Stoplift, Inc.

Ncr Corporation

Other

Intuvision Inc.

Matthew Farrow

Joshua Migdal

Malay Kundu

Vikram Srinivasan

Brian Frank O'Donnell

Sadiye Zeyno Guler

Jason Adam Silverstein

Ian Harris Pushee

Chris Wible

James Morrin

A system and method for verifying a non-scan item at a self-checkout point-of-sale terminal is provided. A merchandise item that isn't scanned at the self-checkout terminal is identified by a detection system. Further, a user helper device is provided to verify the incident by prompting the shopper for an input. The user helper device is operably connected to the self-checkout point-of-sale terminal and the detection system to determine and verify a fraudulent incident occurring during a transaction activity by the shoppers. Further, a store attendant device is provided for human intervention when necessary.

Non-scan loss verification at self-checkout terminal

Embodiments herein include novel ways of alerting store personnel when various activities, events, conditions, etc., occur at the checkout in retail establishments. For example, in accordance with one embodiment, the alerting can take place in substantially real-time, when the event occurs, allowing personnel to take appropriate measures, corrective or otherwise, to deal with the detected event. Examples of such events may include situations such as when a person of interest is detected as shopping at the store, when a cashier has missed scanning an item at the checkout, or to alert store personnel if a non-empty shopping cart has exited the store without payment (a.k.a., a cart push-out). Although the discussion below uses a grocery store as an example retail environment, embodiments herein can be used in any type of retail environment.

Notification system and methods for use in retail environments

A computer system monitors a private network for presence of data traffic. In one embodiment, the private network is a retail environment in which a customer purchases an item scanned at a point-of-sale terminal. The data traffic can be point-of-sale transaction information generated in response to purchase of the item at the point-of-sale terminal. Subsequent to detecting presence of the data traffic in the network, the computer system analyzes the data traffic to identify private information in the data traffic. To protect the private information from improper disclosure to unauthorized entities, the computer system modifies the data traffic. Thus, techniques herein can include detecting occurrence of one or more point-of-sale events in a private network, anonymizing the event information (e.g., data traffic), and forwarding the anonymized event information associated with the point-of-sale events out of the private network to a remote resource.

Anonymization and synchronization based on use of protected content

Video tracking systems and methods include a peripheral master tracking process integrated with one or more tunnel tracking processes. The video tracking systems and methods utilize video data to detect and/or track separately several stationary or moving objects in a manner of tunnel vision. The video tracking system includes a master peripheral tracker for monitoring a scene and detecting an object, and a first tunnel tracker initiated by the master peripheral tracker, wherein the first tunnel tracker is dedicated to track one detected object.

Video tracking systems and methods employing cognitive vision

Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

Cart inspection for suspicious items

A verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

Verification of fraudulent activities at a self-checkout terminal

The present disclosure includes a system in which a video displayer, having access to video surveillance data and point-of-sale transaction data, provides random-access review of point-of-sale transactional video. Embodiments herein provide a new method of reviewing security video of a transaction taking place at a retail establishment's point-of-sale transaction area, or register. Embodiments herein enable random and instantaneous access to any number of transaction events that may be of interest to a loss prevention investigator. In one embodiment, a video displayer obtains video data comprising a point-of-sale transaction occurring at a point-of-sale transaction area, and transaction data from a point-of-sale terminal. The video displayer displays video of individual transaction events from a larger transaction, and visually modifies displayed video at a moment in the video when a transaction event occurs.

Method and apparatus for random-access review of point of sale transactional video

Techniques disclosed herein include a reviewer interface for providing random-access review of point-of-sale (POS) video, visual indicators to point to point to objects within the point-of-sale video, and/or display of interactive statistical features. The system obtains video data of point-of-sale transactions, and corresponding transaction data correlated with the video data. The system identifies and displays transaction events of interest. In one embodiment, such a display includes displaying a visual indicator positioned at a spatial location within displayed video to identify a location of an object in the displayed video that corresponds to the transaction event identified as a transaction event of interest. The system also includes functionality to display visual indicators associated with transaction data to convey magnitude or quantity of transaction events of interest. The system also includes interactive graphs and filtering criteria for displaying transaction statistics and to browse details of transaction events.

Method and apparatus for web-enabled random-access review of point of sale transactional video

According to one configuration, a computer environment includes a first monitoring system, a second monitoring system, and an analyzer. The first monitoring system produces video of an item being entered into a point of sale system. The second monitoring system monitors events associated with entering of the item into the point of sale system. The analyzer detects, via a communication from the second monitoring system, an occurrence of at least one event associated with entering of the item. Based on communication, the analyzer correlates the at least one event to a particular time in the video when the at least one event occurred.

Correlating detected events with image data

A system performs video identification of items involved with transactions, as captured in video data, and compares this item identification information with transaction data obtained from a transaction terminal such as a point-of-sale register to identify situations that are suspicious and may indicate fraudulent activity or operator error. In particular, the system as disclosed herein utilizes unique camera positions, and can use video data from more than one camera, to perform robust detections of fraudulent or suspicious activities associated with transactions in the transaction area. By obtaining video data originating from at least one video camera that monitors a transaction area, the system can automatically (i.e. no human involvement needed) analyze the video data to track items involved in a transaction in the transaction area. Using this information, the system can compare the video analysis of the tracked items to transaction data produced from a transaction terminal to identify suspicious activity. The system can also create, update and maintain object models for items tracked in a transaction area, in part, to assist in identifying suspicious activity.

Growing community of inventors

Canton, MA, United States of America

Matthew Farrow