Transformation of inputs including beacon inputs, Global Positioning System (GPS) inputs, captured panorama inputs, user-penned descriptive inputs, and payment-amount-specifying inputs, via components, into outputs including user device POS configuration setting outputs and/or payment-gateway-directed authorization request outputs. Further, transformation of inputs including POS scanner inputs, POS keyboard inputs, and/or POS printer-directed inputs, via components, into outputs including compliance check outputs, tagged omnibus record outputs, SKU-UPC mapping outputs, and/or convergence/correlation outputs. Additionally, transformation of inputs including older limited-capability POS software image inputs and/or newer limited-capability POS software image inputs, via components, into outputs including update directive outputs. Functionality set forth includes allowing user devices to directly communicate with payment gateways, capturing and making use of scanner-obtained data and printer-destined data in a way that does not require code alternation of already-installed POS software, and allowing software of limited-capability POS devices to be updated without sending large, full-overwrite software images.

Systems and methods are described herein for inserting contextual advertisements into a virtual environment. While presenting a virtual environment, a media device receives speech input. The media device selects an advertisement from an advertisement database based on the speech. The media device then determines the current environment of the virtual environment and generates a virtual object that is related to the current environment. The media device overlays the selected advertisement over the generated virtual object and displays the generated virtual object in the virtual environment.

Systems and methods for tracking movement of individuals through a building receive, by one or more RF nodes disposed near an entrance to the building, RF signals from RF-transmitting mobile devices carried by persons near the entrance, capture an image of the persons while they are near the entrance, determine an identity and relative distance of each RF-transmitting mobile device from each RF node based on information associated with the RF signals received by that RF node, detect humans in the image, determine a relative depth of each human in the image, and assign the identity of each RF-transmitting mobile device to one of the humans detected in the image based on the relative distance of each RF-transmitting mobile device from each RF node and the relative depth of each human in the image, thereby identifying each individual who to be tracked optically as that individual moves throughout the building.

Disclosed herein are a content provisioning platform (150) and a system (100) that utilizes such a content provisioning platform (150). The content provisioning platform (150) includes: a selection engine (153) for selecting, in response to a request from a webpage (300) associated with an electronic transaction, inventory based on at least one of: a contextual attribute associated with the electronic transaction, a customer attribute associated with a user performing the electronic transaction, and a set of predefined rules. The inventory is selected from available inventory derived from at least one inventory provider during the electronic transaction. The webpage (300) is encoded in accordance with a content provisioning application programming interface (API) (140) associated with the content provisioning platform (150), the webpage including a contextual content display region (350) for displaying the selected inventory. The content provisioning API (140) is adapted to deliver the selected content to the contextual content display region (350).

Disclosed herein are a method and system that utilize a programmed content predictor to dynamically select electronic publishing content. In particular, the content predictor applies a selection model to select content for one or more selected webpages presented during an electronic transaction. The selection model utilizes a set of one or more machine learning models to select content based on calculated quality scores. The nature of the quality scores determined by the quality score model depend on the particular application. The predictor generates and populates a permutation quality table based on a set of selected content items and page variant, wherein the page variant defines locations of content positions within a webpage. The predictor then consumes the selection model to select a best permutation of content item-content position combinations to be returned for display on a webpage.

A system for virtually trying on products, including a first user system including a first user interface having an input device and screen view that is generated by software stored on a memory device of a first user transmitter/receiver, the first user system displaying products available to purchase in media and including a mechanism for virtually trying on products. A method of virtually trying on items, by a user interface displaying an item available for virtually trying on, a user selecting the item, and the user virtually trying on the item.

Systems and methods for wallet steering are disclosed. A wallet steering method in accordance with the present disclosure may comprise receiving a consumer identifier, such as a phone number, during a transaction. In response to receiving the consumer identifier, a consumer member profile is automatically created for the consumer identifier and data elements associated with the transaction and/or consumer are be stored in the consumer member profile. The consumer member profile may be built over time based on transactions associated with the consumer identifier. Customized offers which precisely and profitably steer consumer purchases from one business to another, one product/service to another and/or one payment tender to another may then be generated based on the consumer profile.

Methods and systems provide for a unified presentation of cross-platform content to a user visiting a platform. In one embodiment, the system connects a client device associated with a user to a first content platform; receives a request from the client device to present content to the user at the first content platform; receives content associated with one or more additional content platforms; determines a subset of the content to present to the user; standardizes the subset of the content in a format to be used at the first content platform; presents the subset of the content to the user at the first content platform; processes a set of unified cross-platform metrics for user events related to the user interacting with the subset of the content at the first content platform; and provides a report of the set of unified cross-platform metrics for the user events.

This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.