Abstract: A system and method for verifying a non-scan item at a self-checkout point-of-sale terminals 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.

Abstract: At least one image of a produce item on a scale of a terminal is captured during a transaction at the terminal. A machine learning model provides a classification for the item based on the image. The classification indicates whether the item is bagged or unbagged. When the item is in a bag, a tare weight for the bag is subtracted from the weight recorded by the scale to calculate a price for the item. When the item is unbagged, the weight recorded by the scale is used to calculate the price. In an embodiment, the model provides a classification that indicates whether the item is organic or non-organic. When the item is organic, a transaction interface is automatically populated with an organic produce selection and presented to an operator of the terminal for confirmation.

Abstract: A base machine learning model (MLM) is trained to generate N-dimensional feature vectors from images of items per item code or category. A principal component analysis is processed on the N-dimensional feature vectors per item code to generate fewer reference vectors per item code, each reference vector includes fewer dimensions or features than the corresponding N-dimensional feature vectors. A similarity MLM is trained to receive an item code, corresponding reference vectors for the item code, and a current reduced dimensionality feature vector for a current image of an item associated with a transaction. The similarity MLM produces a similarity score or determines whether the current item is the same or similar to a reference item corresponding to the item code. When the current item is not the same or is dissimilar to the reference item, an alert or an interrupt is provided to audit the transaction for potential theft.

Abstract: During a transaction at a terminal, an operator of a terminal indicates that an item is a produce item. An image of the item is provided as input to a machine learning model which determines whether the item is a consumer packaged good (CPG) item type or a produce item (e.g., a non-CPG item type). When the model determines the item is a CPG item type, the transaction is suspended for an audit of the item.

Abstract: Feature vectors derived from images of items are retained in a storage bank and indexed by price lookup (PLU) code. An image of an item and a corresponding entered PLU code during a transaction at a terminal are provided as input to a machine learning model and a current feature vector for the image is provided as output. Model feature vector(s) corresponding to the entered PLU code are obtained from the storage bank. The current feature vector and the model feature vectors are provided as input to a comparison machine learning model, which provides as output a confidence value indicative of a degree of similarity between the feature vectors. When the confidence value fails to meet a confidence threshold, this indicates a low confidence that the item is associated with the entered PLU code and an interrupt is sent to the terminal as an indication of potential shrinkage for the transaction.

Abstract: A root machine learning model receives an item image captured of an item at a terminal and produces output, which is associated with an item classification for the item. A head machine learning model is selected from a plurality of head models based on transaction information associated with a transaction at a terminal. A candidate item identifier is received from the terminal. The head model uses the candidate item identifier, the root model's output data, and localized metadata maintained for the head model to provide a predicted item identifier for the item. An actual item identifier for the item is received as feedback from the terminal. The localized metadata is updated with the root model's output data and the actual item identifier for the item based on the feedback.

Abstract: A system and method for verifying a non-scan item at a self-checkout point-of-sale terminals 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.