RISK-BASED ADAPTIVE RESPONSES TO USER ACTIVITY IN A RETAIL ENVIRONMENT

    公开(公告)号:US20230386217A1

    公开(公告)日:2023-11-30

    申请号:US18298124

    申请日:2023-04-10

    CPC classification number: G06V20/52 G06V20/44 G06V10/70 G06Q20/208 G06V2201/07

    Abstract: The disclosed technology provides for automatically detecting and responding to potentially suspicious or risky activity in a retail environment. A method can include receiving, from monitoring devices in a retail environment, a stream of activity data, applying a model to the stream of activity data to identify a portion of the data corresponding to guest activity during a checkout process, identifying whether a risk event is associated with the activity, determining a guest risk impact score, selecting (i) a particular manual response from among candidate manual responses and (ii) a particular automated response from among candidate automated responses based on the risk impact score satisfying manual response criteria and/or automated response criteria, transmitting instructions to a POS terminal to implement the particular automated response, and/or transmitting instructions to implement the particular manual response to one or more mobile devices, that prompt employees to perform the manual response.

    GENERATING WATCH LISTS FOR RETAIL STORES BASED ON UNSTRUCTURED DATA AND SYSTEM-BASED INFERENCES

    公开(公告)号:US20230005345A1

    公开(公告)日:2023-01-05

    申请号:US17722905

    申请日:2022-04-18

    Abstract: 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.

    IDENTIFYING SCANNING MOTIONS DURING CHECKOUT USING OVERHEAD CAMERAS

    公开(公告)号:US20240296677A1

    公开(公告)日:2024-09-05

    申请号:US18659328

    申请日:2024-05-09

    CPC classification number: G06V20/44 G06N20/00 G06Q20/208 G06V20/52

    Abstract: 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.

    GENERATING SECURITY EVENT CASE FILES FROM DISPARATE UNSTRUCTURED DATA

    公开(公告)号:US20240289804A1

    公开(公告)日:2024-08-29

    申请号:US18658532

    申请日:2024-05-08

    CPC classification number: G06Q20/4016 G06F16/353 G06Q20/20

    Abstract: 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.

    Identifying scanning motions during checkout using overhead cameras

    公开(公告)号:US12243305B2

    公开(公告)日:2025-03-04

    申请号:US18659328

    申请日:2024-05-09

    Abstract: 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.

    GENERATING WATCH LISTS FOR RETAIL STORES BASED ON UNSTRUCTURED DATA AND SYSTEM-BASED INFERENCES

    公开(公告)号:US20240404379A1

    公开(公告)日:2024-12-05

    申请号:US18798991

    申请日:2024-08-09

    Abstract: 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.

    Identifying scanning motions during checkout using overhead cameras

    公开(公告)号:US12014544B2

    公开(公告)日:2024-06-18

    申请号:US17878644

    申请日:2022-08-01

    CPC classification number: G06V20/44 G06N20/00 G06Q20/208 G06V20/52

    Abstract: 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.

    Generating security event case files from disparate unstructured data

    公开(公告)号:US12014375B2

    公开(公告)日:2024-06-18

    申请号:US17722820

    申请日:2022-04-18

    CPC classification number: G06Q20/4016 G06F16/353 G06Q20/20

    Abstract: 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.

    IDENTIFYING BARCODE-TO-PRODUCT MISMATCHES USING POINT OF SALE DEVICES AND OVERHEAD CAMERAS

    公开(公告)号:US20230037427A1

    公开(公告)日:2023-02-09

    申请号:US17878654

    申请日:2022-08-01

    Abstract: Disclosed are systems and methods for determining whether an unknown product matches a scanned barcode during checkout. The system includes a checkout lane having a flatbed scanning area with scanning devices and a point of sale (POS) terminal that scans a product identifier of an unknown product, identifies a product associated with the scanned product identifier, and transmits, to a computing system, product information. An overhead camera idnentifies, based on detecting an optical signal from the POS terminal, that a scanning event occurred, captures image data of the unknown product, and transmits, to the computing system, the image data. The computing system generates machine learning product identification models for identifying unknown products, identifies candidate product identifications for the unknown product based on applying the models to the image data, and determines, based on the candidate product identifications and the information about the product, whether the unknown product matches the product.

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