A shopper valuation system may include a user device associated with a given shopper, and a shopper valuation server. The server may obtain historical purchase data associated with shoppers at a given retailer, apply a machine learning algorithm to determine a corresponding shopper value for each of the shoppers, and obtain historical purchase data associated with the given shopper at the given retailer. The server may apply the algorithm to determine a shopper value for the given shopper based upon the historical purchase data for the shoppers and, the corresponding shopper values for the shoppers, and update the machine learning algorithm based upon the shopper value for the given shopper. The server may also communicate a notification to the user device soliciting the given shopper to enroll in a retailer loyalty program for payment to the given shopper of the shopper value for the given shopper.

A printer system includes: a printer that acquires and transmits, to a management server, identification information for identifying a customer and that also acquires, from the management server, and outputs recommendation information for the customer; and the management server that generates and transmits, to the printer, recommendation information including position information, based on at least one of attribute information or action history information of a customer identified by identification information acquired from the printer.

Hybrid on-policy/off-policy techniques are provided for improving the estimation of quality (reward) of a control policy for decision making by combining the on-policy and off-policy data from multiple estimators into a single metric. In one aspect, a method for estimating a reward of a policy for decision making in a computer system includes: computing multiple reward estimates of the policy using estimators, wherein at least a subset of the estimators compute reward estimates with prediction intervals; and combining the multiple reward estimates using a combiner to produce a new reward estimate. Thus, some of the estimators might compute the reward estimates without prediction intervals. A method for estimating a reward of a policy when another one or more of the estimators compute reward estimates without prediction intervals is also provided.

A computer-implemented method includes: training a machine learning model with training data to obtain a long-term value model, wherein the training data comprises a plurality of series of temporally-sequenced user bubbling events, wherein each of the user bubbling events corresponds to a historical transportation query from a user device and a historical response including a historical discount signal from a server, and wherein the long-term value model is configured to predict long-term values for respectively applying different discount signals to a given transportation plan of given bubbling features; obtaining a plurality of bubbling features of a transportation plan of a user, wherein the plurality of bubbling features; determining a discount signal based at least on feeding the plurality of bubbling features to the long-term value model; and transmitting the discount signal to a computing device of the user.

A method of training a machine learning model to promote an item is provided. Acceptance activity of a first entity is monitored and a determination of a first likelihood that the first entity accepts a future offer is made. The acceptance activity and the first likelihood are input as training data for the machine learning model for training purposes. An offer having an acceptance window and a characteristic is received from a second entity for a first item associated with the first entity. A second likelihood that the first entity will accept the offer is determined and an acceptance window is adjusted based on the determination. When the acceptance window closes, the method searches for a second item associated with a third entity based having the characteristic where an offer for the second item is sent to the second entity on behalf of the third entity.

In accordance with some embodiments, an alternative system for processing a data packet received via a secure pharmacy prescription claim network is provided, in which data included in the data packet as received from a pharmacy system is modified prior to being routed to a PBM. The data packet may be modified such that a first BIN/PCN is replaced by a second BIN/PCN and a Consumer Price is added. The alternative system allows for a third party to act as a switchboard for prescription claims such that requests for prescription claims received from pharmacy systems are pre-adjudicated and data packets included in the requests are modified prior to being routed to a PBM selected by the third party.

Method for personalized marketing or advertising of products for purchase from retail stores. Generally, the method includes utilizing information monitoring devices to gather shopping activities of the persons, including product interaction information, to obtain an information analysis about the shopping activities of the persons, further includes tracking the persons utilizing information monitoring devices, and further includes providing the persons a communication to a retailer location at which the persons can purchase related products. Such communication to the persons further includes marketing/advertising information regarding the products, coupons regarding the products, promotions regarding related products, purchase options regarding the products.

An apparatus, computer program product, and method are provided for predictive recommendations of device rendered objects using one or more objective models. In the context of a method, the method generates, for each device rendered object of a plurality of device rendered objects, a multiple objective score. The method also determines a real-time adjustment factor based in part on a received objective adjustment signal and generates, for each device rendered object of the plurality of device rendered objects, an adjusted multiple objective score based on the real-time adjustment factor. The method also generate a ranked device rendered object set, selects a subset of the ranked rendered object set, and transmits the ranked device rendered object subset to a client device associated with the user object of the user object objective, the subset configured for rendering within the user interface area of the client device.

Various examples are directed to computer-implemented systems and methods for providing a unique market offer code and validation. A method includes generating an offer customized for an intended recipient, and sending the offer electronically to the intended recipient. The method further includes receiving a user selection of the offer, and displaying a landing web page on a graphical user interface (GUI) of a user device. A user selection of an action button on the landing web page is received, and a validation web page is displayed on the GUI, the validation web page including prompts for input of user information. The user information is evaluated to determine authenticity of the offer and to confirm identity of the user as the intended recipient. Upon determining that the user is approved for the offer, access is provided to the user to further information regarding the product.

Providing a purchase incentive to a mobile device based for example the user's location, predicted route of travel, and prior transactions. A tracking server computer determines a number of locations of a mobile device as it travels along a route and an associated timeframe when it is at each of the locations. The tracking server computer records the locations and associated timeframes in a location log and analyzes the location log to predict a subsequent location and associated timeframe that the mobile device will be at that location. The tracking server computer then determines a merchant proximate to the predicted subsequent location of the mobile device and generates a purchase incentive for use at the merchant and delivers the purchase incentive to the mobile device. In the alternative, the incentive may be generated by a merchant computer or the mobile device.