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Google llc (20250094838). Image Analysis by Prompting of Machine-Learned Models Using Chain of Thought

From WikiPatents

Image Analysis by Prompting of Machine-Learned Models Using Chain of Thought

Organization Name

google llc

Inventor(s)

Jason Weng Wei of Mountain View CA US

Dengyong Zhou of Redmond WA US

Xuezhi Wang of New York NY US

Dale Eric Schuurmans of Edmonton CA

Quoc V. Le of Sunnyvale CA US

Maarten Paul Bosma of Cupertino CA US

Ed Huai-Hsin Chi of Palo Alto CA US

Olivier Jean Andrè Bousquet of Zürich CH

Le Hou of South Setauket NY US

Charles Aloysius Sutton of Santa Clara CA US

[[:Category:Nathanael Martin Sch�rli of Mountain View CA US|Nathanael Martin Sch�rli of Mountain View CA US]][[Category:Nathanael Martin Sch�rli of Mountain View CA US]]

Nathan Kemp Sekiguchi Scales of Mountain View CA US

Augustus Quadrozzi Odena of San Francisco CA US

Sharan Ajit Narang of Mountain View CA US

Guy Gur-ari Krakover of Palo Alto CA US

Aakanksha Chowdhery of Santa Clara CA US

David Martin Dohan of San Francisco CA US

Aitor Lewkowycz of Mountain View CA US

Jacob Austin of New York NY US

Henryk Michalewski of Mountain View CA US

David Luan of Mountain View CA US

David J. Bieber of New York NY US

Anders Johan Andreassen of Princeton NJ US

Maxwell Isaac Nye of Mountain View CA US

Image Analysis by Prompting of Machine-Learned Models Using Chain of Thought

This abstract first appeared for US patent application 20250094838 titled 'Image Analysis by Prompting of Machine-Learned Models Using Chain of Thought

Original Abstract Submitted

an example technique for image analysis is provided. an example image analysis method includes obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. the example image analysis method includes inputting, to a machine-learned model, the instructive sequence and an operative image processing query that comprises image data, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. the example method can include generating, using the machine-learned model and responsive to the operative query, an operative image processing response that comprises an analysis of the image data.