Standard Cognition, Corp.

Nicholas J Locascio

Jordan E Fisher

Daniel L Fischetti

Michael S Suswal

A method for identifying gestures in an area of real space is provided. The method including using a plurality of sensors to produce respective sequences of frames of corresponding fields of view in the area of real space, detecting subjects in the area of real space, using a sequence of frames produced by a corresponding sensor in the plurality of sensors in a first inference engine to identify inventory items carried by the detected subjects in the sequence of frames, using outputs of the first inference engine over a period of time in a second inference engine to identify gestures of the detected subjects, and storing the identified gestures in a database.

Inventory tracking system and method that identifies gestures of subjects holding inventory items

A method for tracking inventory items is provided. The method includes using sensors to produce respective sequences of images of corresponding fields of view, the field of view of each sensor overlapping with the field of view of at least one other sensor. The method includes dividing the area of real space into cells having volume and coordinates in a real space, and identifying inventory items and their locations in three dimensions using the sequences of images produced by at least two sensors having overlapping fields of view. The method includes, in response to the identification of the inventory items: (i) matching locations in three dimensions of identified inventory items with cells in the plurality of cells, and (ii) determining, for a particular inventory item matched with a particular cell, whether a count of the particular inventory item is below a threshold for re-stocking the particular inventory item.

Tracking inventory items in a store for identification of inventory items to be re-stocked and for identification of misplaced items

Systems and techniques are provided for tracking inventory items in an area of real space. A plurality of cameras, or other sensors, produce respective sequences of images in corresponding fields of view in the real space. The field of view of each camera overlaps with the field of view of at least one other camera. The system is coupled to the plurality of cameras and uses the sequences of images produced by at least two cameras in the plurality of cameras to identify inventory events. The inventory event includes an item identifier, a location and a timestamp. A plurality of cells having coordinates in the area of real space are stored as a data set in the memory. The processing system calculates scores at a scoring time, for inventory items having locations matching particular cells using respective counts of inventory events.

Deep learning-based store realograms

Systems and techniques are provided for tracking inventory events in an area of real space. A plurality of sensors produce respective sequences of frames in corresponding fields of view in the real space. The field of view of each sensor overlaps with the field of view of at least one other sensor. The processing system uses the sequences of frames produced by sensors in the plurality of sensors to identify gestures by detected subjects in the area of real space and produce inventory events. The inventory events include a subject identifier identifying a detected subject, a gesture type of the identified gesture by the detected subject, an item identifier identifying an inventory item linked to the gesture by the detected subject, a location of the gesture represented by positions in three dimensions of the area of real space and a timestamp.

Product correlation analysis using deep learning

Systems and techniques are provided for tracking locations of inventory items in an area of real space including inventory display structures. A plurality of cameras are disposed above the inventory display structures. The cameras in the plurality of cameras produce respective sequences of images in corresponding fields of view in the real space. A memory stores a map of the area of real space identifying inventory locations on inventory display structures. The system is coupled to a plurality of cameras and uses the sequences of images produced by at least two cameras in the plurality of cameras to find a location of an inventory event in three dimensions in the area of real space. The system matches the location of the inventory event with an inventory location.

Realtime inventory location management using deep learning

Systems and techniques are provided for tracking inventory items in an area of real space including inventory display structures. A plurality of cameras are disposed above the inventory display structures. The cameras in the plurality of cameras produce respective sequences of images in corresponding fields of view in the real space. A memory stores a map of the area of real space identifying inventory locations on inventory display structures. The system is coupled to a plurality of cameras and uses the sequences of images produced by at least two cameras in the plurality of cameras to find a location of an inventory event in three dimensions in the area of real space. The system matches the location of the inventory event with an inventory location.

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San Francisco, CA, United States of America

Nicholas J Locascio