Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Jianfeng Lin

Xiao Tu

Patrick Jee-An Poon

Yi Sun

Fei Xiong

Yikai Ni

Xiangdong Qian

Zhen Zuo

James Stevenson

Michinari Momma

Dheepikaa Natarajan

Phani Harish Wajjala

Nabajyoti Patowary

Gaurav Dasgupta

Vikash Pansari

Jinmiao Fu

Wenbo Wang

Lixi Wang

Deqiang Meng

Systems and techniques are generally described for determining similar content. First embedding data may be generated for first content based at least in part on a specified similarity task. A first query comprising the first embedding data may be generated and sent to a first database partition. A first search result representing second content may be determined using the first embedding data. A second query comprising the first embedding data may be generated and sent to a second database partition different from the first data base partition. A second search result representing third content may be determined using the first embedding data. In some examples, output data comprising at least one of the first search result and the second search result may be generated. The output data may represent content classified as similar to the first content.

Content similarity determination

Systems, methods, and computer-readable media are disclosed for systems and methods for automated detection and extraction of nutrition information for food products. Example methods may include identifying at least a first image comprising nutritional data, generating a second image including a first portion of the first image, and generating a third image including a second portion of the first image. Some embodiments may include generating a fourth image using the second image, generating a fifth image using the third image, determining, using at least one of the second image and the fourth image, the first text, determining, using at least one of the third image and the fifth image, the second text, and causing at least a portion of the first text and a portion of the second text to be stored in association with a product identifier.

Automated detection and extraction of nutrition information for food products

A facility for adapting the prediction of ink is described. In some examples, the facility receives information about a spatial movement by a user. On the basis of the received information, the facility predicts future spatial movement by the user, and generates an ink stroke that reflects both the spatial movement described by the received information and at least a portion of the predicted future spatial movement. The facility enforces against the generated ink stroke a limit that has the effect of controlling the area of a portion of the ink stroke corresponding to the at least a portion of the predicted future spatial movement, and causes the generated ink stroke, subject to the enforcement of the limit, to be displayed.

Adaptive ink prediction

Systems and methods are provided for improving the latency for display of ink during user creation of ink content with an object, such as a stylus, mouse, finger (or other touch input), or other drawing device. In order to reduce or minimize the time for display of ink content created by a user, aspects of the technology described herein generate predictive wet ink that can be displayed with actual wet ink. The predictive wet ink is calculated by extending an active ink segment a predictive distance. The predictive distance is intended to be a distance between a termination ink point for an active wet ink segment and a tip of the writing object. In one aspect, the predictive distance is calculated by determining the current latency period on a touch device and the velocity of the writing object.

Wet ink predictor

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Bellevue, WA, United States of America

Jianfeng Lin