Abstract: Systems, apparatus, methods, and non-transitory media for programmatically grouping consumers are discussed herein. Some embodiments may include a system configured to provide to shared promotion redemptions. The system may include a merchant device with processing circuitry configured to associate multiple consumer accounts with a point-of-sale order. Based on receiving redemption data from a consumer device associated with one of the consumer accounts, the system may be further configured to allow the consumer device to share a selected promotion. For example, the system may determine payment share amounts for each consumer account based on a transaction price of the point-of-sale order and the redemption value of the promotion selected for shared redemption.

Abstract: Embodiments are described for a system and method for providing a list of current offers from retailers to a user operating a mobile client device. Aspects of the method comprise analyzing a transaction history of the user to identify a vendor who has transacted with the user; determining a present geographic location of the user based on a location of the mobile client device; determining a location of the vendor proximate the user based on the transaction history and the present geographic location of the user; searching for a current promotion offered by the vendor; and facilitating the serving of the current promotion to the user through the mobile client device.

Abstract: A computer-implemented method for using machine learning to handle data in a computing system with improved efficiency can include obtaining a handling request associated with a data item, the handling request instructing the computing system to perform a handling operation with the data item, obtaining a trace log comprising one or more distributed trace items, the one or more distributed trace items including data from each of one or more services interacting with the data item, providing the trace log to a data characteristic prediction model including one or more machine-learned models, receiving, in response to providing the trace log to the data characteristic prediction model, one or more data characteristic predictions associated with the data item, and selecting a handling operation of a plurality of candidate handling operations based at least in part on the one or more data characteristic predictions.

Abstract: A signage control system comprises: a primary estimation circuitry to use signage-side images and surveillance-side images to estimate a person feature, attributes and behavior of each person captured in these frame images; a storage device to associate and store results of estimations of the person feature, the attributes and the behavior of each specific person; an estimation result linkage circuitry to use the person feature stored in the storage device to link the results of estimations based on the frame images from multiple ones of the cameras for the same person so as to generate a group of estimation results for each person; and a content change circuitry to change a content displayed on the signage to another based on the attributes of each person expected to be in a position where such person can visually recognize the content on the signage, and based on preceding behavior of such person.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may create a training data set from a first portion of the received data and may create a plurality of validation data sets from a second portion of the received data. For example, each validation data set may correspond to a respective one of the groups of data sources. The computing device may train, using the training data set, a plurality of machine learning models configured for synthesizing data. For instance, respective ones of the machine learning models may correspond to respective ones of the groups of data sources. Further, the computing device may validate the respective machine learning models using the respective validation data set corresponding to the respective group to which the respective machine learning model being validated corresponds.

Abstract: In some embodiments, a multi-tenant loyalty program configuration platform is provided for selective configuration of loyalty programs in a multi-tenant environment.

Abstract: Apparatus and associated methods relate to a rewarded viewing video distribution system configured to reward a user for viewed video content based on presenting video content to a user, assigning the user rewards generated based on the value of the video portion viewed, and automatically presenting the user with marketplace credit to spend the rewards. In some embodiments, the viewed video portion value may be determined as a function of a predetermined reward threshold. In an illustrative example, the predetermined reward threshold may be the video percentage viewed. Various implementations may employ a reward threshold based on advertisements viewed, permitting advertisement spending adapted as a function of verified advertisement views. Some examples may reward views using a video player plug-in. Some embodiments may permit a user to spend viewing rewards on affiliated partner services or products.

Abstract: A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

Abstract: A system and method for processing promotions in connection with digital purchasing. Data associated with a digital purchase request made by a consumer (the data comprising a token, an amount associated with the transaction, a promotional code for each item included in the digital purchase request associated with a promotion, a default transaction amount, and a single default promotional code) is received from a merchant server. The single default promotional code is assigned to all items included in the digital purchase request that are associated with promotions. Using the token, account information associated with the consumer is retrieved from a database. For each promotional code received, promotional information is retrieved. The retrieved promotional information is displayed. The digital purchase request is submitted for processing using the single default promotional code.

Abstract: A learning system including: a training data acquisition unit that, for each of a plurality of feature amounts obtained by converting time-series data on a predetermined period basis, acquires training data with which an event type in the predetermined period is associated; and a classifier learning unit that performs learning on a plurality of classifiers different from each other by using a feature amount group including one or more feature amounts corresponding to some or all periods out of the plurality of feature amounts included in the training data to perform learning on a classifier, which classifies the event type, for multiple times while changing a combination in the feature amount group, and the event type includes a first event type corresponding to a state where a target event to be classified occurs and a second event type corresponding to a state where the target event does not occur.

Abstract: A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including obtaining a machine learning model trained by using training data, the machine learning model predicting presence or absence of purchase actions or predetermined actions of users corresponding to feature information on the users, the training data including feature information on users and information indicating the presence or the absence of the purchase actions of commercial products or the predetermined actions, receiving input of a budget amount in an entry field displayed on a display in association with a user group including users including a common feature in the feature information, and displaying, in association with the budget amount on the display, feature information on the users included in the user group corresponding to the entry field.

Abstract: A settlement device includes an application reception unit that associates and stores a user ID for specifying a user and application information indicating an application to grant a privilege at the time of purchase of a target commodity in a storage unit when the user ID and the application information are acquired, a token generation unit that generates a settlement token and transmits the settlement token to a user terminal when a request for acquiring the user ID and a settlement code is received from a user terminal, a settlement information reception unit that is configured to receive the settlement information used to perform settlement of commodities, the settlement token, and privilege target commodity information for specifying a commodity which is a privilege target from a store terminal that is configured to read the settlement code according to the settlement token, and a privilege granting unit that is configured to grant the privilege corresponding to application information to the user when t

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: A blockchain-based digital advertising and marketing platform for Casinos, Lottery, and iGaming Operators which generally includes a private blockchain upon which both a supply-side platform and a demand-side platform along with a dynamic content generator software to operate using artificial intelligence and machine learning, business intelligence software and search engine technology, to provide an advertising rules service, gamified product offers, coupons and mailers utilizing augmented reality or virtual reality, an interactive game wall, connected to hardware and software interfaces of casino and lottery operators.

Abstract: Methods related to a model training scheme performed on a computing device are described. The computing device may include a display module and a memory module storing multiple prediction models and item data that includes data of multiple items. Example methods may include displaying, on the display module, a first representation of at least one modality of one or more items among the multiple items; detecting an activity of a user of the computing device in response to displaying of the at least one modality of the one or more items to generate user activity log data; and evaluating at least one first prediction model of the multiple prediction models based on the user activity log data to generate an evaluation value of the at least one first prediction model. The at least one first prediction model may be related to a prediction of the one or more items.

Abstract: A system for monitoring a vending machine is presented, the vending machine includes a controller and associated memory configured to store information associated with the condition of the vending machine. The vending machine also has a first short range transceiver configured to transmit the information associated with the condition of the vending machine, a first distance. A plurality of mobile devices each having a second short range transceiver and a first long range transceiver, the mobile devices configured to receive information associated with the condition of the vending machine and transmit it via the long range transceiver. A second long range transceiver is associated with a server coupled to the second long range transceiver.

Abstract: A processing system for fuel transactions analyzes transactions for symbols indicating a purchase amount and directs information about the purchase to a loyalty platform. The loyalty platform may identify and communicate in real time or near real time with a personal device associated with the purchaser of the fuel. A user interface of the personal device may allow the purchaser to review and select options for processing the fuel purchase using value from a loyalty program account. The selected option may be used to modify fuel purchase parameters prior to settlement of the transaction or simply add points to the user loyalty program account.

Abstract: Techniques for implementing unsupervised anomaly detection via supervised methods are provided. In one set of embodiments, a computer system can train an unsupervised anomaly detection classifier using an unlabeled training data set and classify the unlabeled training data set via the trained version of the unsupervised classifier, where the classifying generates anomaly scores for the data instances in the unlabeled training data set. The computer system can further construct a labeled training data set that includes a first subset of data instances from the unlabeled training data set whose anomaly scores are below a first threshold and a second subset of data instances from the unlabeled training data set whose anomaly scores are above a second threshold. The computer system can then train a supervised anomaly detection classifier using the labeled training data set.

Abstract: Methods, systems, and media for dynamically detecting mobile environment information are provided.

Abstract: This disclosure describes one or more implementations of a model segmentation system that generates accurate audience segments for client devices/individuals utilizing multi-class decision tree machine-learning models. For example, in various implementations, the model segmentation system generates a customized loss penalty matrix from multiple loss penalty matrices. In particular, the model segmentation system can generate regression mappings of model evaluation metrics for a plurality of decision tree models and combine loss penalty matrices based on the regression mappings to generate a customized loss penalty matrix that best fits an administrator's customized needs of segment accuracy and reach. The model segmentation system then utilizes the customized loss penalty matrix to train a multi-class decision tree machine-learning model to classify client devices into non-overlapping audience segments.