QR-code) to redeem the reward.
  • the reward interface may display to the user the steps remaining to receive the reward.
  • the reward interface may further display to the user a leaderboard illustrating which users are closest to achieving the reward.
  • the reward interface may further provide users with the reward history for other users. While viewing the reward history, a user can select a particular reward and place a corresponding purchase. In this way, an advertiser can drive purchases from additional users from a reward to one or more particular users.
  • the reward interface may further display: a location- responsive map to assist the user to locate a physical location for the advertiser, which may be relative to the user's location as determined by the location module; a summary of the number or percentage of rewards remaining available; and a summary of the number of users participating in a pick.
  • a user may be presented with various statistics selected from: the number or percentage of users that have made a particular pick or prediction, participated in a campaign, are in a predetermined proximity, have earned rewards; a list of other users that relate to the user by having similar preferences, being of a particular demographic profile, being within a particular score, being socially related; most active user for the campaign and/or particular questions; and a list of others that relate to the user on the basis of the proportion of actual predictions they have gotten correct for that user relative to the number of expected correct predictions, which can be determined based on historical prediction correctness of the user or across one or more users.
  • GUIs may enable users to "like" or comment upon: another user's answer, an advertiser, a reward, a location; and provide messaging, sharing and activity feeds.
  • Fig. 16 illustrates a log in interface for the mobile application comprising an introduction screen and a social network log-in command.
  • the mobile application may obtain social relationship information for the user.
  • the mobile application presents the user with a game interface for providing responses to questions, or making picks.
  • the game interface comprises a plurality of levels wherein a user is required to "pass" a particular level to access further levels. For each level, the user is provided with one or more questions for which responses are gathered.
  • Each level may by dynamically associated with one or more other users social related to the playing user.
  • a question is presented to the user, requesting the user to select a card (response).
  • An exemplary question as shown is "Why is there so much food in the fridge” to which possible cards are "I have no clue”, "Must. Feed. kids.”, “Delivery leftovers” and "I'm a grocery nut”.
  • Responses that relate to preferences of the user are stored by the mobile application for presenting as prediction picks to other users. Conversely, the playing user may be presented with prediction picks for other users. Further, by selecting cards (making picks/responses), corresponding predictions are queued for other users. Additionally, popularity and influence can be inferred based on incoming prediction volume while confidence of pick can be inferred by prediction consistency.
  • FIG. 19 shows a prediction question in which a user is asked to predict the card selected by another user, Larissa.
  • this prediction card a user is asked to select a celebrity who they would not choose to judge a competition.
  • Information that can be inferred from the pick includes popularity of the celebrity, preference of the user (i.e., towards or away from the genre associated with the celebrity), etc.
  • Figs. 20 and 21 a question is asked to a user about their own preference.
  • a user is asked what they might smell like to others with responses of "moldy bread", “dog food”, “smoke” and "fresh air”.
  • a user is asked to select a card for another user's preferences.
  • the user is asked which card a friend, Sandra, would select regarding her behavior at a street party.
  • Example card correspond to excitement, dancing and drinking, from which a response may correspond to the friend being, in the view of the user, a happy person, drinker, etc.
  • Brand awareness can be generated by, for example, disseminating (or withholding) beer ads to the friend.
  • FIG. 23 A question relating to a majority pick is shown in Fig. 23.
  • the user is asked to select a card that would be selected by the majority of users in response to a question. Based on whether the user selects the card that indeed reflects the majority pick, knowledge attributes may be inferred upon the user.
  • association strength may be on a qualitative scale such as, for example, "Know You” (very strong), “Thinking of You” (strong), “Want to Know You” (moderate) and "Everyone Else” (weak).
  • a score can be generated for a user for a level. Following completion of the level, the user can be shown a leaderboard of other users for that level. If the user is competitive, they may wish to replay the level to achieve a higher score to outplay their friends. Correspondingly, new questions can be provided to the user in the replay.
  • brands can sponsor levels or games.
  • a frozen yogurt chain can provide a dedicated level resulting in an offer or promotion to the user.
  • the brand can create a sub-game comprising a plurality of levels, as shown in Fig. 27.
  • FIGs. 28-33 Further options for the mobile application are shown in Figs. 28-33.
  • a summary of the cards picked by others for the user can be shown on the screen.
  • the user can access a level to play for cards corresponding to the predictions of others.
  • the user is given a time limited game to pick as many cards as possible in a set time.
  • a score may be shown on the social strength chart, incenting the user to increase the score by, for example, tying in additional social networks.
  • a brand can configure a map to enable users to interact in a defined order within a game.

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Abstract

Systèmes et procédés pour générer un profil d'utilisateur. Un module de jeu permet aux utilisateurs d'effectuer des sélections et prédictions par rapport aux préférences d'utilisateur et leurs estimations d'autres préférences d'utilisateur. Les prédictions peuvent être validées. Un classement est attribué aux utilisateurs. Les annonceurs peuvent cibler les utilisateurs sur la base d'un profil d'utilisateur comprenant le classement.
PCT/CA2013/050710 2012-09-18 2013-09-18 Système et procédé de génération d'un profil d'utilisateur WO2014043804A1 (fr)

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WO2011094734A2 (fr) * 2010-02-01 2011-08-04 Jumptap, Inc. Système d'annonces publicitaires intégré
US20120072272A1 (en) * 2008-03-10 2012-03-22 Hulu Llc Method and apparatus for saving or bookmarking advertisements for later viewing
US20120239457A1 (en) * 2011-03-18 2012-09-20 Paul Janowitz Systems and methods for generating and utilizing user profiles based on prior user responses

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US20120072272A1 (en) * 2008-03-10 2012-03-22 Hulu Llc Method and apparatus for saving or bookmarking advertisements for later viewing
WO2011094734A2 (fr) * 2010-02-01 2011-08-04 Jumptap, Inc. Système d'annonces publicitaires intégré
US20120239457A1 (en) * 2011-03-18 2012-09-20 Paul Janowitz Systems and methods for generating and utilizing user profiles based on prior user responses

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JP2018521547A (ja) * 2015-11-20 2018-08-02 ▲騰▼▲訊▼科技（深▲セン▼）有限公司 情報を提示するための方法、装置、およびシステム

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