Toshiba Global Commerce Solutions Holdings Corporation

Toshiba Global Commerce Solutions

Evgeny Shevtsov

Charles Ray Kirk

J Wacho Slaughter

Srija Ganguly

Yevgeni Tsirulnik

Andrei Khaitas

Judith L Atallah

David John Steiner

Johanna Hinkle

Suzanne M Bleakley

Michelle M Crompton

Philip Brown

Viktor Ivakhno

Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having an optical sensor device, an optical scanner device and a load sensor device. The optical sensor device has a field of view that includes a first region about the POS system and the load sensor device is operable to measure a load of an object while positioned in a bagging area of the POS system. Further, the first region includes a second region associated with a certain object type that can be positioned in the bagging area without having to be scanned or weighed. The method includes obtaining a successive image captured by the optical sensor device to enable a determination that the target object corresponds to the certain object type that is exempt from being scanned or weighed based on the successive image.

Activity detection at point of sale

Systems and methods of providing navigation information associated with a retail space are provided. In one exemplary embodiment, a method comprises, a network node operationally coupled to a set of optical sensor devices positioned throughout a retail space, the network node receiving a sequence of images from each optical sensor device. The method comprises, obtaining a current position of a certain subject in the retail space based on one or more sequence of images received. The method comprises, obtaining a current position of an obstacle in the retail space based on one or more sequence of images received. The method comprises, sending to a wireless device associated with the certain subject, navigation information associated with the current positions of the certain subject and the obstacle in the retail space.

Retail computer vision system for sensory impaired

Techniques to reconfigure self-checkout kiosks based on computer vision or interaction metrics. One or more graphical images are received. The one or more graphical images at least partially depict a physical area associated with a self-checkout kiosk during a transaction involving one or more items and a form of payment. The one or more graphical images include a first item. The self-checkout kiosk is configured to output one or more communications via one or more graphical user interface (GUI) screens associated with a workflow. An item type or a payment type is predicted for the transaction and using one or more classifiers. The predicted item type or payment type is provided as input to the workflow without requiring an intervention. The transaction is executed.

Self-checkout workflow management system

Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Computer vision grouping recognition system

This disclosure describes an automated process for training an ML model used by a computer vision system in a point of sale (POS) system to recognize a new item. Instead of relying on a manual process performed by a data scientist, the automated process can use images of a new (i.e., unknown) item captured at one or more POS systems to then retrain the ML model to recognize the new item. That is, the images of the item are used to retrain the ML model and to test the accuracy of the updated ML model. If the updated ML model can confidently identify the new item, the updated ML model is then used by the computer vision system to identify items at the POS system.

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Frisco, TX, United States of America

Evgeny Shevtsov