公开(公告)号：US20240212350A1

公开(公告)日：2024-06-27

申请号：US18331007

申请日：2023-06-07

Applicant: SRI International

Inventor： Subhodev Das ,  Ajay Divakaran ,  Ali Chaudhry ,  Julia Kruk ,  Bo Dong

IPC: G06V20/40 ,  G06V10/44 ,  H04N23/21

CPC classification number: G06V20/44 ,  G06V10/44 ,  H04N23/21

Abstract: In general, the disclosure describes techniques for joint spatiotemporal Artificial Intelligence (AI) models that can encompass multiple space and time resolutions through self-supervised learning. In an example, a method includes for each of a plurality of multimodal data, generating, by a computing system, using a first machine learning model, a respective modality feature vector representative of content of the multimodal data, wherein each of the generated modality feature vectors has a different modality; processing, by the computing system, each of generated modality feature vectors with a second machine learning model comprising an encoder model to generate event data comprising a plurality of events and/or activities of interest; and analyzing, by the computing system, the event data to generate anomaly data indicative of detected anomalies in the multimodal data.

公开(公告)号：US20230394413A1

公开(公告)日：2023-12-07

申请号：US18330930

申请日：2023-06-07

Applicant: SRI International

Inventor： Subhodev Das ,  Aswin Nadamuni Raghavan ,  Avraham Joshua Ziskind ,  Timothy J. Meo ,  Bhoram Lee ,  Chih-hung Yeh ,  John Cadigan ,  Ali Chaudhry ,  Jonathan C. Balloch

IPC: G06Q10/0637

CPC classification number: G06Q10/06375

Abstract: In general, the disclosure describes techniques for Artificial Intelligence (AI) models that can automatically generate diverse, explainable, interpretable, reactive, and coordinated behaviors for a team. In an example, a method includes receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team including a plurality of agents; generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator; outputting, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data.