Sony Corporation

Microsoft Technology Licensing, LLC

Sony Group Corporation

Xiaojing Chen

Ming-Chang Liu

Ko-Kai Albert Huang

Ankit Gupta

Yang Chen

Souvik Ghosh

Jing Wang

John P Moore

Chencheng Wu

Haifeng Zhao

Haowen Cao

Jiashuo Wang

Henry Heyburn Pistell

Keyi Yu

Mira Thambireddy

Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

Flexible data security and machine learning system for merging third-party data

Implementations generally relate to determining the position of a tool tip of a surgical instrument and the orientation of the surgical instrument. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting a surgical tool in the at least one image frame. The method further includes determining a position of a tip of the surgical tool. The method further includes determining an orientation of the surgical tool.

Surgical tool tip and orientation determination

Implementations generally relate to determining tool handedness for surgical videos. In some implementations, a method includes receiving at least one image frame of a plurality of image frames. The method further includes detecting one or more objects in the at least one image frame. The method further includes classifying the one or more objects into one or more tool classifications, where the one or more objects are tools. The method further includes determining, for each tool, if the tool is assistive or non-assistive. The method further includes determining, for each tool, a handedness of the tool.

Tool handedness determination for surgical videos

Implementations generally relate to detecting dominant tools in surgical videos. In some implementations, a method includes receiving at least one image frame. The method further includes detecting one or more objects in the at least one image frame. The method further includes classifying the one or more objects into one or more tool classifications, where the one or more objects are tools. The method further includes determining a handedness of the one or more tools. The method further includes determining a dominant tool from the one or more tools based at least in part on the one or more classifications of the one or more tools and based at least in part on the handedness of the one or more tools.

Dominant tool detection system for surgical videos

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Santa Clara, CA, United States of America

Xiaojing Chen