Shoppertrak Rct Corporation

Sensormatic Electronics Corporation

Johnson Controls Tyco IP Holdings Llp

Michelangelo Palella

Steve Edward Trivelpiece

Michael J Paolella

Amit R Patil

Adam Scott Bergman

Richard John Campero

Channing Everet Miller

Zafar Mohammad Haq

Kelly Surles

Daniel Mueller

Manan Kothari

Sanjeev Hasiza

Kevin Weithers

Scott Roberts

Matthew W Wilson

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

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

Example implementations include a method, apparatus and computer-readable medium of controlling a security system by a computer device, comprising receiving device metadata associated with a device in communication with a wireless communication access point. The implementations further include determining that the device metadata comprises matched device metadata based on a match with one of a plurality of previously-identified device metadata in a historical database that stores the plurality of previously-identified device metadata in association with one or more of a plurality of security breach events and a corresponding one of a plurality of security breach probability values. Additionally, the implementations further include determining that the matched device metadata corresponds with a security breach probability value that exceeds a security breach probability threshold. Additionally, the implementations further include generating an alert and sending the alert to a security system device, wherein the alert is configured to initiate a security function.

Apparatus and method of controlling a security system

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

Bartlett, IL, United States of America

Michelangelo Palella