Sensormatic Electronics Corporation

Shoppertrak Rct Corporation

Tyco Fire + Security Gmbh

Johnson Controls Tyco IP Holdings Llp

Michael J Paolella

Michelangelo Palella

Steve Edward Trivelpiece

Amit R Patil

Jeffrey R Rendlen

Joseph Celi, Jr

Craig Evan Trivelpiece

Richard John Campero

Gopi Subramanian

Channing Everet Miller

Timothy C Gamroth

Zhiqian Wang

Michael C Stewart

Spencer Neilan

Zafar Mohammad Haq

Kelly Surles

Manan Kothari

William Dubois

Kevin Weithers

Scott Roberts

Sanjeev Hasiza

A system may be configured to generate real-time analytics using machine learning and computer vision. In some aspects, the system may receive a storage structure video frame and an interaction video frame, determine a storage structure inference based on the storage structure video frame, and determine an interaction inference based on the interaction video frame. Further, the system may determine that the storage structure inference and the interaction inference correspond to a common time period and common location, and generate analytics information based on the storage structure inference and the interaction inference.

Method and system to provide real time interior analytics using machine learning and computer vision

The present disclosure describes devices and methods for an anonymized tracking service. In particular, devices and methods are described for a third party system that allows a user to selectively opt-in to wireless location tracking at specific stores. The anonymized tracking service may include a third party, a third party server system, a mobile device, and one or more store computing systems. In one aspect, a third-party (e.g., not associated with a store) mobile application may be downloaded onto a mobile device of a user. The mobile application provides a central control unit that allows the user to select which stores have permission to track the location of the user within a building associated with the store. In some embodiments, the central control unit may also allow a user to select which type or how much additional information the store may collect and save.

Systems and methods for an anonymized tracking service

Examples described herein generally relate to a system for monitoring workers in a retail environment. The system may include a plurality of cameras located in different regions of the retail location and configured to capture a video feed of a respective region. The system may include a computer system comprising a memory storing computer executable instructions. The system may provide a video feed of at least one region of the retail environment to a machine learning classifier, the machine learning classifier trained on labeled videos, to classify a sequence of images of a worker into a probability certainty of an activity being performed by the worker. The processor may determine, based on the probability certainties generated by the machine learning classifier over a period of time, an activity schedule of the worker. The system may determine labor productivity statistics based on the activity schedule.

Methods and systems for monitoring workers in a retail environment

Techniques for calibrating a device having a stereoscopic camera are disclosed. According to an embodiment, the device calibrates the stereoscopic camera using a target provided by a mobile device. One or more zones each defining an area in a field of view of the calibrated stereoscopic camera are generated. The one or more zones are refined based on input provided by the mobile device.

Techniques for calibrating a stereoscopic camera in a device

A controller for making initiating actions in a building management system (BMS) is shown. The controller includes a processing circuit configured to receive location data relating to the building occupant located at a first location outside of a building. The processing circuit is configured to, in response to determining the first location is within a first distance range of the building, provide control signals to a first subsystem of the BMS. The processing circuit is configured to receive location data relating to the building occupant located at a second location outside of the building. The processing circuit is configured to, in response to determining that the second location is within a second distance range of the building, provide control signals to a second subsystem of the BMS.

Smart building application to prepare for employee arrival and health risks

Systems and methods for returning an item. The methods comprise: performing operations by a self-return station to capture an image of a first item that an individual is trying to return to an entity; performing machine learning operations by the self-return station using the image to determine whether the first item is damaged and to determine a degree of item damage; allowing, by the self-return station, a return of the first item to the entity when the first item is not damaged or when the degree of item damage does not exceed a threshold value; and preventing, by the self-return station, the return of the first item to the entity when the first item is damaged and the degree of item damage exceeds the threshold value.

Self-service product return using computer vision and artificial intelligence

Systems and methods for returning an item. The methods comprise: performing item return operations by a computing device using at least one of machine learned information about a person who purchased the item, machine learned information about a person returning the item, and machined learned information about a condition of the item at the time of sale and at the time of return; and automatively sorting the item using a conveyer system to move the item from a counter to a respective storage area of a plurality of storage areas assigned to different product types.

Self-service product return using computer vision and Artificial Intelligence

Systems and methods for operating a Point Of Sale ('POS') system. The methods comprise: performing operations by the POS system to machine learn at least information about a condition of an article to be purchased using at least one of imaging, sensing, and short range communications; determining by the POS system if the article is accepted for purchase based at least on the machine learned information about the condition of the article; and completing a purchase transaction by the POS system if a determination is made that the article is accepted for purchase. The POS system may also perform operations to deactivate a security tag coupled to the article or actuate a detachment mechanism of the security tag, if the article was successfully purchased during the purchase transaction.

Point of sale artificial intelligence quality determination system

Growing community of inventors

Zurich, IL, United States of America

Michael J Paolella