X Development LLC

Mineral Earth Sciences LLC

Kathleen Watson

Jie Yang

Yueqi Li

Philip Edwin Watson

Christian Ervin

Julia Watson

Implementations are described herein for edge-based real time crop yield predictions made using sampled subsets of robotically-acquired vision data. In various implementations, one or more robots may be deployed amongst a plurality of plants in an area such as a field. Using one or more vision sensors of the one or more robots, a superset of high resolution images may be acquired that depict the plurality of plants. A subset of multiple high resolution images may then be sampled from the superset of high resolution images. Data indicative of the subset of high resolution images may be applied as input across a machine learning model, with or without additional data, to generate output indicative of a real time crop yield prediction.

Edge-based crop yield prediction

A technique for dynamic generation of a therapeutic derivative story includes obtaining attribute data that describes characteristics of a content consumer along with situational details describing an emotional situation involving the content consumer. A relatability score for the therapeutic derivative story is determined. A content data structure (CDS) is selected. The CDS specifies story elements of a preexisting story. The story elements are associated with metadata constraints that constrain modification or use of the story elements. The metadata constraints indicate whether associated ones of the story elements are mutable story elements. One or more of the mutable story elements are adapted based on the attribute data or the situational details as constrained by the metadata constraints and to an extent determined at least in part by the relatability score to generate the therapeutic derivative story.

Derivative content creation using neural networks for therapeutic use

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Sunnyvale, CA, United States of America

Kathleen Watson