A value return index system is disclosed. The system value return index system comprises a user interface via a user client device on a computer network, the user interface including one or more fields to receive user data selections to display (1) a brand value landscape view wherein a brand value is selected by the user and brand competitors are compared as to brand value awareness and calculated value consideration or a brand landscape view wherein a brand is selected by the user and brand values are compared with respect to value awareness and calculated value considerations. The value return index system incorporates one or more servers on the computer network that are programmed to determine an expected purchase propensity score for users and brands by training a machine learning algorithm using a framework of actual purchase data and the user demographic data, brand data and the social media data relating to the brands to create a model for calculating a prediction value of likelihood of a user to purchase a brand.

Disclosed embodiments provide a framework to identify and recommend tasks that can be performed for the benefit of a member. Through this framework, a member is assigned with a representative that, over time, learns about the member's preferences and behavior, which can be used to recommend tasks that can be performed to reduce the member's cognitive load. Further, as the representative develops a relationship with the member over time, the representative can also curate experiences for the member and assist the member in achieving personal goals and ambitions.

A computing device may include a memory and a processor configured to cooperate therewith to operate a virtual share exchange (VSE) platform by establishing member sharing accounts on the VSE platform for respective members of the VSE platform for sharing payment of member healthcare bills across a plurality of the member sharing accounts, establishing healthcare provider accounts on the VSE platform for healthcare providers issuing the member healthcare bills, and receiving member healthcare bills issued by the healthcare providers and establishing a virtual bill account on the VSE platform for each member healthcare bill submitted for payment sharing. The virtual bill account may be externally addressable through a routing number and a unique account number associated therewith. The processor may further electronically transfer funds between the sharing accounts and to the healthcare provider that issued the member healthcare bill using the externally addressable routing number and unique account number.

A method of implementing sub-table replication starts with the processor detecting an update to an entitlements table. The processor performs filtering of a data table based on the update to the entitlements table. The data table including an entitlements column. The processor detects an update to the entitlements column and performs incremental replication of the data table by causing a version-based replication to be executed. Other embodiments are also described herein.

A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

This disclosure describes systems and methods for repositioning vehicles. An exemplary method includes obtaining a plurality of first signals corresponding to a vehicle and a plurality of second signals corresponding to supply-demand statuses in a plurality of neighboring areas of the vehicle; inputting the plurality of first and second signals into a trained neural network and obtaining, from the trained neural network, a plurality of action values for repositioning the vehicle to the plurality of neighboring areas respectively; determining, based on the plurality of action values, a plurality of probabilities for repositioning the vehicle to the plurality of neighboring areas respectively; determining, according to the plurality of probabilities, one of the plurality of neighboring areas for the vehicle to reposition to; and transmitting a signal to a computing device associated with the vehicle to reposition the vehicle to the one determined neighboring area.

A virtual local presence display apparatus, system and method is disclosed. Included are an extraction engine capable of automatically disassociating the virtual local presence from its production background; a first data feed of an actual background in which the displaying device resides; a background data feed of a plurality of optional backgrounds including at least the production background of the virtual local presence; a stream subject data feed of an extracted one of the virtual local presence extracted from the production background; an integrated data feed in which the virtual local presence and a background are integrated; a first processing having a receiver capable of receiving the data feeds and assessing a plurality of focal lengths to focal planes for the virtual presence in the production background, and in a selected one of the other backgrounds; and a second processing for causing displaying, on the display device, the virtual presence at a suitable location with regard to the focal planes in a selected one of the other backgrounds.

Systems, method and computer program products for presenting to a user a visualization of a vehicle dealer performance assessment based on dealer location and vehicle sales transaction data, where the assessment is based on geographically normalized metrics. Dealer location data and historical vehicle sales transaction data is collected by a in a vehicle data system from external data sources. Distances from dealers to geographical regions of interest are determined, and differences between these distances are normalized to produce competition zone indices for the geographical regions. The competition zone indices are then used to aggregate the geographical regions into different competition zones in which dealers of interest have corresponding levels of competitive advantage or disadvantage, normalized according to typical distances associated with transactions in the respective geographical regions.

Systems and methods for optimizing application experiences on devices in an enterprise while maintaining privacy compliance are provided. An enterprise model management service of the enterprise accesses, from a centralized software service, a model comprising one or more rules to be applied to an application. The model is provided to devices of a first cohort, whereby a rule of the model causes an action associated with the application to occur at each device. User metrics associated with the action at each device of the first cohort is aggregated. The user metrics indicate a result of the action at each device. The aggregated user metrics are analyzed, whereby the analyzing includes determining a second cohort at the enterprise. The model is then provided to devices of at least a portion of the second cohort. No user metrics are returned to the centralized software service thus maintaining privacy compliance.

One or more computer processors create a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event. The one or more computer processors generate a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences. The one or more computer processors present the generated virtual audience member in a location associated with the event. The one or more computer processors dynamically adjust a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.