Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.

Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

For at least a selected class attribute of the multiple class attributes, one or more bias metrics are determined that estimate a degree to which a particular workflow (having a set of processing stages) is biased in association with the class attribute. Each user of a set of users is associated with a set of user data to be processed by the particular workflow. At least one of the set of processing stages includes executing a machine-learning model. It can be detected that a bias-mitigation option corresponding to a specific class attribute has been selected. For each of at least two of the set of processing stages: a de-biasing technique is selected; and the processing stage is modified by applying the de-biasing technique. A modified version of the particular workflow (which includes the modified processing stages) is applied to each of a set of input data sets.

Processor(s) of a client device can: analyze one or more features of an electronic resource that is under consideration for solicitation to a user; determine a notification likelihood that the user will access the electronic resource in response to an unsolicited notification of the electronic resource being output to the user; determine a baseline likelihood that the user will access the electronic resource without being solicited; compare the notification likelihood with the baseline likelihood; and cause, based on the comparing, the unsolicited notification to be output to the user. In some implementations, determining the notification likelihood and/or the baseline likelihood is based on applying data associated with the electronic resource as input across a machine learning model to generate output indicative of the notification likelihood and/or the baseline likelihood. In other implementations, determining the notification likelihood and/or the baseline likelihood is based on past behavior or preference(s) of the user.

Electronic correspondence that includes one or more promotions may be generated for presenting to a consumer. In order to determine whether to present the electronic correspondence to the consumer, the promotions included in the electronic correspondences may be analyzed in terms of a probability the consumer will accept the promotions, a relevance level between the promotions and attributes of the consumer, a relevance level between the promotions and the consumer, a relevance level between the promotions and a set of goals or rules, among other similar terms. After the analysis, a determination may be made whether to send the electronic correspondence to the consumer. Similarly, the analysis may compare multiple electronic correspondences, and determine, based on the comparison, which of the multiple electronic correspondences to send to the consumer.

A system and methods for delivering targeted marketing offers to consumers during a session with an online (web-based) Internet portal, particularly suitable for online banking portals of financial institutions. An offer management system receives information corresponding to an advertising campaign of an advertiser corresponding to terms of a targeted marketing offer to be provided to a consumer accessing the online portal, and provides advertising campaign data corresponding to the targeted marketing offer and to an offer-triggering event to an offer placement system. An offer placement system receives the advertising campaign data, determines the occurrence of the offer-triggering event by a consumer during an online session with the online portal, and delivers information corresponding to the targeted marketing offer to the consumer. In response to the offer-triggering event, such as display of a list of transactions, the predetermined targeted marketing offer is delivered to the consumer during the online session.

A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

In general, in one aspect, a method includes compiling user interaction statistics for a set of content items displayed in association with a first target media document having a non-textual portion, at least some of the content items associated with one or more keywords, based on the interaction statistics, associating the first target media document with at least some of the keywords associated with the content items, and based on a common attribute of the first target media document and a second target media document having a non-textual portion, associating the second target media document with at least some of the keywords assigned to the first target media document. Other aspects include corresponding systems, apparatus, and computer programs stored on computer storage devices.