An information processing system that draws a virtual space and that draws multiple mobile media that are moveable in the virtual space and are respectively associated with multiple users. The multiple mobile media include a first mobile medium associated with a user of a first attribute and a second mobile medium associated with a user of a second attribute to whom a predetermined role is assigned in the virtual space, and the image processing system further draws the second mobile medium in a display image for a user of the first attribute or for a user of the second attribute in a manner identifiable from the first mobile medium.

Content can be delivered to devices via one or more networks based on clusters of attributes in some examples. As one particular example, a server can determine a first set of attributes corresponding to a cookie associated with an entity and determine a second set of attributes corresponding to a mobile advertiser identifier (MAID) associated with the entity. The server can generate a cluster of attributes for the entity based on the first set of attributes and the second set of attributes. The system can then receive audience definition criteria defining a target audience for content and determine whether the cluster of attributes satisfies the audience definition criteria. If the cluster of attributes satisfies the audience definition criteria, the server can designate the cookie and the MAID as belonging to the target audience to enable the content to be delivered to the entity.

Aspects of the subject disclosure may include, for example, a method that includes identifying, by a processing system including a processor, a first content source and a second content source as a ground truth set, training a machine learning model for use in determining a target audience score under a user-defined metric for a target audience of a content source not included in the ground truth set, and generating the target audience score using the trained model. Other embodiments are disclosed.

Described herein is a method and system for automated, context sensitive and non-intrusive insertion of consumer-adaptive content in video. It assesses ‘context’ in the video that a consumer is viewing through multiple modalities and metadata about the video. The method and system described herein analyzes relevance for a consumer based on multiple factors such as the profile information of the end-user, history of the content, social media and consumer interests and professional or educational background, through patterns from multiple sources. The system also implements local-context through search techniques for localizing sufficiently large, homogenous regions in the image that do not obfuscate protagonists or objects in focus but are viable candidate regions for insertion for the intended content. This makes relevant, curated content available to a user in the most effortless manner without hampering the viewing experience of the main video.

A system includes a computing platform having processing hardware and a memory storing an asset library and software code including a recommendation engine. The processing hardware executes the software code to receive, from a user, group generation data and a preferences profile of the user, the group generation data identifying a group including the user and another user, and to send, to the other user, an invitation to join the group. The processing hardware also executes the software code to receive, from the other user, one of an acceptance or a refusal of the invitation, obtain, in response to receiving the acceptance, a preferences profile of the other user, generate, using the preferences profiles of the user and the other user, a group preferences profile for the group, and identify, using the recommendation engine and the group preferences profile, one or more assets in the asset library for the group.

An automated order processing for order items registered in advance as commodities targeted for automated order service is performed. In the automated order processing, it is determined whether any container/package for the order items exists in the disposal box, based on internal status data of a disposal box or identification data on the contents of the container/package existing in the disposal box. If it is determined that any container/package for the order items exists in the disposal box, the number of discarded containers/packages for the order items existing in the disposal box is calculated. Then, it is determined whether the number of discarded containers/packages is equal to or larger than an upper threshold set according to a sales mode of the order items. If it is determined that the number of discarded containers/packages is equal to or larger than the upper threshold, ordering data of the order items is generated.

A unified ecommerce platform system that enables sellers to acquire in depth insight into their business market and consumers utilizing a centralized cloud-based platform with enhanced consumer purchasing and information gathering abilities and allows users enhanced granular security tools and information for purchase.

A system that analyzes a user’s communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

A method for presenting pre-approved and pre-underwritten offers to a customer may include: receiving targeting criteria based on at least one of current accounts with the financial institution, assets, creditworthiness, and credit risk for an offer for a financial product; identifying a target population of customers for the offer by applying the targeting criteria to a population of customers; reviewing each customer in the target population for underwriting for the financial product based on inferred income for each customer and accounts that each customer has with the financial institution, wherein the underwriting is performed before the financial product is offered; determining a channel to present the offer to one of the customers that passed underwriting; communicating the offer to the customer over the selected channel; and providing an accepted offer to a fulfilment engine, wherein the fulfilment engine initiates an account opening for the financial product.

A method for generating content for a ticker feed on at least one web site, wherein the method is implemented by a computer comprising a microprocessor, a computer readable medium having instructions stored thereon, the instructions executable by the microprocessor to at least: receive web site information related to at least one of content of the at least one web site having the ticker, advertisements appearing on the at least one web site, metadata; apply a predictive model to the at least one web site information to generate at least one probabilistic user profile; retrieve at least one product or service from at least one provider based on at least one probabilistic user profile; update the predictive model with user input values to refine the at least one probabilistic user profile; retrieve content for the ticker feed based on the at least one probabilistic user profile.