Apple Inc.

Facebook, Inc.

Microsoft Technology Licensing, LLC

Antoine Joseph Atallah

Eric Lance Wilson

Giovanni M Agnoli

Joshua C Weinberg

Joshua R Ford

Roozbeh Mahdavian

Ralf Herbrich

Bradley Ray Green

Alex David Weinstein

Jonathan Daniel Sorg

Iouri Y Poutivski

Andrew Rocco Tresolini Fiore

Jerry Ju

Iouri Y Putivsky

A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Determining relevant information based on user interactions

In one embodiment, a set of user groups of a social-networking system may be accessed. A first subset of the user groups may be determined for a particular user of the social-networking system, based on one or more filtering criteria. A number of recommendation-source processes may be applied to the first subset to determine a number of second subsets of the first set. Each recommendation-source process may represent a particular recommendation source. The second subsets may be combined into a list of user groups. The list of user groups may be ranked, and sent to the particular user.

Ranking and filtering groups recommendations

Processing a request is disclosed. A request associated with a first identifier is received. A selected request handler is selected among a first plurality of request handlers to process the request. The selection of the selected request handler is based at least in part on the first identifier. The request is processed using a second identifier included in the request. Processing the request includes using a local version of a data associated with the second identifier and stored in a storage managed by the selected request handler. The local version of the data has been updated using a centralized version of the data. The centralized version of the data has been determined using processing performed by a second plurality of request handlers. The selected request handler is included in the second plurality of request handlers.

Distributed request processing

Processing a prepared update is disclosed. A prepared update associated with a request that has been used by the sender to update a local version of a data associated with the sender is received from a sender. Based at least in part on an identifier included in the prepared update, a selected data handler is selected among a plurality of data handlers. The selected data handler is used to update a centralized version of the data at least in part by using the received prepared update. The centralized version of the data has been previously updated using a plurality of prepared updates received from a plurality of senders. The updated centralized version of the data is sent to update the local version of the data associated with the sender.

Distributed information synchronization

Architecture for comparing images by building an initial map from the average color and an inserted blackened area. Accordingly, a map can be built that is more information-rich and smaller, thereby making the system more efficient. The architecture employs a Kohonen neural network (or self-organizing map (SOM)) by guiding the learning of the SOM using characteristics of the images such as average color and a central area. A strong component of the average color of the image and the central area at the approximate center of the image are added to the uninitialized SOM, which allows related colors to converge toward the central area of the image. When input, the SOM organizes the color content of the image on a map, which can be used to compare the image with other images.

Image analysis through neural network using image average color

Methods, systems and computer-readable media for finding similarities between visual objects by evaluating user interactions with a collection of visual objects are provided. Using a neural network, human interactions with a collection of visual objects are evaluated to ascertain relationships or connections between visual objects. The relationship between visual objects indicates that the visual objects are similar. Once relationships between visual objects are identified, a user may select one or more visual objects and receive suggested visual objects that are similar to the one or more visual objects selected by the user.

Growing community of inventors

Bellevue, WA, United States of America

Antoine Joseph Atallah