公开(公告)号：US20210390492A1

公开(公告)日：2021-12-16

申请号：US17348588

申请日：2021-06-15

Applicant: SRI International

Inventor： Swati Dhamija ,  Amir Tamrakar ,  Nonye M. Alozie ,  Elizabeth McBride ,  Ajay Divakaran ,  Anirudh Som ,  Sujeong Kim ,  Bladimir Lopez-Prado

IPC: G06Q10/06 ,  G06Q10/10 ,  G06K9/00 ,  G06N3/04 ,  G06N20/00

Abstract: In some examples, a computer-implemented collaboration assessment model identifies actions of each of two or more individuals depicted in video data, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, first behaviors at a first collaboration assessment level, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, second behaviors at a second collaboration assessment level different from the first collaboration assessment level, and generate and output, based at least on the first behaviors at the first collaboration assessment level and the second behaviors at the second collaboration assessment level, an indication of at least one of an assessment of a collaboration effort of the two or more individuals or respective assessments of individual contributions of the two or more individuals to the collaboration effort.

公开(公告)号：US20250124352A1

公开(公告)日：2025-04-17

申请号：US18916442

申请日：2024-10-15

Applicant: SRI International

Inventor： Anirudh Som ,  Karan Sikka ,  Ajay Divakaran ,  Helen Gent ,  Andreas Kathol ,  Dimitra Vergyri

IPC: G06N20/00

Abstract: Techniques are described for a machine learning system configured to generate respective sample embeddings for a plurality of sample statements. The machine learning system may further be configured to generate a statement embedding for a statement. The machine learning system may further be configured to determine, based on the sample embedding and the statement embedding, respective similarity scores for the sample embeddings. The machine learning system may further be configured to select, based on the respective similarity scores for the sample embeddings, one or more sample statements from the plurality of sample statements. The machine learning system may further be configured to generate a prompt including the one or more sample statements, the statement, and at least one of respective ground-truth information or respective paraphrases for the selected one or more sample statements. The machine learning system may further be configured to provide the prompt to a machine learning model.