公开(公告)号：US20190325342A1

公开(公告)日：2019-10-24

申请号：US16383429

申请日：2019-04-12

Applicant: SRI International

Inventor： Karan Sikka ,  Ajay Divakaran ,  Julia Kruk

IPC: G06N20/00 ,  G06N5/04

Abstract: Embedding multimodal content in a common geometric space includes for each of a plurality of content of the multimodal content, creating a respective, first modality feature vector representative of content of the multimodal content having a first modality using a first machine learning model; for each of a plurality of content of the multimodal content, creating a respective, second modality feature vector representative of content of the multimodal content having a second modality using a second machine learning model; and semantically embedding the respective, first modality feature vectors and the respective, second modality feature vectors in a common geometric space that provides logarithm-like warping of distance space in the geometric space to capture hierarchical relationships between seemingly disparate, embedded modality feature vectors of content in the geometric space; wherein embedded modality feature vectors that are related, across modalities, are closer together in the geometric space than unrelated modality feature vectors.

公开(公告)号：US20250110989A1

公开(公告)日：2025-04-03

申请号：US18895080

申请日：2024-09-24

Applicant: SRI International

Inventor： Ajay Divakaran ,  Yi Yao ,  Julia Kruk ,  Jesse Hostetler ,  Jihua Huang

IPC: G06F16/901

Abstract: In general, various aspects of the techniques are directed to causal analysis using large scale time series data. A computing system may convert large scale time series data to first time period records and second time period records according to a multi-scale time resolution. The computing system may implement a hierarchical machine learning model to generate embeddings that capture temporal characteristics of features of the large scale time series data. The computing system may generate a graph data structure indicating cause and effect correlations between features of the large scale time series data based on temporal dynamics captured in the cause and second time period records and/or the embeddings.

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.