公开(公告)号：US11526827B2

公开(公告)日：2022-12-13

申请号：US16588634

申请日：2019-09-30

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Guann-Pyng Li ,  Michael John Klopfer ,  Sergio Gago-Masague ,  Richard P. Donovan ,  Yutian Ren

IPC: G06Q10/06 ,  G06Q50/06 ,  G06Q50/04 ,  G05B23/02 ,  G01R22/10

Abstract: Non-intrusive monitoring and workflow assessment to optimize manufacturing environments are described herein. The systems and method combines computer vision and energy load disaggregation to track worker activity and equipment usage and status. These events are then correlated to optimize workflows and energy efficiency.

公开(公告)号：US20250139446A1

公开(公告)日：2025-05-01

申请号：US18925783

申请日：2024-10-24

Applicant: The Regents of The University of California

Inventor： Guann Pyng Li ,  Yutian Ren

IPC: G06N3/088

Abstract: A system for automatically self-labeling a digital dataset includes a first sensor for generating a first data stream, a second sensor for collecting information to generate a second data stream, and a causal model manager (CMM). The CMM is configured to determine a first causal event from a first data segment of the first data stream, and a causal relation between the first causal event and a second data segment selected from the second data stream. The system further includes (a) an interactive time model for determining an interaction time between the first and second data segments, and (b) a self-labeling subsystem configured to derive a label from the second data segment, associate the first data segment with the derived label, form a self-labeled data pair from the associated first data segment and the derived label, and automatically annotate the self-labeled data pair with the interaction time.