Apple Inc.

Securboration, Inc.

Carlos E Guestrin

Qi Shan

Srikrishna Sridhar

Samuel C White

Jeffrey P Bigham

Yucheng Low

Mingzhe Li

Xiaoyi Zhang

Ping Wang

Thorsten Gernoth

Onur Canturk Hamsici

Shawn R Scully

Shreyas V Joshi

Gustav M Larsson

Leon A Gatys

Kory R Watson

Haijie Gu

Karla P Vega

Lee Krause

Alice Zheng

James B Schneider

Bruce R McQueary

Craig T Hagan

Tim Muss

Zach Nation

Eric Wolfe

Brian Kent

Chris DuBois

Evan Samanas

Sethu Raman

Sean K Thornton

Peter M LaMonica

Craig S Anken

Calibration of eye tracking is improved by collecting additional calibration pairs when user is using apps with eye tracking. A user input component is presented on a display of an electronic device, detecting a dwelling action for user input component, and in response to detecting the dwelling action, obtaining a calibration pair comprising an uncalibrated gaze point and a screen location of the user input component, wherein the uncalibrated gaze point is determined based on an eye pose during the dwelling action. A screen gaze estimation is determine based on the uncalibrated gaze point, and in response to determining that the calibration pair is a valid calibration pair, training a calibration model using the calibration pair.

On-the-fly calibration for improved on-device eye tracking

A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Machine learning assisted image prediction

The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

User-interface for developing applications that apply machine learning

A method receives a first request from a client object at a device. The first request specifies a data source. In response to the first request, the method uploads data from the data source, stores the data as a plurality of first columns, and instantiates a first server object that provides access to the first columns. The method later receives a second request from the client object. The second request specifies a transformation of the data. In response to the second request, the method stores one or more additional columns and instantiates a second server object that provides access to the additional columns and one or more of the first columns. Each of the additional columns is constructed from the first columns according to the requested transformation, and each of the additional columns includes a plurality of data values all having the same data type.

Systems and methods for operating a server-side data abstraction layer

A method of optimizing graph operations is performed by a computing system. The method comprises: (1) receiving a first request to perform a first operation on a first graph, where the first graph comprises a set of vertices and a set of edges, each edge connecting a pair of vertices, and each vertex having one or more associated properties; (2) logging the first request, but not performing the first operation; (3) receiving a second request to perform a second operation; (4) logging the second request, but not performing the second operation; (5) receiving a query for data from the first graph, where the data includes property values for one or more vertices; (6) in response to the query: (a) generating a second graph by optimizing and performing the first and second operations; and (b) returning data responsive to the query, where the returned data is based on the second graph.

Systems and methods for optimizing performance of graph operations

Methods and systems for node and link detection in social network analysis. Interactive noise reduction allows reduction of the data set under analysis to enable substantially real time detection of links and nodes.

Methods and systems for node and link identification

Growing community of inventors

Seattle, WA, United States of America

Carlos E Guestrin