Abstract: 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.

Abstract: Example implementations include a method, apparatus, and computer-readable medium comprising receiving visual data captured by cameras at a store; retrieving one or more pre-defined events related to shopper pose or location; applying data analytics to the visual data, including comparing the visual data with the pre-defined events to identify a sequence of events related to activities of a shopper in relation to an item in an aisle or zone of the store; generating a score based on the events to indicate a likelihood of the shopper completing a purchase transaction of the item at the store; determining, responsive to the score being above a threshold, whether the events indicate that the shopper completed the purchase transaction; identifying, responsive to the score being above the threshold, friction reasons that resulted in the shopper failing to complete the purchase transaction; and generating an alert corresponding to the score or the friction reasons.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure include methods, apparatus, and systems for providing advertisements including receiving membership information associated with a shopper, retrieving shopper information based on the membership information associated with the shopper, receiving location information associated with a location of the shopper, generating the advertisement based on the shopper information and the location information, and transmitting the advertisement to be displayed on a display associated with the location of the shopper.

Abstract: Examples described herein generally relate to a system for managing a retail environment. The system may collect data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system. The system may predict a condition based on a machine-learning model applied to a combination of the collected data from at least two systems of the plurality of retail information systems. The system may push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for improving accuracy of detecting characteristics of an image in an image detection system. The methods comprise: capturing, by a camera, at least one image or video showing one or more customers in a facility; performing image or video analysis to identify words that define emotions of the one or more customers shown in the at least one image or video; translating the identified words to numerical values in accordance with a pre-defined symbol coding scheme; and combining the numerical values to derive a customer sentiment value for the one or more customers in the at least one image or video.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for managing human resources are provided. In one embodiment, a resource management system includes a network resource management server including at least one database containing data related to human resources and at least one resource management system application. The resource management system may further include business rules that, when applied to the human resource data, generates an action. Access to the system can be provided to both an employer and employee by telephone or electronically for efficient and standardized processing of a leave request.