EP2751766A1 - Procédé et appareil de marketing personnalisé - Google Patents

Procédé et appareil de marketing personnalisé

Info

Publication number
EP2751766A1
EP2751766A1 EP12828256.3A EP12828256A EP2751766A1 EP 2751766 A1 EP2751766 A1 EP 2751766A1 EP 12828256 A EP12828256 A EP 12828256A EP 2751766 A1 EP2751766 A1 EP 2751766A1
Authority
EP
European Patent Office
Prior art keywords
user
offer
offers
entertainment
game
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP12828256.3A
Other languages
German (de)
English (en)
Other versions
EP2751766A4 (fr
Inventor
Robert Emrich
Samuel Goldberg
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Paedae
Original Assignee
Paedae
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Paedae filed Critical Paedae
Priority claimed from PCT/US2012/052936 external-priority patent/WO2013033266A1/fr
Publication of EP2751766A1 publication Critical patent/EP2751766A1/fr
Publication of EP2751766A4 publication Critical patent/EP2751766A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • 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
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement

Definitions

  • One model is to provide content to attract a viewer to a web site and to present advertising or present offers to the visitor.
  • Income can be generated by the number of visitors that view a particular ad.
  • internet ads and offers are clickable in that a user can click on the ad or offer to reach a site provided by the advertiser where product purchase can be completed, or at least additional information can be provided about the product or products being advertised.
  • An internet user who "clicks through" on an ad link is more valuable than a user who merely views a web page that includes advertising. Therefore, internet advertisers are interested in ways to encourage a user to click on an ad or more actively engage with internet advertising.
  • One current method of increasing the engagement of a user with a website is to provide compelling entertainment or compelling content.
  • the theory is that the longer a user is engaged on a site, the greater the chance that the user can react to the offer or advertising presented. In other circumstances, the user is required to watch an ad prior to, or as a short interruption of, viewing of content.
  • One method of user engagement is to provide games for the user to play.
  • WEST ⁇ 237492833.4 a web site based on predicted demographics.
  • a system may have some user history that can be useful in customizing ad and offer presentation to the user, but still existing systems to not provide a truly personalized user experience.
  • the system provides personalized marketing combined with the user satisfaction of gamified applications and playing games, which provides a unique offer presentation entertainment solution with the computer implemented capacity to personalize offers.
  • the system includes a learning module that collects data and behavioural information from each user and customizes advertising, offers, and even application utility/play for the user. Over time, the system provides interaction with the user that provides consumer opportunities that are more likely to be accepted by the user,
  • the system provides a plurality of applications and games that can be played by a user of the system.
  • a user may or may not be required to make a purchase to utilize the system.
  • the system presents players with the opportunity to win a tangible benefit regardless of their application/game outcome.
  • the system incorporates "achievements" that present the user with rewards based on gameplay and certain actions. Some of the achievements can be improved offers (e.g. 35% off instead of 30% off, longer redemption time limits, multiple users of coupons, and the like).
  • the system allows merchants to distribute offers with a very high degree of variability in all attributes of the offer. That, combined with a sense of "winning" an offer, presents offers in such a way that a merchant could choose to have a spectrum of offers ranging from negative margin to revenue generating and could control the distribution of those offers temporally, demo graphically, or individually.
  • the system has online and offline utility, for this system may offers that could be used in physical stores.
  • the system's technology presents merchants a way to create multiple tiers of offers (e.g. $1 or $100 off) to multiple demographics of users through the exciting delivery system of applications or playing games.
  • the system allows merchants to identify individuals with a high likelihood of becoming customers, and also to create offers that are likely to be accepted by users.
  • Figure 1 illustrates an embodiment of a structure of the system.
  • Figure 2 illustrates one embodiment of interaction with the sytem.
  • Figure 3 is a flow diagram illustrating the initial log-in to the system in one embodiment.
  • Figure 4 is a flow diagram illustrating operation of the system at the user interface in one embodiment.
  • Figure 5 is a flow diagram illustrating application or game play in one embodiment of the system.
  • Figure 6 is a flow diagram illustrating the presentation of an offer in one embodiment of the system.
  • Figure 7 is a flow diagram illustrating the operation of the Offer Wheel in one embodiment of the system.
  • Figure 8 is a flow diagram illustrating an embodiment of user preference collection.
  • FIG. 9 is a flow diagram illustrating product placement in one embodiment of the system.
  • Figure 10 is a flow diagram illustrating an offer in an embodiment of the system.
  • Figure 1 1 is a flow diagram illustrating an embodiment of ad generation in the system
  • Figure 12 is a flow diagram of an embodiment of offer creation in the system.
  • Figure 13 is a flow diagram illustrating an embodiment of personalization in the system.
  • FIG. 14 is an example computer environment.
  • the system provides a computerized process for personalizing offers in association with digital/online/mobile applications or games.
  • the system may be implemented on a website, mobile app, video game console or networked consoles, or social networking site. In the examples below, some are described in conjunction with the offer of a game. It should be understood that even though a game is mentioned, the system can provide applications (e.g. "apps” and the like) entertainment, or other environments without departing from the scope and spirit of the system.
  • the system presents an application, game or other form of entertainment to a user. During the course of user's interaction with the system, user is presented with an offer which the user may accept or decline.
  • the redemption form a user completes for the advertiser is integrated into mobile, browser or other technology so that the form may appear in native format.
  • the form may utilize an auto-fill function for personal (or other information). So instead of displaying a click-through ad, the user is shown a redemption form native to the applicable platform (mobile, browser, console, etc).
  • the system incorporates many styles of games, which may include, but are not limited to: Tabletop games, Board games, Card games, Dice games, Casino-style games, Miniature games, Pencil -and-paper games, Tile-based games, Role-playing games, Chess game Video games, Arcade games, Computer games, Console games, Handheld games, Mobile games, Online games, Flash games Alternate reality games, Educational games, trivia, Card Games Children's games, Creative games, Lawn games, Letter games Play-by-mail games, Play-by-post games, Locative games, Mathematical games Parlor games, Party games Conversation games, Daring games, Guessing games, Singing games, Paper and pencil games, Playground games, Pub games Drinking games Puzzles, Quizzes, Redemption games, Role-playing games Skill games, Street games, Travel games, Wargames, and Word games
  • the system may provide a number of features to the user including:
  • Offer Wheel - displays offers that emanate from an offer generator.
  • the offer generator is a personalization engine that matches an offer (from a portfolio of available offers) to the user's preference(s). In the absence of known user preferences, then the offer generator will select the "highest quality" offer to present to the user (based on business criteria and the performance history of offer in the system).
  • the Offer Wheel may be in the form of a slot machine, a prize wheel, or other embodiment.
  • FIG. 7 is a flow diagram illustrating the operation of the Offer Wheel in one embodiment of the system, it should be noted that the Offer Wheel may not appear in every game or embodiment of the system.
  • the system identifies the current user.
  • the system retrieves personalization information of the user.
  • the system applies the personalization information to the offer generator.
  • the offer generator identifies a plurality of offers that represent the best match for the user's personalization data. It should be noted that the matches may also be influenced by other metrics, such as time of day, day of week, time of year, recent user activity, and the like.
  • the system identifies at least a certain number of offers to place on the offer wheel (e.g. 5, 10, or 20). The system has the flexibility to automatically format the offer wheel so that whatever the number of offers may be, the offer wheel appears fully populated with no blank spaces.
  • the system applies a filter to the selected offers to determine if any offers should be excluded from the offer wheel. This may be due to an offer being explicitly rejected, or it may be that an offer has just been presented to the same user and repetition is discouraged. There may also be rules, conditions, or metrics defined by the offer provider (e.g. merchant, advertiser, publisher, and the like) that would result in it being excluded at this point.
  • the system populates the offer wheel with the best ranked offers and presents the offer wheel to the user at step 707.
  • step 708 the user may then activate the offer wheel.
  • the system provides offers to users during the course of their interaction with the system.
  • Offer Store a virtual store where users may exchange virtual currency for offers.
  • the Offer Store embodiments include a variety of methods for redemption. Offers available to players in said Offer Store may be redeemed in exchange for: virtual currency, points, completed levels in a game, special events in a game (for example, acquiring an in-game magic potion or unlocking an in-game special bonus or achievement), or another method.
  • the Offer Store makes offers available to the player in a presentation format (this format allows players to select an offer): an overlay, a popup, a scroll menu, a link stating "You Won a Free Offer! (or similar text), or other method.
  • the Offer Store may also provide categories of offers (for example: women's apparel, sporting goods, electronics, etc) from which a user may select available offers.
  • the Offer Store may also show a compare and contrast list of items for sale from a particular merchant at a price before the offer is implemented, and then showing the price of the item after the offer is implemented (for example, a bicycle for $100 is the current price at Store X.
  • the $100 bicycle at Store X is then compared by the system to a discounted price of $80 for the Store X bicycle when a user implements the relevant Store X offer).
  • the process of a player selecting an offer would then include the user engaging in one of the redemption methods (above) in combination with an offer selection method (above) that results in the player receiving an offer.
  • User preference collection users enter information that indicates a user's preferences for deals, goods, or services, This may appear as a checklist, scroll menu, popup, empty text box asking for key words, or some other embodiment.
  • Figure 8 illustrates an example of user preference collection in an embodiment of the system.
  • the system presents the user with specific queries to solicit demographic information or consumer preference information. This may be at account creation time, on a regular calendar basis, or periodically as desired. If the user has previously provided responses to the queries, the step may just be a confirmation that the
  • WEST ⁇ 237492833.4 user data is still up to date and current.
  • the system checks for any personal accounts of the user that have been associated with the system, such as social network accounts, game system accounts, and the like. If so, the system scrapes personalization and preference data from the associated accounts at step 803.
  • the system pulls historical data from the system associated with the user.
  • This historical data can include data on apps/games played, number of log-ins, time spent in the system, offers clicked, offers accepted, offers purchased, applications downloaded and other historical data that represents the interaction of the user with the system.
  • the system applies a preference generation algorithm to the collected preference data to generate a preference profile for the user.
  • This preference profile can be used in other parts of the system to match the user with offers to optimize the user experience, to maximize the chance of offer acceptance, and to provide returns to advertisers and merchant partners of the system. Completing this preference profile may also facilitate faster generation of offers and personalization in the system because less system resources will be used in the future if this step is done first.
  • One example of a preference profile may include a list of items, services, goods, and the like in which the user has expressed an interest either explicitly or implicitly. The table below is one embodiment of categories in the present system.
  • User information collection - users enter demographic information, which may sync with their social networking information, or provide the system with other information.
  • Timer - a timer keeps track of how long each user is logged on to the system. The timer also indicates how long a user is spending on any part of the system including individual apps/games.
  • Scoreboard may be a public or private metric kept for users to see who has the highest scores on particular apps/games. Metrics are also be kept for offer accepts, the amount of savings users have accumulated, etc. Scoreboards may appear in a variety of embodiments using various temporal (for example, daily, weekly, monthly, lifetime or other variable timeframe) or metric scoring. Scoreboards also appear in individual games and may appear in the form of a filling thermometer or other visual device to indicate when a player is close to earning an offer.
  • Friend invitation - users invite friends to join the system (the company's website or platform).
  • the system also allows users to post badges, and/or pictures of themselves with the system's emblem, on other websites.
  • the system in one embodiment enables an "invite" system that locks out other, non-invited players from joining the game.
  • Product placement in applications, games, or another form of entertainment the system will establish product placement in applications, games, or another form of entertainment.
  • Product placement will occur by installing brand and/or product specific source code into the applications, games, or another form of entertainment that displays the brand/product in the applications, games, or another form of entertainment. From the player's perspective, this brand/product will appear to be part of the applications, games, or another form of entertainment.
  • FIG. 9 is a flow diagram illustrating the product placement operation in an embodiment of the system.
  • the system identifies the interaction selected by the user. This may be a game, an application or some other interaction.
  • the system determines if the game is suitable for a product placement insertion. If not, the system returns to step 901.
  • step 903 identifies the inserts that may be used with the game.
  • the system determines if the insert is an automatic insert (e.g. regardless of personalization data). If so, the system adds the insert at step 905. If not, the system proceeds to decision block 906 to determine if the user personalization is above the insertion threshold. If so, the system inserts the placement at step 907. If not, the system returns to step 901.
  • an automatic insert e.g. regardless of personalization data
  • affiliate Cookie - tracking cookies will be used to enhance user preferences as well as continue the consummation/purchase of offers. Tracking cookies may stay active indefinitely or have a preset or yet-to-be-determined expiration.
  • Advertising for third parties ⁇ third parties advertising may be incorporated into the system and include various forms (banners, pop-ups, etc).
  • Advertising for the system - users that promote the system through advertising will be rewarded with offers or virtual currency.
  • users may also receive virtual currency or unique offers through promoting the system. Promotional activities include posting on social networking sites, inviting other users via email to join the system, etc,
  • Real World Events - real world events, like election outcomes, sporting events, or weather will be available for users to "guess” and win virtual currency or immediate offers.
  • the Real World Events may also be personalized based on the database determining the types of events a user has interest in following.
  • the user may receive a personalized offer incorporating information about a real world event. For example, the user is at a major league baseball game where the using is playing an app that allows the user to guess if the next batter will get a hit.
  • the system may generate a personalized offer generated from the user's history, profile, performance at the real world event, performance while watching the real world event, and/or other business criteria.
  • the offers could be physically delivered (for example, an usher could bring the user a hot dog at the game), emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • Deal-of-the-day system The described system may also be used for deal- of-the ⁇ day offers like those found on Groupon or Living Social.
  • the typical deal-of-the- deal enables a customer to purchase a gift certificate for a higher value than the amount paid. (For example, receiving a $50 gift certificate to Acme, Inc. in exchange for $25 payment).
  • the system incorporates a unique aspect of presenting gift certificates.
  • player initiates applications, games or another form of entertainment.
  • a player may pre-select a gift certificate as the potential offer for play.
  • a player starts with the ability to buy a gift certificate for face value (for example, the gift certificate has a face value of $50, so the player may purchase it at the commencement of the game for $50).
  • the player begins interaction with the system (i.e. plays a game, interacts with an app or entertainment, and the like).
  • the player Through achievements/milestones completed during app/game play or interaction, the player receives a higher value of gift certificate in exchange for the same $50 payment (for example, a player passes a level in an app/game, so now the player's $50 purchase buys a gift certificate with a value of $55).
  • the player starts with a $50 gift certificate face value, and through completing achievements/milestones during app/game play, the price the player has to pay for the same $50 value gift certificate is lowered (for example, a player passes a level in an app/game, and now may buy a gift certificate worth $50 for only $45).
  • step 1004 the system determines if the player has reached a milestone or achievement. If not, the system returns to step 1003 and play continues. If so, the system proceeds to step 1005 to determine if the maximum upgrade to the certificate has already
  • step 1007 it is determined if the interaction is ended, if not, the system returns to step 1003. If so, the system awards the current level of the certificate at step 1008. As noted above, this may be an increased value of a certificate for the original price, a lower price for the same value of the certificate, or some combination thereof.
  • a cap is placed on the amount of additional value the player may receive, so as to "max out" the potential reward for each gift certificate (for example, in the embodiment where a player earns more value for the same price paid, then a player may not receive more than $100 in gift certificate value for a $50 purchase. Alternatively, for the embodiment where a player pays less for the same value of gift certificate, then a player may not receive a $50 gift certificate value for less than $25).
  • the deal-of-the-day user does not know the exact merchant for the gift certificate or offer; rather, the system generates a personalized offer for the user.
  • Advertiser interface a platform for the system and advertisers (third party merchants) to exchange information.
  • the advertiser interface allows advertisers to login to the platform to view the platform's unique analytics. These analytics include:
  • WEST ⁇ 237492833.4 prices for example, "20% of Macy's merchandise”
  • the algorithm determines the optimal percentage off to maximize revenue (based on the historic activity of player's spending or the player's demographic categories), and then provides recommendations to advertisers on what specific offers are likely to maximize the specific advertiser's revenues.
  • Internal affiliate network - third party merchants may go directly to the system's advertiser interface and provide offers to present to users.
  • the system will also Present Advertisers with Demographic Data from their industry or associated industries/advertisers that have associative relationships with advertiser's products/offerings.
  • the system can use geo-location mechanisms in mobile devices to permit location based advertising or offers (e.g. using users' latitude and longitude to present offers when the user is within a certain distance from the advertiser's locations).
  • location based advertising or offers e.g. using users' latitude and longitude to present offers when the user is within a certain distance from the advertiser's locations.
  • the system can also track and monitor how many of an advertiser's customers that have received offers (with said offer comprising of when XYZ criteria) are met (online criteria) actually visit the advertiser's physical store. Therefore, the advertiser will obtain information on the types of offers to be presented online/mobile that will drive traffic to the advertiser's physical store.
  • the system provides an interface that allows an advertiser to create an offer using templates provided by the system. This precludes the need to have an advertising company prepare the offer, or even to have in-house staff prepare the offer.
  • the system allows the advertiser to create an offer, match the offer to a set of metrics to identify a particular player or type of player (or even to a specific player in one embodiment), and to choose an offer delivery method.
  • the system provides a mechanism and system for determining the Ad and the Ad Price.
  • FIG 11 is a flow diagram illustrating the operation of the self-serve ad system in one embodiment.
  • the advertiser logs into the system.
  • the system confirms the billing arrangement for the advertiser. The arrangement
  • WEST ⁇ 237492833.4 may be billed later, pay immediately, pay set fee, performance based billing, pay ala- carte depending on the ad generated, or the like.
  • the advertiser selects the ad or offer type that the advertiser would like to present to the user, as well as the demographics of the targeted customer. Different types of ads or offers are defined in categories as described below. The advertiser will also identify the categories of interest, provide a desire monthly budget, daily budget cap, gender of the target (it may be both or either), one or more age ranges, and geographical information (e.g, country, state/province, metro area/city/town).
  • the system at step 1104 presents the advertiser with templates for that ad type and queries to assist in generating an ad.
  • the queries solicit information that is then automatically added to the ad or offer when presented in the system.
  • the system at step 1 105 presents a bidding mechanism enabling advertisers to make bids on the types of ads they would like to place in the system.
  • the system also allows advertisers to bid for premium placement that would augment the advertiser's offers in the queue to receive priority placement amongst the users the advertiser wishes to reach.
  • the ad generation process may be freeform, with or without image and writing.
  • the system may allow the advertiser to provide a link/URL/FB page/mobile site/etc.
  • the advertiser confirms the ad and it is now available for use in the system.
  • the system may ask the advertiser for a desired result (e.g. click-through rate, offer acceptance, and the like) and the system may identify the optimal ad type to achieve the desired result.
  • a desired result e.g. click-through rate, offer acceptance, and the like
  • the system also includes a service for advertisers that helps to define an optimized promotion for the advertiser.
  • an advertiser knows the demographic that the advertiser wants to reach, but may not know the best promotion with which to reach that demographic. Because the system has extensive historical and demographic information on prior promotions, the system can identify the optimal promotion for a particular demographic.
  • Figure 12 illustrates an example of an embodiment of this process.
  • This process takes advantage of the system's statistical database.
  • the system classifies promotions into one or more of several types and categories. Each promotion type is associated with historical data of when the promotion type has been offered or presented and the results of each presentation. This allows the creation of meaningful data about the performance of the promotion type and when it works and doesn't work, as well as for which demographic indicators the promotion has been most successful.
  • promotion types include, but are not limited to, the following:
  • Cost-per-action information (provide us with your contact information and get a free T-shirt)
  • the advertiser logs into the system.
  • the advertiser enters target demographic information, type of products they offer, and desired performance metrics (e.g. click-through rate, offers redeemed, and the like).
  • the system retrieves historical information associated with the target metrics identified by the advertiser in step 1202.
  • the system identifies the best performing promotions and suggests the best performing promotion based on promotions targeting similar demographics as well as products that performed well (click, redemption, etc),
  • the best performing promotions are presented to the advertiser along with
  • the auto-creation of an offer is a mechanism to encourage advertisers to create better offers so that users have a better experience and are more likely to redeem advertiser's offers.
  • the price charged to advertisers is in some manner dependent on the cost of the offer to the user. For example, when an advertiser provides an offer that is free to the user, the price to the advertiser will be lower than if the offer has a cost to the user. Thus, the cost increases if the anticipated redemption rate is lower - this results in a balance in cost-to-redemption because something that is free with conditions may be redeemed only 15% of the time whereas something that is totally free may be redeemed 45% of the time.
  • the system incentivizes advertisers to create offers that are more likely to be redeemed.
  • the system uses historical data (elements that the system has collected) as well as category and industry data (elements from the system or from external sources) to determine a subjective/objective balance of redemption likelihood and cost to advertisers.
  • the system captures all elements of code and creative.
  • the ad may have a designated end date. In other instances, the ad may be open ended.
  • the system may periodically pings/scrapes the destination page to determine if the page is active.
  • the system may also cache the HTML and other code on the page, and then periodically check to see if the code was changed. The system may do this to preserve the fidelity of matching offers presented within the system to the redemption page. If it is not, the system sends an alert so that users of the system are not presented with defunct offers or deals.
  • the system may automatically pause the offer or classify it as inactive and even remove the offer from the system or the live bucket of offers being presented to users...
  • the present system incorporates a Personalization Mechanism/Methodology/Process, which stores, databases, and organizes user information. Algorithms/programs utilize this information in order to present users offers they are more likely to accept.
  • Demographic information will be requested/harvested at step 1301 , such as (but not limited to): name, gender, age, occupation, location, interests, hometown, relationship status, education level, names of schools attended, country of origin, credit history, income, employment history, IP Address, Neilson DMA, MAC address, cookies or other indicators of previous website visits, user behavior obtained through tracking of mouse, keyboard or other human/machine interface point, or the like.
  • the system accesses information from social networks or third party aggregation services by scraping data from those systems or through contractual arrangement with those systems.
  • Such systems can include, in addition to social networks, other personal information gathering devices such as computerized smart glasses like Google glasses, computerized smart vehicle like Google car smart silverware, smart-phones, and any other device that can provide information about the user.
  • the system builds a history of the user with the system itself (e.g. machine learning from user interaction with system including history of binary acceptance/denial of offers, clicks, games played, offers purchased, temporal metrics, risk preference, style of play, and the like), Because users are regularly presented with offers, the system accumulates a large amount of user choice information data.
  • the applications/gaming element is instrumental in gathering user choice information, for applications and games engage users to remain engaged with the system.
  • the element of "winning” or “earning” presented offers also provides the system with a higher rate of user interaction because users feel compelled to take advantage of offers that are won (or perceived by the user to
  • step 1305 the system applies decision tree learning using a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.
  • association rule learning is a method for discovering interesting relations between variables in large databases. These associations are stored in the user's personalization database and form part of the personalization profile of the user.
  • Inductive logic programming at step 1307 is an approach to rule learning using logic programming as a uniform representation for examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, the ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples.
  • Support vector machines are a set of related supervised learning methods used at step 1308 for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, the SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
  • Cluster analysis or clustering at step 1309 is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense.
  • Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.
  • Associations made between offers in different categories is developed by the system to determine cross-category personalization. For example, the system may determine a positive correlation between Airlines offers (or a specific airline, like Delta) and laptop computers (or a specific laptop
  • WESTC37492833.4 like a 13" monitor Dell Computer in one embodiment, a user that clicks on an airlines offer will then be identified as a likely candidate to accept a laptop computer offer (this association then triggers the system to load laptop computer offers or the like into a user's queue of offers). These associations may be developed based on user behavior, user demographics, advertiser metrics/desired demographics, or other business criteria.
  • the system can obtain personalization data from any source.
  • the system can obtain information from game systems (e.g. Xbox, PlayStation, Wii, Kinect, and the like) or other forms of information sources (photo analysis technology, tv viewship analysis technology, computerized smart glasses like Google glasses, computerized smart vehicles like google car, smart silverware, smart phones, mobile devices, and the like) and use the information to further refine the personalization of the user.
  • game systems e.g. Xbox, PlayStation, Wii, Kinect, and the like
  • other forms of information sources photo analysis technology, tv viewship analysis technology, computerized smart glasses like Google glasses, computerized smart vehicles like google car, smart silverware, smart phones, mobile devices, and the like
  • the system can utilize user behaviour to determine the user's risk-preference and identify the level/type of offer as well as the frequency of offer presentation that is optimized for that user. For example, if player A is an aggressive (risky) player and player B is a meek player, the system will note that in player profiles. The level of riskiness may be determined by a player's willingness to choose high-risk items in the game. If an offer is presented and player A clicks on it and player B does not, the system may determine that this offer is preferred by risky players.
  • a user's risk profile is assessed, and the user is then presented with free chips, coupons or other elements to incentive the user to play a game where the casino has better odds of winning. For instance if there is a very aggressive
  • the system may also derive information based on the user's length of play, propensity to share offers on social networks or via email, speed of play, and the like.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the system can personalize offers based on shows/commercials that a user viewed, did not "fast-forward through,” did not “mute,” or other business criteria. This information is also retained to update the user's personalization information.
  • the user is tracked, and the system provides contextual offers.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically .
  • the offers could also be delivered to a store's database/system, so upon checkout, the user will automatically get discount from the offer.
  • the user may receive an offer based on analysis of photos.
  • Photos may be of the user, of the user's activities, friends, favorite places, and the like.
  • Information from user's photos is utilized by the system to determine associations as well as facilitate offer personalization.
  • a hair dye offer could be presented to someone with neon colored hair in their photo.
  • Another example is presenting an offer for New York because the user has photos of the Empire State Building on their social network page.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the user may win an offer while in a computerized smart vehicle (driverless vehicle, for example Google car), and then the vehicle may drive the user to a location to redeem the offer.
  • the system presents offers based on the destination or route a user enters into the computerized smart vehicle.
  • a vehicle interactive system such as OnStar (or related) type services provide information that we can then also customize offers. For example a user may be driving and passengers are watching a video or there is a streaming video being viewed in the car, and the system will provide a contextual offer related to the video/entertainment content.
  • the user is tracked, and the system provides contextual offers.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the offers could also be delivered to a store's database/system, so upon checkout, the user will automatically get discount from the offer.
  • the system may implement monitoring technology in a user's silverware, plate, or other food preparation or eating utensils, so that the system may present contextual offers based on food selections (for example, if doing a lot of BBQ, then BBQ sauce offers; or if eating a lot of red meat, then Lipitor offer, etc).
  • the monitoring technology may be a small camera, sensor with protein density evaluating capabilities, or other technology able to determine the type of food.
  • the information gathering source may be a mobile/online application where the user enters the foods consumed.
  • the user is tracked, and the system provides contextual offers.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the offers could also be delivered to a store's database/system, so upon checkout at the physical store, the user will automatically get discount from the offer.
  • the system obtains information about a user based on a user's use of a weight measurement device or body mass index analytic device.
  • Personalized offers may be presented to the user based on single points of data or aggregate points of data (for example, someone is losing weight, so the user is presented offers for low-top shirts; alternatively, a user is gaining weight, so the user is presented with an offer for a weight loss program).
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the system may be integrated with computerized smart vision systems (such as Google glasses) to present offers based on what the user is viewing, has viewed, or a combination of viewing multiple items over time or during a specified temporal period (for example, viewer looks at steaks, corn, and charcoal in the past 20 minutes, so the system presents user with an offer for BBQ sauce).
  • the user is tracked, and the system provides contextual offers.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the offers could also be delivered to a store's database/system, so upon checkout, the user will automatically get discount from the offer.
  • the system can share data about a user based on geo- location or other information based on data from a phone.
  • the system may automatically share user data, either through a system database, or via a device on the user (e.g. smartphone, ipad, dongle, and the like).
  • the system tracks the user as well to add to the personalization information about the user that is part of the system, and that allows more personalized and contextual information to be provided,
  • the system is not limited to browser or mobile based interfaces.
  • the system is implemented in casino type games. These may be slot machines, table games, or other casino-style games.
  • the system may be utilized with a casino player's card system for presenting contextual offers to the user during game play.
  • One advantage of the system is the ability to provide personalized and contextual offers to the user even when a user does not win the individual play or bet. For example, if a user bets $10 on a hand of black jack and wins, a user gets the wager plus a free medium pizza (based on user demographics and user behaviour).
  • the system adds to the casino revenue stream by allowing third party offers to be presented to players, either during play or at the conclusion of play, when their player card (typically obtained from the casino) is updated.
  • the system may offer (e.g. for a select group of players who have spent a specific amount of time with a particular game/app or they've interacted with a specific vendor) a mystery offer that may include a special offer, the opportunity to level-up, or a package of goods/virtual goods.
  • the mystery offer could be offered to just one person, or it could be an invitation for a "playoff with other people meeting the personalized selection criteria that triggered the system to present a mystery offer.
  • the mystery offer can be tied to any suitable trigger as noted above. This is a way for advertisers to reach users that may or may not fit their specific demographic metrics.
  • the user is playing real-life games.
  • the user is playing a game at a fair.
  • the fair game may have a video monitor, card reading system, or other technology to identify the user.
  • the fair game may present the user with a personalized offer or reward based offer.
  • the offers could be physically delivered, emailed, delivered to mobile device, or otherwise delivered to a user electronically.
  • the system is compatible with environments (such as role playing games) that have their own virtual currency.
  • Game X has virtual currency and an embodiment of the present invention incorporates Game X's virtual currency is as part of a personalized offer to the player playing Game X. This may be in combination with another offer or a standalone offer of virtual currency personalized based on the player's play, demographics, and other elements. Local virtual currency may be combined with a real world offer.
  • the aggregate information obtained through various sources is added to the database and utilized to better personalize offers to users regardless of the offer presentation source the user chooses to use.
  • the system provides the ability to enter a form/sweepstakes form in the native environment (e.g. the system environment). Even if an offer comes from outside the system, the system can provide the user the ability to accept or decline that offer without leaving the system (e.g. without going to another website). This may be accomplished by providing API's and templates to all participating advertisers. In other embodiments, the system can read metadata or XML data from a destination website, collect the data from the personalization database, and handle all the communication with the third party website behind the scenes so from the user's perspective it is a seamless/smooth user experience.
  • Offers to present to users will be accumulated by utilizing a variety of means including (but not limited to); affiliate brokers, direct affiliate or contractual agreements
  • Figure 1 is a diagram of the structure and operation of an embodiment of the system.
  • a user 1 can interact with the system via a terminal 2 or mobile device 3.
  • the terminal 2 may be an electronic or electromechanical hardware device that is used for entering data into, and displaying data from, a computer or a computing system.
  • Terminal 2 includes, but it not limited to, personal computers, laptop computers, video game consoles (e.g. Xbox, PlayStation), and the like.
  • the Mobile device 3 is a mobile version of a computer terminal and may be embodied as a smart-phone, tablet computer, or other mobile device with internet and/or mobile application capability.
  • the user 1 interacts with the application via a Website or Mobile Application 4.
  • the website may be accessed via the terminal 2 or the mobile device 3.
  • the Website 4 in one embodiment is a collection of related web pages containing images, videos or other digital assets, accessible via a network such as the Internet or a private local area network through an Internet address known as a Uniform Resource Locator.
  • the Mobile Application may be an application for mobile devices that allows for interaction/use similar to a website but native to a mobile device.
  • the user can interact with the system via a Social Network 5.
  • the Social Network 5 may be a mobile application or web based social platform like Facebook or Twitter where users interact and communicate with other users.
  • the user interface is the primary interface where user 1 plays games and is presented with offers. User 1 enters information into user interface 6 through the operations of playing games, entering preference or personal data (for example, surveys),
  • a Virtual Currency module 7 tracks the fictitious and nominally valueless online tokens may be redeemed for opportunities using the system.
  • the virtual currency also includes an envisioned offer store, where users may redeem online currency for specific offers of their choosing.
  • the system includes in one embodiment a Data Analysis Processor 23 that includes modules for Games 9, Offer Generator 10, Personalization Engine 12 and Recommendation Engine 13.
  • the Data Analysis Processor 23 directly accesses information from social networks or third party aggregation services through login system, user submitted information, and user interactions with offers (machine learning from user interaction with system - history of binary acceptance/denial of offers).
  • the Games module 9 stores information, code, and/or links to games that the user 1 may play using the system.
  • the user may play games and win specific offers or virtual currency that may be redeemed for Offer Wheel spins (which result in an offer) or specific offers.
  • Games may also include gamified applications.
  • This may include a user interface for app/game developers to enter their own games for use within the system.
  • the app/game user interface may provide templates for basic look and feel to be compatible with the app/game.
  • the templates may allow options for the developer, such as including (but not limited to): a logo, text font, text color, text size, choosing primary and secondary foreground colors, primary and secondary background colors; age range, and gender scale (e.g. from 0-100% female).
  • the Offer generator 10 utilizes information from the user database, apps/games, and machine learning system engine to generate offers personalized for each user. This includes but is not limited to the offer resulting from an Offer Wheel spin.
  • the offer generator 10 may also incorporate a weighted scale to provide users with more attractive (higher value gift certificates or more % off coupons) depending on user's
  • the Machine Learning Personalization Engine 12 uses data provided by the user, as well as data collected by the system based on actual activity of the user 1. This data can include games played, amount of time spent, offers selected, purchases made, points earned, advertisements click through, and the like. The Machine Learning Personalization Engine 12 uses the data to define and refine a user profile that can be used by other parts of the system to provide more personalized opportunities to the user 1.
  • the Machine Learning Personalization Engine (12) and the Machine Learning Recommendation 13 (used to assist in the selection of offers or ads) utilize algorithms that organize the information as described in conjunction with Figure 13.
  • the Advertisers module 14 is a database of third parties that provide coupons, gift certificates, discounts, promotional items, or other forms of advertising to be awarded to users as offers. This may be merchants or proxies for the merchants (e.g. advertising agencies, publishers, and the like).
  • the Advertiser Interface 15 is the primary interface where advertisers (14) input and receive information from the system. For example, advertisers may choose the duration, quantity of offers, the percentage of discount, the amount of gift certificates, geographical limitations, limiting offers to users with specific demographic information, how many offers are released per hour, as well as other information. This interface allows advertisers to manage and customize ads, adjust parameters, define desired demographics for particular ads, all without needing assistance from the system administrator.
  • Each offer can include a plurality of metadata that can be used by the merchant to define the preferred receiver of the offer, temporal aspects of the offer (either length of time to run the offer and/or particular times and days to make the offer).
  • WESTC37492833.4 merchant may have a plurality of offers that are appropriate for a variety of users, and the Advertiser Interface 15 allows the merchants to upload multiple and different types of offers as well as to define appropriate users for the offers.
  • Each offer can have some associated conditional rules that can help customize the offer even further. For example, if an offer is for a male in the 18-25 range, and a user fits that description, the offer could also have a conditional rule that provides greater or lesser rewards depending on if the user "won" the game or earned some other achievement associated with the system. Alternatively, the offer may have a conditional rule if the user fits a particular behavioral or demographic category.
  • Advertiser Database 16 Information about the offers, prospective customers and metadata is stored in Advertiser Database 16.
  • the External affiliate Network module 17 is a network of affiliate offers; acting as an intermediary between publishers and merchant affiliate programs.
  • the externa! affiliate network brokers and organizes payments made into the system in exchange for a user making a purchase, completing a survey, or completing another task.
  • the Internal affiliate System 18 is similar to external affiliate system 17, however, the internal affiliate system is managed internally with no fees paid to the external network.
  • An Internal Offer Completion Interface 19 provides a direct method to complete user financial transactions within the system, without the need to navigate to an external system site.
  • an External Offer Completion Interface 20 allows users to make financial transactions/purchases on a third party merchant's website (like Amazon.com).
  • the Financial Institution module 21 links to a bank, credit union, or other financial institution capable of processing financial transactions associated with the system.
  • the system may also provide an Internal Merchant module 22 that implements a store, utilizing traditional ecommerce format, to offer goods/services directly to users.
  • FIG. 3 is a flow diagram illustrating the operation of an embodiment of the system.
  • a user logs into the system using a computer terminal, video game system, or mobile device.
  • the system determines how the user is accessing the system. If it is a website the system proceeds to step 303. If it is a mobile app the system proceeds to step 304, and if it is a social network the system proceeds to step 305.
  • the user interface may appear differently in a website, mobile app, or social network.
  • Figure 4 is a flow diagram illustrating operation of the system after the user has reached the user interface.
  • the user accesses the user interface.
  • the system determines the user's device accessing the system. Personalization of offers may also be derived from determining the user's device.
  • decision block 402 it is determined if the user will interact with an internal offer. If so, the system takes the user to an Internal Offer Completion Interface(19) at step 403 to complete an offer within the present inventions internal ecosystem.
  • the offer may be emailed, sent via text message, enabling a phone call from the advertiser to the user's phone, or other method of communication (for this embodiment, offer completion is performed using a link in user's email, responding to a text message, through a phone call, or other means outside the user interface of the system).
  • step 404 the system updates the user database with appropriate information from the internal offer interaction.
  • This information includes all elements from which the user interfaced) collects information, and includes all elements from which the user database(8) has collected information.
  • the system moves to decision block 405 to determine if the user has earned virtual currency. If so, the system
  • WESTY237492833.4 updates the user's virtual currency account at step 406 and exchanges information with the personalization engine at step 407. After step 404 or step 407, the user proceeds to the game interface at step 408.
  • Figure 5 is a flow diagram illustrating the system during game play in one embodiment.
  • the user selects an app/game to play.
  • the system determines if there is a pre- app/game offer associated with this app/game and/or with this user. If so, the system presents the offer at step 503. This offer will typically be customized for the user based on demographic and personalization data associated with the user. The offer may be a stand-alone offer or it may be associated and modified based on some result of the app/game. The system then proceeds to app/game play at step 504 and the user plays the app/game.
  • the system awards the achievement at step 506. If not, the system continues app/game play at step 507.
  • step 508 it is determined if the app/game is over. If not, the system returns to step 504 and play continues. If so, the system proceeds to step 509 and selects a custom offer for the user based on a number of factors. These factors can include the score/level achieved in the app/game, any achievements unlocked with the app/game, the demographic and personalization information of the user, temporal or seasonal information associated with the merchant providing the offer, the history of offers made to the user, and the like.
  • the offer is presented to the user. In one embodiment, offers are presented to the user during app/game play rather than at the end of play.
  • FIG. 6 is a flow diagram illustrating the operation of offer generation in one embodiment of the system.
  • the system generates an offer trigger.
  • An offer trigger is an event, situation, circumstance, request, or some other condition for which the system determines if an offer is appropriate.
  • the system analyzes the trigger, determines the type of trigger it is and compares the trigger to a database of advertiser's triggers in the advertiser database.
  • the system identifies all advertisers who have indicated a desire to provide an offer when this particular trigger is generated.
  • the system retrieves the personalization data for the user associated with the trigger.
  • the trigger could have been generated in a number of ways. It could have been generated from log-in, from app/game selection, from internal offer selection, from app/game achievement, from pre- app/game offer selection, from score achieved, from level achieved, and the like.
  • the system compares the personalization data of the user to the metrics defined by each advertiser that was identified in step 603.
  • an advertiser can define metrics and metadata of users to whom it wishes to advertise or make offers.
  • the system generates a score for how closely the personalization data of the user matches the metrics of the advertiser.
  • the system identifies the top scoring advertiser. This advertiser will have the opportunity to serve an ad or offer based on the trigger to the user.
  • the selected advertiser may be an advertiser with the top score or it may be an advertiser who has paid a certain price to guarantee ad serve for this particular trigger based on time of day, day of week, geographical location, gender of user, type of app/game, or other metrics that can be defined by the system.
  • the system compares all ads and offers of that advertiser to the personalization data of the user and ranks the available offers in order of effectiveness for that user. This means that the system determines the ad or offer that is most likely to result in an affirmative or successful response from the user.
  • the system serves the top ranked ad to the user.
  • the system uses a bucket sync system to review advertiser campaigns, determine which campaigns/offers were viewed, then apply a frequency monitor to prevent the same offers from being seen before the other offers are displayed first (to avoid seeing the same offer twice in a row, or in very close temporal proximity). For the next user session, then the sync goes to the offer that is most clicked or another ranking based on "business criteria.”
  • the targeting mechanism When an offer is targeted, the targeting mechanism will choose from this bucket. It will consider the top offers first and then demographic information, etc. In one embodiment the system uses rating as the primary metric - however, the system may also apply historical data to provide another embodiment for selecting an offer.
  • a think mechanism reviews each offer in a bucket, or each potential offer, and reviews the history. The think mechanism knows if an offer was viewed X amount of times, or if was clicked X amount of times and it was redeemed X amount of times. For example, the system can place the offers with the highest redemption rates at the top, or the highest click-through rates at the top, depending on the business criteria to be implemented. For the other offers, the values may be lowered.
  • Bucket sync For the Bucket sync, in one embodiment it reviews all system campaigns. Some campaigns may be an A/B campaign, where there are two offers or two types of offers (offer "A" and offer “B”). The system determines which offer has the most and which has the least number of views. In determining which offer to present, the system can use this numerical and historical data to distribute views between the "A" and "B" offers evenly, or pursuant to an advertiser request. In other cases, the system reviews the user and attempts to avoid consecutive presentations of the "A" offer, for example, and seeks to alternate offers so the user does not see the same offer more than once.
  • the system is able to evaluate all the historic data in real time.
  • the time window of analysis can also be changed so that the system can review history by day, week, month, and the like.
  • the bucket may also incorporate specific user preferences in its sync. For example, in an A/B test, a specific user may see only offer "A" and not offer "B" (in the same advertiser campaign). Over the course of time, the system recognizes the types of offers that the specific player has selected (or rejected) in the past, and thus will load a corresponding offer from the A/B test that correlates to an offer the specific player is most likely to accept/redeem.
  • the Offer Generator exchanges information with the Machine Learning Recommendation Engine.
  • the Machine Learning Recommendation Engine helps advertisers choose effective offers; therefore, the Offer Generator provides information about users specific to offers such as offers accepted or declined, the context of the acceptance or decline (e.g. game played, time of day, day of week, value/cost of offer, relationship to prior offers from the same advertiser, and other data metrics and metadata that can aid in fine tuning offers for a particular user.
  • the Offer Generator(lO) also exchanges information with the Machine Learning Personalization Engine 12 for the purpose of enhancing offer personalization for the Offer Generator (10), This also collects information from the Offer Generator(lO) (based on user choices) to create better personalization in the Machine Learning Personalization Engine (12)
  • the Advertiser Interface(15) also exchanges information with the Machine Learning Recommendation Engine (13), which allows improved advertiser(H) recommendations regarding what offers create results with users(l). This may include,
  • WESTV237492833.4 but is not limited to, the Machine Learning Recommendation Engine(13) providing advertisers(14) with data on what users(l) accepted offers, what users(l) declined offers, and recommendations for the types of offers that are likely to generate results for the advertiser(14).
  • this information can also include contextual information such as time of day, length of time or number of times playing a game, value/cost of the offer, and the like. For example, if an offer is available to a user and the offer includes conditions for maximizing the value or lowering the cost, it may be that a particular user only responds to offers after the value has been maximized or the cost lowered. In such a case, it may be useful to provide an easier path for the user to obtain the maximum value in order to entice that user to accept an offer.
  • Figure 2 illustrates the system experience from the perspective of user 1.
  • the User (1) accesses the system via a website (2), mobile app (3), video game network (4), social network (5), or other application of invention.
  • User 1 optionally logs in through log-in system. It should be noted that play/entertainment may also be available without login.
  • User 1 has an opportunity to provide personal information and preferences via User Preference Input 7. To encourage the user to participate, the system may receive an offer or virtual currency immediately after user completes this information disclosure. User 1 may also receive virtual currency (9), and/or may continue gaming/entertainment (8). Surveys may appear before, during, or after app/game play. Timers may be attached to the User l 's information disclosure step, so that even if User Fs information disclosure is not completed, User 1 is able to return to app/game play after the timer expired (when a timer is used, User 1 may return to app/game play immediately after completing the information disclosure - even if the timer has not yet expired).
  • User 1 selects an app/game or item for entertainment (8). User 1 plays the app/game or views/hears the entertainment. User 1 has multiple of choices of apps/games.
  • WESTV237492833.4 [00200] During User l 's interaction with the system, User 1 is presented with an offer (offer to user: offer redemption interface) (10). In one embodiment, the offer is a binary yes/no offer. User 1 may also be presented with virtual currency (9), User 1 has the opportunity to redeem winnings of virtual currency right away or "Bank" winnings of virtual currency for use at a later time.
  • the virtual currency may be used for "Offer Wheel Spins” (1 1) or for offer for goods/services/cash (10). Each "Offer Wheel Spin” results in the user being presented with an offer.
  • This mini-system of the virtual currency (9), offer redemption interface (10), and Offer Wheel spin (1 1) are collectively labeled (12). User 1 may redeem virtual currency in the offer store for specific offers (13).
  • User 1 selects an offer, User 1 is directed to the external offer completion interface (14) or the internal offer completion interface (15).
  • the external offer completion interface (14) directs User 1 to a third party merchant's system to complete the financial transaction necessary to make purchases.
  • the internal offer completion interface (15) gives the user the opportunity to, in the system, complete the financial transaction necessary to make purchases.
  • the system may deliver offers to a player's mobile phone via SMS or some other mechanism.
  • the offer can be a scannable image (e.g. bar code, 2D bar code, VR code, and the like) that allows redemption, or it could be cash for using the phone as a virtual wallet.
  • the system may also deliver offers directly to the user's game system (Xbox and the like) or provide a simple accept/decline interface for things such as free pizza to be delivered based on an in-app/game offer.
  • User 1 may utilize selected offers in virtual or real stores. User 1 may print out coupon or gift certificate for use in a "real" store (19) (e.g. one with a physical space for consumers; like a store in a mall).
  • a "real" store (19) e.g. one with a physical space for consumers; like a store in a mall).
  • An embodiment of the system can be implemented as computer software in the form of computer readable program code executed in a general purpose computing
  • WESTC37492833.4 environment such as environment 1400 illustrated in Figure 14, or in the form of bytecode class files executable within a Java.TM run time environment running in such an environment, or in the form of bytecodes running on a processor (or devices enabled to process bytecodes) existing in a distributed environment (e.g., one or more processors on a network).
  • Figure 14 may be scaled, so that there are muitiple/stacked/clustered processors and/or servers that are networked together to perform a set of functions.
  • a keyboard 1410 and mouse 1411 are coupled to a system bus 1418. The keyboard and mouse are for introducing user input to the computer system and communicating that user input to central processing unit (CPU 1413). Other suitable input devices may be used in addition to, or in place of, the mouse 1411 and keyboard 1410.
  • bi-directional system bus 1418 represents such I/O elements as a printer, A/V (audio/video) I/O, etc.
  • Computer 1401 may be a laptop, desktop, tablet, smart-phone, or other processing device and may include a communication interface 1420 coupled to bus 1418.
  • Communication interface 1420 provides a two-way data communication coupling via a network link 1421 to a local network 1422, For example, if communication interface
  • ISDN integrated services digital network
  • modem communication interface 1420 provides a data communication connection to the corresponding type of telephone line, which comprises part of network link 1421.
  • ISDN integrated services digital network
  • LAN local area network
  • communication interface 1420 provides a data communication connection via network link 1421 to a compatible LAN.
  • Wireless links are also possible, in any such implementation, communication interface 1420 sends and receives electrical, electromagnetic or optical signals which carry digital data streams representing various types of information.
  • Network link 1421 typically provides data communication through one or more networks to other data devices.
  • network link 1421 may provide a connection through local network 1422 to local server computer 1423 or to data equipment operated by ISP 1424.
  • ISP 1424 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the "Internet" 14214
  • Internet 14214 Local network 1422 and internet 14214 both use electrical,
  • the signals through the various networks and the signals on network link 1421 and through communication interface 1420, which carry the digital data to and from computer 1400, are exemplary forms of carrier waves transporting the information.
  • Processor 1413 may reside wholly on client computer 1401 or wholly on server 14214 or processor 1413 may have its computational power distributed between computer 1401 and server 14214.
  • Server 14214 symbolically is represented in FIG. 14 as one unit, but server 14214 can also be distributed between multiple "tiers".
  • server 14214 comprises a middle and back tier where application logic executes in the middle tier and persistent data is obtained in the back tier.
  • processor 1413 resides wholly on server 14214
  • the results of the computations performed by processor 1413 are transmitted to computer 1401 via Internet 14214, Internet Service Provider (ISP) 1424, local network 1422 and communication interface 1420. In this way, computer 1401 is able to display the results of the computation to a user in the form of output.
  • ISP Internet Service Provider
  • Computer 1401 includes a video memory 1414, main memory 1415 and mass storage 1412, all coupled to bi-directional system bus 1418 along with keyboard 1410, mouse 141 1 and processor 1413.
  • main memory 1415 and mass storage 1412 can reside wholly on server 14214 or computer 1401, or they may be distributed between the two.
  • Examples of systems where processor 1413, main memory 1415, and mass storage 1412 are distributed between computer 1 01 and server 14214 include thin-client computing architectures and other personal digital assistants, Internet ready cellular phones and other Internet computing devices, and in platform independent computing environments,
  • the mass storage 1412 may include both fixed and removable media, such as magnetic, optical or magnetic optical storage systems or any other available mass storage technology.
  • the mass storage may be implemented as a RAID array or any other suitable storage means.
  • Bus 1418 may contain, for example, thirty-two address lines for
  • the system bus 1418 also includes, for example, a 32-bit data bus for transferring data between and among the components, such as processor 1413, main memory 1415, video memory 1414 and mass storage 1412. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.
  • the processor 1413 is a microprocessor such as manufactured by Intel, AMD, Sun, Arm Holdings, etc. However, any other suitable microprocessor or microcomputer may be utilized, including a cloud computing solution.
  • Main memory 1415 is comprised of dynamic random access memory (DRAM).
  • Video memory 1414 is a dual-ported video random access memory. One port of the video memory 1414 is coupled to video amplifier 1419. The video amplifier 1419 is used to drive the cathode ray tube (CRT) raster monitor 1417.
  • Video amplifier 1419 is well known in the art and may be implemented by any suitable apparatus. This circuitry converts pixel data stored in video memory 1414 to a raster signal suitable for use by monitor 1417.
  • Monitor 1417 is a type of monitor suitable for displaying graphic images.
  • Computer 1401 can send messages and receive data, including program code, through the network(s), network link 1421, and communication interface 1420.
  • remote server computer 14214 might transmit a requested code for an application program through Internet 14214, ISP 1424, local network 1422 and communication interface 1420.
  • the received code maybe executed by processor 1413 as it is received, and/or stored in mass storage 1412, or other non-volatile storage for later execution.
  • the storage may be local or cloud storage.
  • computer 1400 may obtain application code in the form of a carrier wave.
  • remote server computer 14214 may execute applications using processor 1413, and utilize mass storage 1412, and/or video memory 1415.
  • the results of the execution at server 14214 are then transmitted through Internet 14214, ISP 1424, local network 1422 and communication interface 1420.
  • computer 1401 performs only input and output functions.
  • Application code may be embodied in any form of computer program product.
  • a computer program product comprises a medium configured to store or transport computer readable code, or in which computer readable code may be embedded.
  • Some examples of computer program products are CD-ROM disks, ROM cards, floppy disks, magnetic tapes, computer hard drives, servers on a network, flash memory devices, and carrier waves.

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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention se rapporte à un système qui permet de combiner un marketing personnalisé à la satisfaction fournie à l'utilisateur par des jeux et des applications présentées sous la forme de jeux. Le système selon l'invention constitue une solution unique pour présenter des offres à la façon d'un divertissement, avec la capacité mise en œuvre par ordinateur de personnaliser lesdites offres. Le système selon l'invention comprend un module d'apprentissage qui recueille auprès de chaque utilisateur des données et des informations relatives au comportement et qui individualise la publicité, les offres, voire même l'utilitaire de l'application ou le jeu, à l'intention de l'utilisateur. Le système permet de créer au fil du temps une interaction avec l'utilisateur qui donne lieu à des possibilités de consommation plus susceptibles d'être acceptées par ce dernier.
EP12828256.3A 2011-08-30 2012-08-29 Procédé et appareil de marketing personnalisé Withdrawn EP2751766A4 (fr)

Applications Claiming Priority (2)

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US201161575888P 2011-08-30 2011-08-30
PCT/US2012/052936 WO2013033266A1 (fr) 2011-08-30 2012-08-29 Procédé et appareil de marketing personnalisé

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EP2751766A1 true EP2751766A1 (fr) 2014-07-09
EP2751766A4 EP2751766A4 (fr) 2015-03-25

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100004993A1 (en) * 2008-07-02 2010-01-07 Ann Cameron Troy Intelligent multi-media player
US20100228617A1 (en) * 2008-03-03 2010-09-09 Wildfire Interactive, Inc. Providing online promotions through social media networks

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100228617A1 (en) * 2008-03-03 2010-09-09 Wildfire Interactive, Inc. Providing online promotions through social media networks
US20100004993A1 (en) * 2008-07-02 2010-01-07 Ann Cameron Troy Intelligent multi-media player

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of WO2013033266A1 *

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