A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include updating, dynamically, the at least one risk-reward score and identifying a recommended stage for the at least one advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the at least one advertisement to the user at the recommended stage.

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include identifying a recommended stage for the advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the advertisement to the user at the recommended stage.

A process for rewarding the audience of a signage device, the process providing for detecting an interest-based interaction of a person with the signage device; acquiring at least one information about the person; selecting a reward to attribute to the person according to the information; dispensing the reward to the person.

The operating method of a video display apparatus according to an embodiment of the present invention comprises the steps of: recognizing content by using automatic content recognition; displaying a questionnaire for personalized service when the number of use of the recognized content reaches a threshold; receiving a user response to the displayed questionnaire; and providing the personalized service on the basis of the questionnaire reflecting the received user response.

In embodiments, the present invention provides a method and system for managing playback of content delivered to a mobile device with a pause and resume functionality. The method and system including receiving delivered content on a mobile device, initiating playback of the delivered content, pausing playback of the content being delivered to the mobile device in response to an action and resuming delivery of the content upon a request.

A method that incorporates the subject disclosure may include, for example, providing a first digital assistant to engage in a first communication exchange with a user of the first communication device where the first digital assistant has a first digital assistant persona that controls information provided by the first digital assistant during communication exchanges, analyzing user input during the first communication exchange, and adjusting the first digital assistant persona based on one or more of the user persona, user deficiency, or promotional offers. Other embodiments are disclosed.

A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights.

Content presented to a user of an online system is presented along with an option allowing the user to view one or more reasons for presentation of the advertisement content to the user and one or more characteristics of the user satisfying targeting criteria associated with the content. For example, a description identifying one or more selected characteristics of the user satisfying the targeting criteria is presented along with the content. The online system selects the one or more characteristics presented along with the content by applying one or more rules to characteristics of the user satisfying targeting criteria included in the content. The rules may account for accuracy of models used to determine whether the user has characteristics, revenue received by the online system from presenting content associated with targeting criteria identifying various characteristics, or prevalence of characteristics among users or targeting criteria of content.

Disclosed are a system, apparatus, and method for prioritizing and scheduling of notifications to a user on a user device based on the user's behavior profile and user state. The method includes collecting user related data by a plurality of data collectors. The data may be collected by the user's device and a server connected to the user's device. The data collected by the user's device and the server may be different. The collected data is analyzed to generate user states data and user behavior data. The prioritizing and scheduling of the messages is done based on the user states data and the behavior data.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.