Target Brands, Inc.

Christopher Mark Brakob

Ethan Sommer

Todd A Hagen

Andrew James Wipf

Ryan David Siskind

Kathleen Jacqueline Roush

Prakash Mall

Neha Dixit

Arun Vaishnav

Dharmavaram Arbaaz

Francisco Cancino, Iii

Arun Patil

Described herein are systems and methods for determining whether a scanning motion occurred during a checkout process. The system includes a checkout lane having a scanning area that receives products to be purchased by a user, scanning devices, a point of sale (POS) terminal that identifies a product based on a scan, using the scanning devices, of a product identifier for the product as the product is moved through the scanning area, and an overhead camera that captures image data of the user's body movements and transmits, to a computing system, the image data. The computing system can, during runtime, identify whether a scanning motion occurred during the user's body movements based on application of one or more motion identification models to the image data and determine, based on identification of the scanning motion, that the user performed an affirmative scan during the checkout process.

Identifying scanning motions during checkout using overhead cameras

Described herein are systems and methods for generating a watch list of users who pose specific security threats to a store. The method can include retrieving, by a computer system from a data store, case files that document activity that poses a security threat by a user at the store, predicting, based on applying prediction models to the case files, future activity associated with the case files, determining threat scores for the case files based on the predicted future activity, ranking the case files into a candidate list from highest to lowest threat score, generating a watch list for the store that includes a subset of the ranked case files based on which case files pose a greatest current threat to the store, generating summary videos for each case file in the watch list, and transmitting the watch list and summary videos to a user device.

Generating watch lists for retail stores based on unstructured data and system-based inferences

Described herein are systems and methods for generating security event case files with unstructured data. For example, the method can include receiving, by a computing system, unstructured data and system-based inferences from devices positioned throughout a store, and adding structure to the unstructured data and system-based inferences based on applying one or more structuring models. Adding structure can include labeling the data and system-based inferences, classifying them into security event categories, and identifying objective identifiers to identify users in the data and system-based inferences. The method also can include generating case files for each of the objective identifiers, where the case files include the associated data. The method can include determining whether the case files satisfy alerting rules. The case files can then be reported out and acted upon (e.g., based on satisfying the alerting rules) and/or stored for subsequent analysis and use.

Generating security event case files from disparate unstructured data

A computer-implemented method includes determining that a group of items left a space together based on RFID exit times and determining that at least one item of the group of items left the space without authorization by evaluating transactions that took place within a first time period before the at least one item left the space. A single investigation file is automatically created that includes the group of items that left the space together.

Item tracking system

Disclosed herein are systems and methods for determining whether an unknown product matches a scanned barcode during a checkout process. An edge computing device or other computer system can receive, from an overhead camera at a checkout lane, image data of an unknown product that is placed on a flatbed scanning area, identify candidate product identifications for the unknown product based on applying a classification model and/or product identification models to the image data, and determine based on the candidate product identifications, whether the unknown product matches a product associated with a barcode that is scanned at a POS terminal in the checkout lane. The classification model can be used to determine n-dimensional space feature values for the unknown product and determine which product the unknown product likely matches. The product identification models can be used to determine whether the unknown product is one of the products that are modeled.

Identifying barcode-to-product mismatches using point of sale devices

For a measurement period, signal strengths, device identifiers and access point identifiers are received from a plurality of access points. A record for the measurement period is stored in random access memory with the record being associated with a single device identifier and containing access point identifiers associated with signal strengths received for the measurement period. The location of a device is determined for the measurement period by retrieving spatial coordinates of a corresponding access point for each access point identifier in the record for the measurement period and setting the location of the device for the measurement period to the average of the retrieved spatial coordinates.

Memory management for mobile device correlation

A computer-implemented method obtains a transaction log for a transaction at a self-service terminal during which the self-service terminal fails to detect the presence of an item at the self-service terminal. Features are extracted from the transaction log and the features are applied to a model to generate an inference for the transaction. The inference is used to determine whether to issue an alert that improper scanning has occurred at the self-service terminal.

Using transaction logs to determine that an undetected item was not scanned at a self-service terminal

A method includes monitoring performance of a system that determines locations of items in a retail store based on sensor values from a collection of sensors in the retail store. When the performance is insufficient to process the sensor values as the sensor values are received, a spatial resolution used to determine the locations of the items in the retail store is reduced.

Adaptive spatial granularity based on system performance

A method includes obtaining an RFID, an associated barcode and an exit time for an RFID tag that was detected by an RFID sensor as exiting a building. A transaction database is accessed to determine if a product with a matching barcode was purchased before the exit time when a product with a matching barcode was not purchased before the exit time, marking the RFID as bypassing purchase.

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Maple Grove, MN, United States of America

Christopher Mark Brakob