A gaze initiated interaction technique for facilitating social networking in augmented reality. Methods are shown which allow users to interact with each other following an event of interlocking gaze. Methods are also shown which allows tagging using augmented reality filters following a gaze interlock. Methods are also introduced to allow an augmented reality target to determine themselves how they would like to be enhanced in augmented reality following gaze interlocks. This disclosure also introduces a device for social networking in augmented reality which comprises a gaze-interlock event detection circuit and a gaze-interlock event processing unit.

Embodiments provide computer apparatuses, computer systems and computer-executable methods for generating geographic zone information associated with a consumer. A method includes receiving a first geographic location associated with the consumer, and programmatically identifying a first geographic zone associated with the first geographic location. The method also includes programmatically generating a first importance score associated with the first geographic zone and associated with the consumer. The method also includes storing, on a non-transitory computer-readable medium, the first geographic zone and the first importance score associated with the consumer.

The embodiments of the present disclosure provide systems and methods for outbound forecasting, comprising receiving an initial set of solutions comprising receiving a prediction of a regional sales forecast indicative of a customer demand for each stock keeping unit (SKU) in each region, receiving a prediction of a correlation of one or more SKUs that will be combined in customer orders in each region, receiving a prediction of a size of customer orders in each region, wherein a customer order profile is simulated based on the predicted correlation and the predicted size, receiving an inventory stow model that is generated using at least one of open purchase orders or past customer orders; and, predicting a FC for managing outbound of each SKU based on the predicted regional sales forecast, the simulated customer order profile, and the inventory stow model, and modifying a database to assign the predicted FC to each corresponding SKU.

A multi-region risk relationship data store may contain electronic records representing a plurality of multi-region risk relationships and, for each multi-region risk relationship, an electronic record identifier and a set of multi-region attribute values including a plurality of hierarchical local risk relationship attribute values. A back-end application computer server may receive an indication of a selected risk relationship and display at least some of the associated multi-region attribute values. The server may receive adjustments to some of the multi-region or local risk-relationship attribute values, and displayed information may be automatically and dynamically configured based on the received adjustments and a standalone risk calculation unit. At least some local risk relationship attribute values may be replicated for multiple regions. When adjustments have been completed, the server may automatically assemble a multi-region risk relationship document.

A computer-implemented system and method are disclosed for facilitating dynamically-priced offer data between a restaurant and a consumer, the data reflecting dynamically-priced offers which are contingent upon the consumer first honoring in full their contractual promises to the restaurant before the restaurant is bound to honor its side of the deal. In an embodiment, the method and system enables a consumer to specify, in advance, a minimum amount of money the consumer contractually binds themselves to spend at a restaurant as well as the specific hour of the day of a specific day of the week of a specific week of the year the consumer promises to arrive at the restaurant or expects delivery of an order from a restaurant, in return for more of the goods or services the restaurant contractually, but contingently binds themselves to provide the consumer. Further disclosed is a system and method for dealing with instances where a consumer fails to meet their contractual obligation to the restaurant, upon which assurances the restaurant's bid was contingent. In an embodiment, a controller is provided for an authorized restaurant representative to use to access a data storage device in which is stored restaurant deal offers which may be triggered by consumer's Request For Bid (RFB). The same controller receives those RFB's from consumer and accesses the same data storage device as it attempts to present responsive bids from local area restaurants.

A method implemented by a determination engine is provided. The determination engine receives a media dataset comprising target piece music information, target piece audience information, corpus music information, corpus audience information, and corpus preference data. The determination engine determines a subset of the corpus music and preference information and determines at least one surprise factor of the subset of the corpus music and preference information across features at one of a plurality of orders. The determination engine learns a model that estimates a likelihood that time-varying surprise trends across the features achieves a preference level. The determination engine determines at least one surprise factor of the target piece music information across the features at the one of the plurality of orders and predicts, using the model, preference information using the time-varying surprise trends for the target piece music information across the features.

The present disclosure relates systems and methods for food recommendations. More particularly, it teaches a systems and methods that can provide personalized menu item recommendations through a nutrient-focused machine learning model for nearby restaurants in near real-time, in order to assist in the selection of dishes that match a user's persona. Using these systems and methods enable a user to see a personalized prediction score of how much the system predicts that a particular user would like a particular dish.

Various implementations directed to price time priority queue routing for transportation capacity units are provided. In one implementation, a method may include receiving origin location data and destination location data. The method may also include generating routes based on the origin location data and the destination location data. The method may further include determining virtual hubs along the routes, where the virtual hubs include a first virtual hub based on the origin location data and a second virtual hub based on the destination location data. The method may additionally include receiving travel cost data for the routes for geolocation exchange units. In addition, the method may include receiving market depth data for a geolocation exchange for the geolocation exchange units based on the routes. The method may also include selecting an optimized route of the routes for the geolocation exchange units based on an objective function.

In one implementation, a system for providing product suggestions to provide to a customer during a checkout process at a retail store includes a checkout configuration that includes a point of sale terminal, a scanner to scan products, a scanning preparation area where products to be scanned are positioned, and a customer-facing device. The system can further include one or more product sensors to capture and transmit product signals. The system can also include a product identification system that is programmed to identify the products contained in the scanning preparation area. The system can further include a product recommendation engine that is configured to identify contents of a customer cart from the products contained in the scanning preparation area; determine product recommendations based, at least in part, on the contents of the customer cart; and transmit the product recommendations to the customer-facing device for display to the user.

Implementations of the present specification disclose a method and a system for recommending a target transaction code setting region. The method includes the following: dividing a target region to obtain multiple sub-regions, where the multiple sub-regions include one or more label sub-regions with known target transaction code setting effects and one or more sample sub-regions with unknown target transaction code setting effects; obtaining an association feature between the multiple sub-regions; obtaining predicted effect values of setting a target transaction code in the one or more sample sub-regions by using a prediction algorithm based on at least estimated effect values of setting a target transaction code in the one or more label sub-regions and the association feature; and determining at least one recommended region for setting a target transaction code from the one or more sample sub-regions based on at least the one or more predicted effect values.