45 results about "E loyalty" patented technology

A system for providing context-specific item recommendations is provided, for example in support of customer loyalty programs. In examples, a contextual offer recommendation engine utilizes a deep neural network in an epsilon-greedy agent to implement a contextual multi-arm bandit. The contextual multi-arm bandit is used to explore optimal solutions regarding correspondence between offers and customers. The optimal solutions may represent customer-offer combinations which may be published to a campaign manager for display to a customer, e.g., via a retail server. The deep neural network may be continually and adaptively retrained an extended based on observed actions between customers and new or preexisting offers.

Various aspects disclosed herein are directed to intelligent advantage spin techniques implemented in wager-based games conducted on electronic gaming devices. The intelligent advantage spin techniques may be incorporated into wager-based slot games for offering players various types of re-spin opportunities via the use of Advantage Spin (AVS) credits, loyalty points, or game credits. During gameplay, the system provides real-time analysis of game outcomes, identifying strategic moments such as near-miss scenarios to dynamically present customized advantage spin offers to players. Players can leverage AVS credits to selectively re-spin individual reels and / or symbols. AVS credits may also be employed as promotional incentives, redeemable across different game themes or EGMs, and can persist across gaming sessions, different EGMs, different venues, etc. Integration with systems like TITO and player loyalty accounts ensures seamless credit management, allowing casinos to issue, track, and incentivize credits based on both gaming and non-gaming activities.

The present disclosure generally relates to the use of loyalty accounts, private label payment accounts, and general payment accounts using an electronic device with an electronic wallet. Various accounts are linked to the electronic device. In some examples, the electronic device is NFC-enabled. The electronic device may be used to provide loyalty account information and payment account information to a payment terminal, such as an NFC-enabled payment terminal.

A system for configuring a digital wallet application for implementing a casino player loyalty program includes a memory storing reward configuration data associated with digital wallet awards and a processor configured to: (a) provide an award type user interface for receiving player loyalty award types; (b) receive award type data corresponding to one or more selected player loyalty award types; (c) provide an award threshold user interface for receiving at least one indication corresponding to player loyalty award thresholds; (d) receive award threshold data corresponding to one or more selected player loyalty award thresholds; (e) provide an award trigger user interface for receiving player loyalty award triggers; (f) receive award trigger data corresponding to selected player loyalty award triggers; and (g) configure a rules engine for a mobile wallet based, at least in part, on the award type data, the award threshold data and the award trigger data.

Methods, system and devices for managing loyalty incentive schemes are provided. In particular, payment terminals are provided that each comprise an electronic screen for displaying information to a user relating to a payment transaction, data input means, including at least one payment instrument interface for interfacing with a customer payment instrument, and at least one telecommunication module. A terminal management server is provided for managing the operation of the payment terminals. An incentive management server is also provided, for managing incentives to be applied at the payment terminals.

The invention relates to the technical field of enterprise data intelligent management, in particular to a service optimization method and system based on multi-dimensional enterprise data fusion, and the method comprises the following steps: taking a unified social credit code as a main key, and combining an enterprise name and an operation address as auxiliary matching keys; industrial and commercial registration data, tax declaration data, energy consumption monitoring data, social insurance payment data and operation development data are fused; the method has the beneficial effects that a real-time calculation framework and an intelligent algorithm are adopted, the sudden risk of an enterprise, such as sudden energy consumption increase and tax decline, can be captured in time, and an early warning and service intervention mechanism is automatically triggered. Therefore, enterprises can quickly respond to market changes, the operation risk is reduced, and the competitiveness is improved. And by accurately sensing enterprise demands and service states, the system can realize optimal configuration and efficient utilization of the service resources. Therefore, the service cost can be reduced, the service quality can be improved, and the customer satisfaction and loyalty can be enhanced.

Examples provide a system, method, and computer storage device for automatically presenting a loyalty index for a user. A loyalty index is calculated using transaction details for a plurality of users for a time period. The calculation involves calculating a total entity transaction amount, a total user transaction amount, and an entity proportion for each entity per user. The exclusivity loyalty factor proportion is an average value of the entity proportion for each entity in the favorite entities list weighted by the total entity transaction amount of each user with that entity. The examples present each entity and corresponding exclusivity loyalty factor proportion as an index icon in a user interface and automatically move the index icons to a list in the user interface in descending order of exclusivity loyalty factor proportion.

A method, apparatus, and computer readable storage to implement an augmented game system. A player can play an online game and accumulate loyalty points without have to pay cash. The player can enter a physical casino and play an electronic gaming machine which can retrieve the player's information from the online game including the number of loyalty points the player has and any other incentives or game add-ons the player would be entitled to. The player would then play a physical game on the electronic game normally (by depositing cash and playing) but the game play would be augmented by virtue of the player having the loyalty points or other incentives or add-ons. For example, the game can be augmented by giving the player a better paytable.

A player loyalty kiosk, system, method, and program product provide a loyalty game machine game with an enhanced player interface. A display system of the player loyalty kiosk displays the gaming interface including an animated game effect in a first area of the display system with one or more moving elements. Responsive to a player input a player input rate measurement is adjusted based on a rate of player inputs over time. A velocity of a first one of the moving elements is adjusted based on the player rate input measurement and adjusting a conclusion of the animated game effect based at least in part on the movement of the adjusted moving element.

A method for exchanging loyalty points includes storing one or more user loyalty profiles including a user identifier and one or more merchant identifiers associated with a loyalty point total; receiving a transaction request from a computing system including at least a user identification, a merchant identification, and a transaction amount; identifying a user loyalty profile including a user identifier that matches the user identification; in response to identifying the user loyalty profile, determining the merchant identification matches one of the one or more merchant identifiers; determining the loyalty point total associated with the matching merchant identifier is lower than a loyalty point total required to cover the transaction amount; and exchanging a first amount of loyalty points associated with a different merchant identifier of the one or more merchant identifiers for a second amount of loyalty points associated with the merchant identifier that matched the merchant identification.

The invention relates to a public transportation and customized public transportation collaborative dynamic pricing method and system. The method comprises the following steps: acquiring and fusing passenger behaviors, a vehicle Internet of Things, an external environment, a competition situation and operation cost data; constructing a dynamic user portrait based on the fused data, carrying out clustering and grouping, and generating price sensitivity, time sensitivity, comfort preference and service loyalty portrait tags; predicting payment willingness of the user based on the portrait; a collaborative pricing and travel recommendation strategy for different groups is generated by taking the system state and the user group portrait vector as input through a reinforcement learning pricing engine; executing the strategy and carrying out differentiated ticket price adjustment and service recommendation; passenger feedback data are collected, and user portraits and reinforcement learning model parameters are updated. According to the invention, the conversion from static splitting pricing to dynamic collaborative pricing is realized, the problems of poor collaboration and lack of elasticity of the system are solved, and the revenue capability and resource allocation efficiency of the system are remarkably improved.

A computer-implemented method, a computer program product, and a computer system for phygital integration in commerce-based checkout experiences. A computer aggregates data from one or more personal devices of a user and data from one or more biosensors of the user. A computer trains a machine learning model on features extracted from aggregated data. A computer uses the machine learning model to predict purchase preferences of the user and to generate for the user personalized recommendations, promotions, and loyalty rewards. A computer generates notifications about the personalized recommendations, promotions, and loyalty rewards. A computer transmits the notifications to the one or more personal devices. A computer monitors interactions of the user with the personalized recommendations, promotions, and loyalty rewards. A computer uses information of the interactions to train the machine learning model for future prediction of the purchase preferences and future generation of the personalized recommendations, promotions, and loyalty rewards.

A method for changing operation modes includes receiving disaster information from trending data, a newsfeed, or an alert message; upon detecting at least one redirection trigger from the disaster information, configuring a loyalty system to operate in a redirection mode; receiving or accessing data associated with a transaction between a customer and a merchant; determining, from at least one of customer information and merchant information in the data associated with the transaction, a donation amount and a location associated with the transaction; when the loyalty system is configured to operate in a redirection mode, generating signals to cause accrual of at least a portion of the donation amount to a redirection account based on the location associated with the transaction; and generating signals to cause accrual of any remaining portion of the donation amount to one or more defined donation accounts based on charity catchment area parameters.

A system and method are provided for managing loyalty program accounts. The method is executed by a device and includes providing, from the device, access to a loyalty wallet application. The method also includes storing loyalty account information for a plurality of loyalty accounts in the loyalty wallet application and integrating a loyalty tracking application, operable from the device, with at least one transaction channel accessed via the device. The method also includes receiving transaction data from the at least one transaction channel; detecting, from the transaction data, that a transaction yet to be executed is eligible for a loyalty reward associated with one or more of the plurality of loyalty accounts; and modifying the transaction to use corresponding loyalty account information to obtain the loyalty reward. The method also includes enabling the modified transaction to be completed to obtain the loyalty reward.

Examples provide a system, method, and computer storage device for automatically presenting a loyalty index for a user. A loyalty index is calculated using transaction details for a plurality of users for a time period. The calculation involves calculating a total entity transaction amount, a total user transaction amount, and an entity proportion for each entity per user. The exclusivity loyalty factor proportion is an average value of the entity proportion for each entity in the favorite entities list weighted by the total entity transaction amount of each user with that entity. The examples present each entity and corresponding exclusivity loyalty factor proportion as an index icon in a user interface and automatically move the index icons to a list in the user interface in descending order of exclusivity loyalty factor proportion.

In a system and method for providing a points liability forecast, data associated with transactions related to a retail loyalty program based on points accumulated by each customer enrolled in the retail loyalty program is received and stored. One or more training sets of data is created based on the received and stored data. The one or more training sets are used to generate a machine-learning model that forecasts points liability. Input parameters related to retail loyalty program are received from aa user, for input to the machine learning model. Forecast parameters based on the input parameters are received, as output from the machine learning model. Finally, the forecast parameters are provided to the user via an interface.

A method, system and computer program for providing a loyalty engine for automated cause marketing. The method includes a loyalty program system administrator linking merchants and charities to the loyalty system. Members register with the loyalty engine through loyalty system which is accessible via the Internet. Once the merchants and the charities are linked and the members are registered, by operation of the loyalty engine, the merchants can create a customized loyalty program defined by a plurality of merchant defined rules for administering their loyalty program. The merchant defined rules instruct the loyalty engine as to which charities should receive in-kind donations in connection with one or more transactions by a member that is associated to the loyalty program. Once a loyalty system defined time period or donation threshold has been achieved, the loyalty system releases the donation in-kind to the specific charity. A system is also provided that consists of a server computer including theloyalty engine and the charity utility. The charity utility provides the functions of the method and is linked to a server computer that is connected to the Internet. The computer program of the invention is a web application that enables the members, charities, and merchants to access the functions of the invention via a web browser.

The present invention provides a loyalty extraction machine, wherein “quadratic multiform separation” (QMS) is modified and executed multiplicatively in an even generalized way. In each execution, the characteristic of one single membership is either enhanced or reduced. This process is performed in turn to each membership. Thus, every sample data (or element) receives multiple classification results. Then, the multiple classification results are collected and analyzed by an “eclectic classifier” to reach a final decision. The combination of the generalized QMS and the eclectic classifier therefore develops the loyalty extraction machine. Moreover, a label called “loyalty type” of the element is introduced to describe the effectiveness of membership recognition with respect to a training set.

A system and method are provided for managing loyalty program accounts. The method includes providing, from the device, access to a loyalty wallet application. The method also includes storing loyalty account information for a plurality of loyalty accounts in the loyalty wallet application and integrating a loyalty tracking application, operable from the device, with at least one transaction channel accessed via the device. The method also includes receiving transaction data from the at least one transaction channel; detecting, from the transaction data, that a transaction yet to be executed is eligible for a loyalty reward associated with one or more of the plurality of loyalty accounts; and modifying the transaction to use corresponding loyalty account information to obtain the loyalty reward. The method also includes enabling the modified transaction to be completed to obtain the loyalty reward. Notifications may be determined and sent using a machine learning system.

Systems and methods are disclosed for associating a player loyalty account of a player with a stored value account. The player can accumulate loyalty points over time. The accumulated loyalty points can be converted to value, such as cash or coupons, associated with the stored value account. The value can be accessed by the player through the use of a stored value payment vehicle associated with the stored value account. The value added to the stored value account can be restricted such that the use of the value is limited to particular merchants or particular types of transactions.

A method for implementing loyalty points as digital currency using blockchain includes: receiving first transaction data for a first transaction including a first recipient address associated with a blockchain wallet and a positive loyalty point amount; processing the received first transaction data for the first transaction to add a first new block to the blockchain that includes the first recipient address and the positive loyalty point amount; receiving, after a predetermined period of time, second transaction data for a second transaction including a second recipient address associated with the blockchain wallet and a negative loyalty point amount; and processing the received second transaction data for the second transaction to add a second new block to the blockchain that includes the second recipient address and the negative loyalty point amount.

Systems and methods that utilizes one or more attributes of a player retrieved from a gaming establishment player tracking system in determining one or more attributes of an additive manufactured asset created and distributed to the player.

The invention discloses a client loyalty and right dynamic adjustment realization method based on a telecommunication BSS system, and relates to the technical field of resource evaluation. Comprising the following steps: step 1, based on a telecommunication BSS system, collecting customer behavior data, designing a general event model, defining a behavior event type according to the general event model, storing a behavior event, verifying the behavior event, storing the behavior event for future reference if the verification is passed, and rejecting duplicate events; 2, an integral calculation engine is used for configuring integral rules and dynamically calculating integral, 3, a grade layering model is constructed, a corresponding evaluation rule is defined for each grade, and grade rapid evaluation is triggered according to an evaluation triggering mechanism, and 4, a right and interest matching mechanism is defined; 5, multi-channel display and real-time linkage of related service systems are carried out; and step 6, creating a life cycle model, defining loyalty life cycles of the customer by using the life cycle model, including a new network access period, an active period, a stable period, a silence period, a loss period and a regression period, and matching different incentive measures according to the loyalty life cycles of the customer.

A loyalty server enables customers to use a merchant's mobile loyalty and payment program by pairing a customer's loyalty account through use of temporary codes. A customer may login to the customer's loyalty and payment account using a loyalty application installed on a mobile device. The customer can request a temporary code from the loyalty server, and provide it to a POS system associated with a merchant. The POS system can send a request to the loyalty server to retrieve customer information corresponding to the temporary code. The POS system can receive the customer's information, and the customer then earns rewards for transactions conducted with the merchant. In some embodiments, the POS system conducts a transaction using rewards or coupons. In some embodiments, the loyalty server obtains and utilizes the geolocation of the mobile device and the merchant as a portion of the means for pairing.

The embodiments of the present application belong to the field of intelligent decision-making technology, and relate to a method for obtaining intervention measures for customers with high churn risk and related equipment. The method includes: extracting a feature subset related to the activity from the customer attribute features and dividing them into an intervention group and a control group; training a random forest Uplift model for each feature subset; predicting and evaluating the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect; constructing a personalized Uplift scoring matrix based on the comprehensive score of each user; and prioritizing the intervention measures for each customer based on the personalized Uplift scoring matrix to determine the optimal intervention measure. The present application can more accurately identify groups with high churn risk and take targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rate and enhance the overall competitiveness of the enterprise.

To accurately classify customer loyalty by product brand and easily visualize their characteristics. [Solution] The information processing system comprises a control unit. The control unit acquires purchase history information, including the number of purchases and the number of units purchased for each brand of a predetermined category of products for multiple customers over a predetermined period. Based on the number of purchases for each brand and the ratio of the number of units purchased for each brand to the total number of units purchased for all brands, the control unit determines the loyalty classification of each customer for each brand from at least a first customer classification, a second customer classification lower than the first customer classification, and a third customer classification lower than the second customer classification. In response to the brand selection by the user terminal, the control unit outputs a predetermined statistical chart to the user terminal based on the number of units purchased for each determined loyalty classification of that brand.

The invention provides a method and system for automatically classifying, auditing and uploading steel product warranty books, and belongs to the technical field of product quality management, and the method comprises the steps: building and storing a warranty book manual auditing and uploading rule maintenance base table; after the material warranty book is generated and the pre-examination is qualified, judging whether a material ordering standard code in the material warranty book is in the base table or not; when the material ordering standard code is maintained in the base table, outputting a prompt that the warranty book needs to be manually audited, waiting for receiving an audit result of manual audit, and uploading and publishing the warranty book after the audit result of manual audit is obtained; and when the material ordering standard code is not maintained in the base table, automatic auditing is carried out, and the warranty book is uploaded and published after the auditing is passed. According to the invention, related functions of an existing quality system are optimized, the preset rules are added by considering factors such as supply stability and user loyalty, and whether the product warranty book needs to be uploaded after manual auditing is automatically judged, so that the conformity of the warranty book is ensured, and manpower is liberated to the maximum extent.

The invention provides an insurance loyalty prediction method and system based on multi-dimensional customer behavior data, and the method comprises the steps: extracting key behavior characteristics and behavior correlation characteristics based on the multi-dimensional customer behavior data, and constructing a multi-dimensional input vector; constructing an insurance loyalty prediction model by taking the multi-dimensional input vector as input and the insurance loyalty score as output; wherein the insurance loyalty prediction model comprises a base model and a meta model; inputting a multi-dimensional input vector corresponding to the multi-dimensional behavior data of the to-be-predicted customer into the base model and the meta-model, and obtaining an insurance loyalty predicted value of the to-be-predicted customer according to the output and the weight of the base model and the meta-model; and outputting the corresponding execution strategy according to the insurance loyalty prediction value of the to-be-predicted customer, thereby effectively improving the accuracy and reliability of insurance customer loyalty prediction.