Standard Cognition, Corp.

Kyle E Dorman

Juan C Lasheras

Jordan E Fisher

Daniel L Fischetti

John F Novak

Brandon L Ogle

Kenneth S Kihara

A method for identifying an inventory item is provided. The method includes using first one or more sensors to produce first one or more outputs indicative of an inventory item, using second one or more sensors to produce second one or more outputs indicative of the inventory item, generating, based on the first one or more outputs and independent of the second one or more outputs, a first confidence level of the inventory item being a first inventory item, generating, based on the second one or more outputs and independent of the first one or more outputs, a second confidence level of the inventory item being a second inventory item, and identifying, based on the first confidence level and the second confidence level, the inventory item as one of the first inventory item or the second inventory item.

Identifying inventory items using multiple confidence levels

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. In one embodiment, the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The first image processors process images to identify subjects represented in the images in the corresponding sequences of images. The system includes second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The second image processors mask the identified subjects to generate masked images. Following this, the second image processors process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.

Systems and methods for deep learning-based shopper tracking

Machine learning-based subject tracking

Predicting inventory events using foreground/background processing

Systems and methods for deep learning-based notifications

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. A processing system is coupled to the system. In one embodiment, the processing system comprises image recognition engines receiving corresponding sequences of images from the plurality of cameras. The image recognition engines process the images in the corresponding sequences to identify subjects represented in the images and generate classifications of the identified subjects. The system processes the classifications of identified subjects for sets of images in the sequences of images to detect takes and puts of inventory items on shelves by identified subjects.

Item put and take detection using image recognition

Predicting inventory events using semantic diffing

Growing community of inventors

San Francisco, CA, United States of America

Kyle E Dorman