WO2013173799A1 - Structures de récompense - Google Patents

Structures de récompense Download PDF

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Publication number
WO2013173799A1
WO2013173799A1 PCT/US2013/041714 US2013041714W WO2013173799A1 WO 2013173799 A1 WO2013173799 A1 WO 2013173799A1 US 2013041714 W US2013041714 W US 2013041714W WO 2013173799 A1 WO2013173799 A1 WO 2013173799A1
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WIPO (PCT)
Prior art keywords
reward
users
server system
user
implementations
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Ceased
Application number
PCT/US2013/041714
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English (en)
Inventor
Nathan Stoll
Andrew Holt
Andrew ELLERHORST
Melissa CROUNSE
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Luvocracy Inc
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Luvocracy Inc
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Publication date
Priority claimed from US13/769,181 external-priority patent/US20130317941A1/en
Priority claimed from US13/896,310 external-priority patent/US9799046B2/en
Application filed by Luvocracy Inc filed Critical Luvocracy Inc
Publication of WO2013173799A1 publication Critical patent/WO2013173799A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0609—Qualifying participants for shopping transactions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G06Q10/42—Determination of affinities or common interests between users
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G06Q10/46—Determination of level of influence of users within social networking services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G06Q10/48—Business processes related to social networking or social networking services using social graphs

Definitions

  • the disclosed implementations relate to the field of online commerce generally and to rewarding user participation in particular.
  • One way to differentiate from other online retailers is to provide rewards to users who engage and participate with the commerce system. Specifically, by rewarding a user for being active in a social referral system, users are encouraged to help customize and improve the commercial experience for all users. As such, it would be useful to have a reward structure to encourage the users of the system to help curate the large number of products, services, and vendors available to provide the best experience to other users.
  • the computer system is a participatory social commerce system.
  • a participatory social commerce system is one that provides participatory rewards to its members on an equal footing for all of their commercial actions taken within the system.
  • the actions in the world can be stored as an action -
  • these genealogies include records of past causal actions (actions or relationships that have influenced or caused the current action.). [0006] In some implementations, the genealogies also track users or products that are added to the system. When a user is invited to join the system, the inviting user would receive some credit for all downstream economic activity caused by the invited user.
  • the genealogies are extracted from pre-existing materials or relationships. For example, a user's email contact book is uploaded. An email thread shows the product's original recommender and any other users who passed the recommendation along. This can be user to assign rewards to the users.
  • a method for rewarding users in a social referral system in accordance with some implementations is disclosed.
  • the method is performed on a server system having one or more processors and memory storing one or more programs for execution by the one or more processors.
  • the server system stores genealogy data for a plurality of users of the computer system.
  • the server system detects a user interaction with the computer system.
  • the server system determines whether the detected user interaction is associated with a reward.
  • the server system determines, based on the stored genealogy data associated with the detected user interaction, a list of one or more users to receive at least a portion of the reward associated with the detected user interaction.
  • the server system then allocates the associated reward to at least one of the one or more listed users.
  • a server system for rewarding users in a social referral system has one or more processors, and memory storing one or more programs to be executed by the one or more processors.
  • the one or more programs include instructions for storing genealogy data for a plurality of users of the computer system.
  • the one or more programs further include instructions for detecting a user interaction with the computer system.
  • the one or more programs further include instructions for determining whether the detected user interaction is associated with a reward.
  • the one or more programs further include instructions for, in accordance with a
  • a non-transitory computer readable storage medium storing one or more programs configured for execution by a server system.
  • the one or more programs also include instructions for storing genealogy data for a plurality of users of the computer system.
  • the one or more programs further include instructions for detecting a user interaction with the computer system.
  • the one or more programs further include instructions for determining whether the detected user interaction is associated with a reward.
  • the one or more programs further include instructions for, in accordance with a determination that the detected user interaction is associated with a reward, determining, based on the stored genealogy data associated with the detected user interaction, a list of one or more users to receive at least a portion of the reward associated with the detected user interaction.
  • the one or more programs further include instructions for allocating the associated reward to at least one of the one or more listed users.
  • Figure 1 is a block diagram illustrating a client-server environment in accordance with some implementations.
  • Figure 2 is a block diagram illustrating a client system in accordance with some implementations.
  • Figure 3 is a block diagram illustrating a server system in accordance with some implementations.
  • Figure 4 depicts a block diagram of an exemplary data structure for a user profile database for storing information related to users of the server system in accordance with some implementations.
  • Figure 5 is a flow diagram illustrating the process for rewarding users in a social referral system in accordance with some implementations.
  • Figure 6 is a flow diagram illustrating the process rewarding users in a social referral system in accordance with some implementations.
  • a computer system operates a participatory social commerce system.
  • a participatory social commerce system is one that provides participatory rewards to its members on an equal footing for all of their commercial actions taken within the system.
  • FIG. 1 is a block diagram illustrating a client-server environment 100 in accordance with some implementations.
  • the client-server environment 100 includes one or more client systems 102-1 and 102-2, a server system 120, one or more vendors 170, and one or more third parties, all connected over a network 110.
  • the client system 102 includes one or more client applications 104 and a display 106.
  • the server system 120 includes a communication module 122, an interaction processing module 124, an interaction analysis module 126, a reward module 128, a genealogy building module 130, a product database 140, a genealogy database 150, and a user profile database 160.
  • the network 110 may consist of any one or more of any of a variety of networks, including local area networks (LAN), wide area networks (WAN), wireless networks, wired networks, the Internet, or any combination of such networks.
  • the client system 102 includes one or more client applications 104.
  • the one or more client applications 104 include, but are not limited to, a web browsing application for connecting to the server system 120.
  • the client system 102 also includes a display 106.
  • the client system 102 is a computing device with the display integrated into the device itself, such as a laptop, a smart phone, a personal computer, a tablet computer, or other computing device.
  • the display 106 is connected to, but not integrated into the client system. For example, desktop computer systems often do not have an integrated display and instead connect to a standalone display.
  • the client system 102 transmits an interaction 114 request to the server system 102.
  • an interaction request 114 is a request to the server system 120 to use one of the services provided by the server system 120.
  • requested interactions 114 include, but are not limited to: a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral, a request to establish trust with another user of the server system, and any responses to any requests from the server system 120.
  • the client system 102 sends requests to one or more third party and receives responses from those third parties.
  • the third parties are social networking services, such as Facebook or Linkedln.
  • the requests sent to a third party are recorded or logged by the third party and reflect to some degree the social network of the user of the client system 102.
  • the user of the client system 102 can authorize the server system 120 to access the information stored at the third party 180 for analysis.
  • the client system 102 sends communications to other users, either through the server system 120, through a third party (e.g., Twitter), or using a communications protocol like electronic mail.
  • a record of these communications is stored either locally at the client system 102 or remotely on a server.
  • a local address book and email program stores a record of outgoing and incoming email and the contact information for the users that are frequently contacted.
  • the client system 102 receives communications from the server system 120.
  • the server system 120 sends a message to the client system 102.
  • the message is a
  • the server sends the user a recommendation for a particular pair of shoes at a particular price.
  • the message is a request for a recommendation from the user associated with the sever system.
  • the client system receives a purchase confirmation message from the sever system 120.
  • a purchase confirmation message requests that the client system 102 to confirm a purchase.
  • the client system 102 receives a reward notification from the server system 120.
  • a reward notification is a communication from the server system 120 that informs a user that the user has received a reward from the server system 120 and identifies the type of the award and, if appropriate, the amount of the award.
  • the client system 102 receives a reward from the server system 120 (e.g., when the reward is a digital reward of some kind, like an application, digital media, digital currency, or the like).
  • the server system 120 includes a communication module 122, an interaction processing module 124, an interaction analysis module 126, a reward module 128, a genealogy building module 130, a product database 140, a genealogy database 150, and a user profile database 160.
  • the communication module is configured to send and receive communications over the network 110 to one or more client systems 102 and one or more vendors 170.
  • the communication module 122 handles communication with the one or more client systems 102 or with third part systems 180.
  • the communication module 122 receives an interaction request 114 from a client system.
  • the interaction request is one of a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral, a request to establish trust with another user of the server system, and any responses to any requests from the server system.
  • the communication modules 122 routes the received interaction request 114 to the appropriate module with the server system 120.
  • the communication module 122 conducts communication between one or more third party web-sites and the server system 120.
  • the communication module 122 has received information from a user of a server system 120 that allows the communication module 122 to access data stored on the third party (e.g., login information).
  • the communication module 122 then sends a request to one or more third party 180 for information stored by the third party.
  • the server system 120 only gathers additional information from third party with the express consent of a user.
  • the communication module 122 forwards received requests to the relevant systems at the server system 120. In some implementations, most interaction requests are forward to the interaction processing module 124. In some implementations, requests to add products to the product database 140 are transferred immediately to the product database 140. In other implementations, data received from a third party regarding a user's social activity on a third party system is forwarded to the interaction analysis module 126 for analysis.
  • the interaction processing module 124 receives an interaction request 114 from a user associated with a client system 102. In some implementations, the interaction processing module 124 receives an interaction request 114 from a user associated with a client system 102. In some
  • interaction requests include, but are not limited to: requested interactions 114 include:: a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral (e.g., an invitation to use the server system), a request to establish trust with another user of the server system, and any responses to any requests from the server system 120.
  • the interaction processing module 124 in response to receiving an interaction request 114, the interaction processing module 124 responds to the received interaction request 114 appropriately.
  • the interaction request 114 is a product purchase request and the interaction processing module 124 purchases the product on behalf of the user that sent the request.
  • the interaction processing module 124 receives a recommendation and stores it in a recommendation database.
  • the received interaction request 114 is a user referral request and the interaction processing module 124 sends the user invitation to a potential new user.
  • the interaction processing module 124 identifies which user sent the interaction request 114 to the server system 120. In some implementations, the interaction processing module 124 identifies which user sent the interaction request 114 to the server system 120. In some implementations,
  • the interaction processing module 124 determines the user that sent the interaction request 114 based on the user ID associated with the interaction request 114. In some implementations, the interaction processing module 124 requests the user profile of the requesting user from the user profile database 160. The interaction processing module 124 then sends the retrieved user profile and the interaction request to the interaction request module 126.
  • the interaction analysis module 126 determines, based on the specific interaction requested by the user, whether there is a reward associated with the requested user interaction.
  • specific interactions have pre- established rewards that are regularly associated with them. For example, the server system 120 grants a reward when a user refers another user to the server system.
  • the " rewards associated with interactions change based on user history, vendor promotions, and promotions from the server system 120.
  • users are granted a reward, or a larger than normal award, for the first time a user performs a certain type of interaction with the sever system.
  • the server system 120 grants a larger than normal reward when a user makes its first purchase or recommendation.
  • vendors 170 can instruct the server system 120 to run a promotion for a certain type of user interaction.
  • a user could be awarded a larger than normal reward when it purchases or recommends a product supplied by a specific vendor.
  • the server system 120 itself will run a promotion with larger than normal rewards.
  • users associated with the server system 120 have an associated level or rank.
  • users of the server system ( Figure 1, 120) are rewarded with points that count towards increasing a user's level or rank as the user interacts with the server system.
  • the level or rank of a user is a measure of the user's influence or status in the server system ( Figure 1, 120) (e.g., the number of people that trust the user or act on their recommendations.)
  • the reward given to a user is dependent on the level of the user. For example, users that are very influential (e.g., style makers) receive increased rewards.
  • rewards can be given in one or more of the following methods: cash or cash equivalents (e.g., gift cards), charitable gifts on behalf of a user, access to additional features, points in a reward or rank system, digital currency, digital media, applications, or any other suitable reward system.
  • rewards include donate-able value or in-kind goods. For example, a user may be rewarded with a $5 donation to a specific charity. In another example, a user may be rewarded with a free product from among the products they have recommended.
  • rewards can be given to users socially.
  • a user earns credit towards various levels or scores. These might be divested from the economic or real payouts the user can receive, or they might be linked, e.g., two action -> value mappings can coexist, one for social value and one for real value.
  • Certain actions may provide values in both mappings. For example, a user might achieve a level of "trust" in a category because enough people have made purchases based on its recommendations. A user might also achieve a label for having a broad set of categories in which they have
  • a tastemaker might receive a label or level based on how "far reaching” their recommendations have gone (they started a whole trend).
  • rewards can be given to users as currency of some kind. It could be given as credits towards additional commercial activity in the service (with real money value), value-in-kind (get any book a friend recommends for free), or cash delivered as a check or other cash equivalent.
  • being able to receive certain types of real rewards could be predicated on the completion of certain sets of tasks or sets of actions, or various social levels. For example, a user must have purchased at least one recommendation from a friend this month to receive real cash value for all of the recommendations the user made that the user's friends have purchased (these limits would make it impossible for certain members to free-ride on only one side of the ecosystem: everyone would need to participate).
  • the user could also have to achieve a level, e.g. "Professional" to qualify to receive cash for his or her activity.
  • rewards could also be presented in various "spiffed" formats that make it feel more fun to a user. For example, a user could receive "lottery tickets" that allow a user to unwrap the reward and receive a surprise
  • rewards are triggered for users based on qualitative criteria. For example, a participant associated with an outside brand could serve as a judge for a contest based on the user that has the best collection of recommendations.
  • the server system recognizes this determination based on qualitative criteria and then triggers a reward for the user or users who built it.
  • the server system gives rewards based on judgment actions (e.g., a user marking a product or recommendation as a favorite or liking a product, recommendation, or collection of recommendations) of users of the system. For example, the server system gives a reward to users who have over 1000 likes.
  • the server system ( Figure 1, 120) rewards the user with the most number of favorites during a given month.
  • rewards could also be given to a user by other users of the computer system, as part of their follow-on actions to the user's actions. This would make it more explicit socially (e.g., "buying you a thank you beer”.)
  • vendors or companies can partner with the server system to create rewards structures and fund them.
  • partner vendors or companies can choose a general goal or theme for the rewards and allow the server system to create specific reward structures. For example, a company like Nike can choose a theme like pre-marathon workout products and provide funding. The server system then creates rewards based on the goal theme, such as creating a reward for best "pre- marathon workout products" collection of recommendations or increased rewards for purchasing products that fall into that category.
  • the partner vendor or companies choose specific rewards for specific actions. For example, Nike can establish rewards for purchasing or recommending a new shoe line.
  • the partner vendors Users then are rewarded through the server system for purchasing a shoe from the new shoe line.
  • the server system records the interaction that results in a reward, delivers that information to the partner vendor or company, and the partner vendor or company delivers the reward to the user directly.
  • a reward module 128 determines a reward amount and type based on the type of interaction, the user's social rank or level, and other factors. For example, when a user with a high social rank purchases a product, the user may receive a larger reward than a lower social rank user. The reward module 128 determines that the established reward is site credit based on the price of the product. As a user with a high social rank, the user is given perhaps 1.5% of the value of the product (a user with a lower social rank might get perhaps 1.0% or 0.5%) as credit on the server system.
  • more than one user qualifies to receive a reward for a particular user interaction with the server system. 120.
  • the reward module 128 obtains a genealogy for the user interaction.
  • the obtained genealogy is associated with a particular user.
  • the obtained genealogy identifies users who have caused the current user to interact with the server system 120 and therefore have caused the reward to be generated.
  • a genealogy for Bob includes the series of users who issued invites to the server system prior to Bob being invited.
  • the genealogy is related to the user's interaction with the server system 120.
  • the genealogy then includes users whose actions played a part in the current user's interaction with the server system 120. For example, if a user purchases a product based on a recommendation, the genealogy associated with the recommendation includes the user who first recommended the product and any subsequent user who re- recommended the product.
  • a user receives a recommendation because of an explicit recommendation from another user.
  • the server system 120 recommends a product to a user based on the user's social connections recorded on the server system 120 or other social networking platform. If a recommendation is displayed to user A based on their social relationship with user B, user B is included in the genealogy of the recommendation.
  • the reward module 128 generates a list of users who are eligible to receive a reward based on the genealogy data.
  • the genealogy data is stored in a directed graph with nodes and edges. In this case, the reward module 128 selects only users who are within a predetermined graph distance (a predetermined number of edges) from the primary user.
  • users must have qualified to receive rewards prior to the interaction with the server system 120.
  • users qualify to receive rewards by taking a particular action, such as by making a first purchase.
  • the user needs only to register with the server system 120.
  • the reward module 128 divides the total reward among the listed users. In some implementations, the reward is split evenly between all the listed users. In other implementations, the reward is given mainly to the user that initiated the interaction with the server system 120 and less is given to the other listed users.
  • the credit given can be capped asymptotically (e.g., a total of X% of the economic event is given to the entire genealogy) such that the most causing user is given the most credit, and all of the upstream causal users are given a divided remainder (divided such that it cannot ever exceed the asymptote). E.g., the first level might get 3% of 6% of a $100 purchase.
  • the genealogy building module 130 collects information stored at the server system ( Figure 1 , 120) and other places, and uses it to build genealogies.
  • the most basic genealogy is the user invite genealogy, which would list, starting with the current user, the user who invited or referred that user to the server system 120.
  • a genealogy is built for a particular interaction. For example, a first user purchases a product based on a recommendation. The genealogy for that recommendation includes the user who first recommended the product and any users who re- recommended the product to the first user.
  • the genealogy building module 130 builds genealogies for users that describe the user's social network based on trust graph information stored at the server system ( Figure 1, 120).
  • the genealogy building module 130 builds a genealogy based on a social network data and communication data retrieved from a third party(e.g., Facebook or Twitter)
  • the product database 140 contains a plurality of product entries. Each product entry is associated with a single product and contains information related thereto.
  • the product entries store the unique identifier of the product (e.g., bar code, name and module number, SKU number, or other product identifier), the specifications of the product (size, color, capacity, etc), a list of vendors that can supply the product, the price of the product, and any other pertinent information.
  • different vendors will have different prices and thus the product entry in the product database 140 will store a list of vendors and a vendor specific price for each vendor. For example, if a product is available from four vendors, the product database 140 will store a separate price for each vendor.
  • the products in the product database 140 are added by partner vendors 160.
  • the products in the database 140 are added by users and recommenders in the server system 120.
  • the products database 140 includes information for each product, including, but not limited to, the price of the product, its specification (size, color, etc), the vendor or vendors from which it is available, whether the item is in stock, and whether it is available for delivery.
  • the vendor selection module 130 and the purchasing module 128 use the information stored in the product database 140 to purchase the selected product.
  • the product database 140 searches accessible catalogs or databases of vendors or other databases maintained by vendors that are available over a network 110 for the desired product, and updates the product database 140 based on information stored on accessible databases or in catalogs.
  • the information in the product database includes a time stamp or other date indication. Thus, if a product is on sale or otherwise time limited, the product database can notify users or make recommendations at the appropriate time.
  • the genealogy database 150 includes calculated genealogies. Genealogies are built by the genealogy building module 130, and then the completed genealogies are stored in the genealogy database 150. In some implementations, genealogies are updated as new interactions occur between the server system ( Figure 1, 120) and the client systems 102.
  • a genealogy database 150 further includes stored interaction data 152.
  • Stored interaction data is data recording all user interactions with the server system ( Figure 1, 120) including, but not limited to: a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral, a request to establish trust with another user of the server system, and any responses to any requests from the server system 120.
  • the user profile database 160 contains user profiles for users who have registered to use the service provided by the server system 120.
  • a user profile includes the name, ID, demographic information (gender, age, location, etc), purchasing history, social network information, and trust information.
  • the user profile database 160 includes trust graph data.
  • the trust graph data includes information describing a directed trust graph.
  • the trust graph includes nodes, which represent users, and edges connecting nodes, which represent the trust level between the two users represented by the two nodes that the edge connects.
  • the vendors 170 are commercial organizations with products to sell.
  • the vendors 170 can receive orders to purchase products from the server system 120 and fulfill those orders.
  • third party services 180 include a plurality of publicly available third party service. These services include, but are not limited to, social network services, messaging services, blog services, chat services, and webmail services.
  • FIG. 2 is a block diagram illustrating a client system 102, in accordance with some implementations.
  • the client system 102 typically includes one or more processing units (CPUs) 202, one or more network interfaces 210, memory 212, and one or more communication buses 214 for interconnecting these components.
  • the client system 102 includes a user interface 204.
  • the user interface 204 includes an associated display device 104 and optionally includes an input means such as a keyboard, mouse, a touch sensitive display, or other input buttons 208.
  • the display device 106 includes an audio device or other information delivery device.
  • some client systems use a microphone and voice recognition to supplement or replace the keyboard.
  • Memory 212 includes high-speed random access memory, such as DRAM,
  • Memory 212 may optionally include one or more storage devices remotely located from the CPU(s) 202. Memory 212, or alternately the non- volatile memory device(s) within memory 212, includes a non-transitory computer readable storage medium. In some implementations, memory 212 or the computer readable storage medium of memory 212 stores the following programs, modules and data structures, or a subset thereof:
  • an operating system 216 that includes procedures for handling various basic system services and for performing hardware dependent tasks
  • a network communication module 218 that is used for connecting the client system 102 to other computers via the one or more communication network interfaces 210 (wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
  • a display module 220 for enabling display of media content on a display 106
  • a browser application 224 for sending purchase requests 114 to a server
  • client data module(s) 230 for storing data related to the client system 102, including but not limited to: o client message data 232, including data representing messages to be sent to the server system ( Figure 1, 120) and messages received from the server system ( Figure 1, 120); and/or o user profile data 234, including information concerning users of the client system 102 such as a user profile, user preferences and interests, user contact information, and other information relevant to providing services to the user.
  • FIG. 3 is a block diagram illustrating a server system 120, in accordance with some implementations.
  • the server system 120 typically includes one or more processing units (CPUs) 302, one or more network interfaces 304, memory 306, and one or more communication buses 308 for interconnecting these components.
  • CPUs processing units
  • network interfaces 304
  • memory 306
  • communication buses 308 for interconnecting these components.
  • Memory 306 includes high-speed random access memory, such as DRAM,
  • Memory 306 may optionally include one or more storage devices remotely located from the CPU(s) 302. Memory 306, or alternately the non-volatile memory device(s) within memory 306, includes a non-transitory computer readable storage medium. In some implementations, memory 306 or the computer readable storage medium of memory 306 stores the following programs, modules and data structures, or a subset thereof:
  • an operating system 310 that includes procedures for handling various basic system services and for performing hardware dependent tasks
  • a network communication module 122 that is used for connecting the server system 120 to other computers via the one or more communication network interfaces 304 (wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
  • server application module(s) 314 for enabling the server system 120 to perform the functions offered by the server system 120, including but not limited to: o an interaction processing module 124 for receiving interaction requests,
  • an interaction analysis module 126 for completing the interaction, recording the interaction, and determining whether the interaction was associated with a reward
  • a reward module 128 for determining one or more users who have qualified to the reward and allocating the reward between them
  • a genealogy building module 130 for analyzing user's social network data and interactions with the server system to build genealogies for interactions and users
  • server data module(s) 330 for storing data related to the server system 120, including but not limited to: o user profile data 160 including user preferences and interests, user contact information, and user history, including past user purchases, searches, page views, previous product recommendations, previous product reviews, social connections of the user, preferred vendors, vendor reviews, and any other information related to the user; o genealogy data 150 including a database of connected and directional graphs that represent the causal links between users on the server system 120 and the actions the user takes; o product data 140 including information for products that may be purchased through the server system 120, including, but not limited to, the price of the product, its features (size, color, etc), recent changes in price, and the vendor or vendors from which it is available; and/or o user interaction data 152 including a database of previous user interactions with the server system ( Figure 1, 120),
  • Figure 4 depicts a block diagram of an exemplary data structure for a user profile database 160 for storing information related to users of the server system ( Figure 1, 120).
  • the user profile database 160 includes a plurality of user profiles 402-1 to 402-P, each of which corresponds to a user registered with the server system ( Figure 1, 120).
  • each user profile 402 contains a user profile ID 404, a user history 406, trust information 408, recommendations 410 made by the user, user contact information 412, and vendor preferences 414.
  • the user profile also includes a user's social rank.
  • user profile ID 404 is a value that uniquely identifies a specific user of the server system ( Figure 1, 120). In some implementations, this value is chosen by the user and is a user name. In other implementations, this value is assigned to the user by the server system ( Figure 1, 120) as part of the registration process.
  • the user history 406 stores a history of the user's past interactions with the server system ( Figure 1, 120) including past user purchases and purchase details, searches, page views, previous product recommendations, previous product reviews, and social connections of the user including previously recorded trust information for other users and/or social information received from social networking sites.
  • trust information 408 includes data describing the social connections of the user and includes a trust level for other users of the server system ( Figure 1, 120).
  • a trust level is a value representing the degree to which a user trusts the opinions and recommendations of another user.
  • trust information is explicitly submitted by the users; in other situations the server system ( Figure 1, 120) infers trust information from the actions of users; and in yet other situations trust information includes information from both user submissions and server inferences.
  • recommendation 410 data includes product purchases and product recommendations previously submitted by the user.
  • contact information 412 includes a list of contact information for contacting the user through a plurality of communication methods and information describing if and when the server system ( Figure 1, 120) should use that method.
  • the contact information 412 includes, but is not limited to, email addresses, phone numbers, Twitter handle/user name, social network ID, physical address and any other information that helps the server system ( Figure 1, 120) contact the user. The user may select that the server system ( Figure 1, 120) should never use social network messaging to contact to user and should use email address at all times except for the weekend and that a text message to a mobile phone should be used on the weekend.
  • the contact information 412 includes login and password information for one or more social networking services.
  • vendor preference data 414 includes data indicating a user's vendor preferences.
  • vendor preference data 414 includes vendors that the user has explicitly indicated as preferred vendors and vendors that the user has explicitly indicated as non-preferred vendors.
  • vendor preference data 414 is determined based on previous user behavior (e.g., vendors that the user has repeatedly selected).
  • a user's vendor preference is based on feedback or reviews from the user. Vendors that received positive reviews will be noted as potential favored vendors and vendors that receive negative reviews will be noted as potential disfavored vendor.
  • contact information stored for the user includes one or more ways to contact the user including, but not limited to, email addresses, phone numbers, Twitter handle/ user name, social network ID, physical address and any other information that helps the server system (Figure 1, 120) contact the user.
  • Figure 5 is a flow diagram illustrating the process for rewarding users in a social referral system in accordance with some implementations.
  • Each of the operations shown in Figure 5 may correspond to instructions stored in a computer memory or computer readable storage medium.
  • Optional operations are indicated by dashed lines (e.g., boxes with dashed-line borders).
  • the method described in Figure 5 is performed by the server system ( Figure 1, 120).
  • the server system ( Figure 1, 120) generates (502) genealogy data for one or more users associated with the server system ( Figure 1, 120).
  • generating genealogy data includes storing (504) previous
  • the server system determines (506) causal links between other users of the server system ( Figure 1, 120) and the actions of the other users of the server system ( Figure 1, 120).
  • the server system ( Figure 1, 120) generates genealogy graphs based on the determined causal links.
  • a causal link is a connection between two users, a user and an interaction, or two interactions where one action or user may be said to be at least partially responsible for the acts of a second user. For example, if user A refers user B to the server system ( Figure 1, 120), user A is at least partially responsible for any interactions user B takes while registered with server A.
  • the server system determines causal links based on analyzing data for at least the following user interactions: a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral, a request to establish trust with another user of the server system, and any responses to any requests from the server system 120.
  • the server system ( Figure 1, 120) generates genealogies by accessing and analyzing user relationships in forums/social media/social networking services. All this information is gathered to create genealogy graphs that are directed graphs showing connections between users and showing connections between actions (which ones caused other ones).
  • the server system ( Figure 1, 120) accesses (510) social network data associated with one or more users of the server system, and, based on the accessed social network data, generates (512) a social relationship graph.
  • the genealogy graphs are directed graphs.
  • the accessed social network data is trust graph data stored on the server system.
  • the accessed social network data is social network data from the computer system, one or more a third party social networking sites, or one or more social messaging sites.
  • the third party sites can include Twitter, tumblr, Facebook, or any other suitable service.
  • the server system ( Figure 1, 120) stores (514) genealogy data for a plurality users of the server system.
  • the server system ( Figure 1, 120) detects (516) a user interaction with the computer system.
  • the detected user interaction includes, but are not limited to: a product purchase request, a recommendation, a request for recommendations, a request to redeem a reward, a request to pre-establish purchase intent, a user referral, a request to establish trust with another user of the server system, and any responses to any requests from the server system 120 (517).
  • the server system ( Figure 1, 120) determines (518) whether the detected user interaction is associated with a reward. In accordance with a determination that the detected user interaction is associated with a reward, the server system ( Figure 1, 120) determines (520), based on the stored genealogy data associated with the detected user interaction, a list of one or more users to receive at least a portion of the reward associated with the detected user interaction. In some implementations, the reward is one of currency, credit on the computer system, or social based points.
  • Figure 6 is a flow diagram illustrating the process for rewarding users in a social referral system in accordance with some implementations. Each of the operations shown in Figure 6 may correspond to instructions stored in a computer memory or computer readable storage medium. Optional operations are indicated by dashed lines (e.g., boxes with dashed-line borders). In some implementations, the method described in Figure 6 is performed by the server system ( Figure 1, 120).
  • the server system ( Figure 1, 120) allocates (602) the associated reward to at least one of the one or more listed users. In some implementations, allocating the associated reward to at least one of the one or more listed users further includes the server system ( Figure 1, 120) establishing (604) a maximum reward amount. In some implementations, the server system ( Figure 1, 120) identifies (606) a first user from the one or more users identified in a genealogy as responsible for the action that caused the reward to be granted.
  • the server system ( Figure 1, 120) then allocates
  • the server system ( Figure 1, 120) allocates (610) the remaining reward to any other users in the one or more users, allotting portions based on the degree to which a respective user is responsible for the granting of the reward; wherein the sum of all the divided portions of the reward does not exceed the maximum reward amount.
  • the detected user interaction is the purchase of a product via the server system 120, and the server system ( Figure 1, 120), for each respective user listed in the stored geology, determines (612) whether the respective user has qualified to receive a reward from the server system. In some implementations, in accordance with a determination that the respective user has qualified to receive a reward from the server system, the server system adds (614) the respective user to the list of one or more users.
  • a user qualifies to receive a reward from the server system by performing one or more of purchasing a product from the server system, registering with the sever system, referring another user to the server system, or making recommendations to the server system.
  • the stored genealogy data is organized in a connected graph with nodes and edges, and only users within a predetermined distance of the source node qualify to receive a reward from the server system.
  • first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the present implementations.
  • the first contact and the second contact are both contacts, but the first contact and the second contact are not the same contact.
  • the phrase “if it is determined” or “if (a stated condition or event) is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting (the stated condition or event)” or “in response to detecting (the stated condition or event),” depending on the context.

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Abstract

Selon certains modes de réalisation, la présente invention concerne un procédé servant à récompenser des utilisateurs dans un système de consultation sociale. Le procédé est mis en œuvre sur un système serveur ayant un ou plusieurs processeurs. Le système serveur stocke des données de généalogie pour une pluralité d'utilisateurs du système informatique. Le système serveur détecte ensuite une interaction d'utilisateur avec le système informatique. Le système serveur détermine ensuite si l'interaction d'utilisateur détectée est associée à une récompense. Le système serveur détermine ensuite, selon une détermination du fait que l'interaction d'utilisateur détectée est associée à une récompense, sur la base des données de généalogie stockées associées à l'interaction d'utilisateur détectée, une liste d'un ou de plusieurs utilisateurs susceptibles de recevoir au moins une partie de la récompense associée à l'interaction d'utilisateur détectée. Le système serveur alloue ensuite la récompense associée à au moins un utilisateur dans la liste du ou des utilisateurs.
PCT/US2013/041714 2012-05-17 2013-05-17 Structures de récompense Ceased WO2013173799A1 (fr)

Applications Claiming Priority (30)

Application Number Priority Date Filing Date Title
US201261648582P 2012-05-17 2012-05-17
US201261648588P 2012-05-17 2012-05-17
US201261648578P 2012-05-17 2012-05-17
US201261648591P 2012-05-17 2012-05-17
US201261648564P 2012-05-17 2012-05-17
US201261648566P 2012-05-17 2012-05-17
US61/648,591 2012-05-17
US61/648,564 2012-05-17
US61/648,566 2012-05-17
US61/648,582 2012-05-17
US61/648,578 2012-05-17
US61/648,588 2012-05-17
US201261688655P 2012-05-18 2012-05-18
US61/688,655 2012-05-18
US13/769,181 US20130317941A1 (en) 2012-05-17 2013-02-15 Trust Graph
US13/769,181 2013-02-15
US13/896,318 2013-05-16
US13/896,310 US9799046B2 (en) 2012-05-17 2013-05-16 Zero click commerce systems
US13/986,607 US9875483B2 (en) 2012-05-17 2013-05-16 Conversational interfaces
US13/986,608 2013-05-16
US13/896,292 2013-05-16
US13/896,292 US10740779B2 (en) 2012-05-17 2013-05-16 Pre-establishing purchasing intent for computer based commerce systems
US13/896,310 2013-05-16
US13/986,612 US20130311337A1 (en) 2012-05-17 2013-05-16 Universal consumption service
US13/986,612 2013-05-16
US13/986,606 US20130311573A1 (en) 2012-05-17 2013-05-16 Progressively asking for increasing amounts of user and network data
US13/896,318 US20130311260A1 (en) 2012-05-17 2013-05-16 Reward Structures
US13/986,606 2013-05-16
US13/986,608 US20130311621A1 (en) 2012-05-17 2013-05-16 Universal Communications Infrastructure
US13/986,607 2013-05-16

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230274326A1 (en) * 2022-02-28 2023-08-31 Benjamin Yeung Systems for providing decentralized product and rewards delivery platform

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2484054A4 (fr) 2009-09-30 2014-11-05 Evan V Chrapko Systèmes et procédés d'analyse de données de graphe social pour déterminer une connectivité dans une communauté
US20110099164A1 (en) 2009-10-23 2011-04-28 Haim Zvi Melman Apparatus and method for search and retrieval of documents and advertising targeting
US20170358027A1 (en) 2010-01-14 2017-12-14 Www.Trustscience.Com Inc. Scoring trustworthiness, competence, and/or compatibility of any entity for activities including recruiting or hiring decisions, composing a team, insurance underwriting, credit decisions, or shortening or improving sales cycles
US9922134B2 (en) 2010-04-30 2018-03-20 Www.Trustscience.Com Inc. Assessing and scoring people, businesses, places, things, and brands
US12574307B2 (en) 2010-09-16 2026-03-10 Www.Trustscience.Com Inc. Computing cluster for providing virtual markers based upon network connectivity
US9578043B2 (en) 2015-03-20 2017-02-21 Ashif Mawji Calculating a trust score
US20170235792A1 (en) 2016-02-17 2017-08-17 Www.Trustscience.Com Inc. Searching for entities based on trust score and geography
US9679254B1 (en) 2016-02-29 2017-06-13 Www.Trustscience.Com Inc. Extrapolating trends in trust scores
US9721296B1 (en) 2016-03-24 2017-08-01 Www.Trustscience.Com Inc. Learning an entity's trust model and risk tolerance to calculate a risk score
US10180969B2 (en) 2017-03-22 2019-01-15 Www.Trustscience.Com Inc. Entity resolution and identity management in big, noisy, and/or unstructured data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070043583A1 (en) * 2005-03-11 2007-02-22 The Arizona Board Of Regents On Behalf Of Arizona State University Reward driven online system utilizing user-generated tags as a bridge to suggested links
JP2007249833A (ja) * 2006-03-17 2007-09-27 Fujitsu Ltd 手数料最適化方法および手数料最適化プログラム
KR100825204B1 (ko) * 2007-02-14 2008-04-28 송원영 상품별 또는 상품군별 다중 네트워크 구조를 활용한전자상거래 운영 방법
US20090319436A1 (en) * 2008-06-18 2009-12-24 Delip Andra Method and system of opinion analysis and recommendations in social platform applications
US20110131106A1 (en) * 2009-12-02 2011-06-02 George Eberstadt Using social network and transaction information

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010011235A1 (en) * 2000-01-29 2001-08-02 E-Net Co., Ltd. Apparatus for realizing personal shops in an electronic commerce business
KR20010076971A (ko) * 2000-01-29 2001-08-17 박규헌 지능형 장바구니 기능을 구비한 전자 상거래 시스템
JP2004326634A (ja) * 2003-04-28 2004-11-18 Hitachi Ltd ユーザ間の信頼関係を用いるお奨めデータ選択装置
KR100807264B1 (ko) * 2006-04-29 2008-02-28 강찬구 온라인 컨텐츠 마켓플레이스 시스템 및 운영방법
JP5108679B2 (ja) * 2007-10-22 2012-12-26 ヤフー株式会社 ユーザ推薦システム、ユーザ推薦方法、商品情報出力システム、商品情報出力方法およびプログラム
KR101624680B1 (ko) * 2009-03-03 2016-05-27 구글 인코포레이티드 소셜 네트워크의 사용자들에게 광고를 제공하기 위한 방법 및 시스템
JP2011170471A (ja) * 2010-02-17 2011-09-01 Nippon Telegr & Teleph Corp <Ntt> ソーシャルグラフ生成方法、ソーシャルグラフ生成装置、およびプログラム
KR20110117475A (ko) * 2010-04-21 2011-10-27 중앙대학교 산학협력단 웹 기반 소셜 네트워크에서의 신뢰도 및 평판 추론장치 및 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070043583A1 (en) * 2005-03-11 2007-02-22 The Arizona Board Of Regents On Behalf Of Arizona State University Reward driven online system utilizing user-generated tags as a bridge to suggested links
JP2007249833A (ja) * 2006-03-17 2007-09-27 Fujitsu Ltd 手数料最適化方法および手数料最適化プログラム
KR100825204B1 (ko) * 2007-02-14 2008-04-28 송원영 상품별 또는 상품군별 다중 네트워크 구조를 활용한전자상거래 운영 방법
US20090319436A1 (en) * 2008-06-18 2009-12-24 Delip Andra Method and system of opinion analysis and recommendations in social platform applications
US20110131106A1 (en) * 2009-12-02 2011-06-02 George Eberstadt Using social network and transaction information

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230274326A1 (en) * 2022-02-28 2023-08-31 Benjamin Yeung Systems for providing decentralized product and rewards delivery platform

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