Abstract: Item codes for items are mapped to multidimensional space as item vectors based on transaction contexts. Similarities between item codes are based on distances between the item codes within the multidimensional space. Substitute items for out-of-stock items are automatically identified based on the item similarities and based on collected feedback from transactions. The substitute items are provided in real time to customers during transactions, item picking services during item fulfillment, and shelf management services for item shelf stocking. In an embodiment, the substitute items are further determined based on a specific transaction history for a given customer and specific feedback collected for the given customer from the specific transaction history.

Abstract: One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters. A data representation of the product clusters may then be stored including data identifying products and the product clusters to which they are partitioned.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: Item codes are mapped to multidimensional space as item vectors based on each item codes context relevant to other item codes in a product catalogue. A transaction history for a given customer is obtained and each item vector associated with a corresponding item purchase made by that customer is obtained. All item vectors per customer are summed to create an aggregated and single vector representing the purchase history of each customer. The aggregated customer-item vectors for the customers are plotted in the multidimensional space. The plotted customer-item vectors are then clustered into groupings based on their distances from one another in the multidimensional space; the groupings representing data-driven customer segments. The data-driven customer segments along with customer identifiers for the customers comprising each segment are provided as input to promotional engines and/or loyalty systems.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: Transaction item codes for transactions are mapped to item vectors within multidimensional space. Each transaction defines a plurality of item vectors for transaction item codes that are mapped within the multidimensional space. Any given item vector's positions within the multidimensional space can have distances calculated to other specific item vectors plotted within the multidimensional space. The distances between the other specific item codes and a given item vector's positions represent probabilities that specific items are likely to be associated with the corresponding item associated with the transaction. Item vector distances below a predefined threshold for a given transaction having a given set of items represent items that are not present in the given transaction but should be recommended to be included with the given transaction.

Abstract: A transaction is identified for a partial rescan security check based at least in part on basket items of the transaction. A total number of rescan items from the basket items is identified for rescan; the total number of rescan items selected for rescan is less than a total number of the basket items in the transaction. Based on the basket items and transaction features for the transaction, item categories or item departments are identified from which the total number of rescan items are to be selected from the basket items for the rescan security check. The total number of rescan items and the item categories for selection are provided to an attendant terminal for the rescan security check. The rescan security check is processed to determine whether the transaction was associated with theft or not associated with theft.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: A machine-learning algorithm is trained with features relevant to transaction exceptions, distributions of items in transaction mapped to product hierarchies, and operator data. The trained algorithm is trained to predict whether a given transaction requires a transaction exception for potential fraud or for management approval. The trained algorithm is then provided a set of in-progress input data for an in-progress transaction being processed on a transaction terminal. Output from the trained algorithm is used to determine whether the in-progress transaction is allowed to continue processing unabated or whether the in-progress transaction is to be suspended with a transaction exception requiring a manager override or security credential to continue processing.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.

Abstract: Various embodiments herein each include at least one of systems, methods, and software implementing a data-driven classifier. One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters.

Abstract: An image of a transaction receipt is obtained and optical character recognition (OCR) is processed on the image to identify a transaction identifier, a transaction total, and a handwritten amount associated with a transaction. When a confidence value associated with the OCR is above a threshold value, the handwritten amount is processed automatically through a transaction interface for the transaction to update the transaction total for the transaction.

Abstract: A machine-learning algorithm is trained with features relevant to predict a time-series value or rate for a current interval of time. The actual rate is compared against the predicted rate and when a deviation between the actual rate and the predicted rate is outside a threshold deviation, an automated action is processed to attempt to remedy the deviation.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for model-based data validation to identify when self-scan checkout data requires validation. Some embodiments, in the form of a method includes receiving, via a network from a self-scanning device, a self-scan dataset of items for purchase within a purchase data processing transaction and evaluating the self-scan dataset to determine whether to require a rescan of items represented in the self-scan dataset. In such embodiments when a rescan is determined to be required, the method includes transmitting via the network to at least one of the self-scan device and at least one device of a store employee data indicating a rescan is required. However, when a rescan is not determined to be required, the method includes permitting the purchase data processing transaction to proceed.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for model-based data validation to identify when self-scan checkout data requires validation. Some embodiments, in the form of a method includes receiving, via a network from a self-scanning device, a self-scan dataset of items for purchase within a purchase data processing transaction and evaluating the self-scan dataset to determine whether to require a rescan of items represented in the self-scan dataset. In such embodiments when a rescan is determined to be required, the method includes transmitting via the network to at least one of the self-scan device and at least one device of a store employee data indicating a rescan is required. However, when a rescan is not determined to be required, the method includes permitting the purchase data processing transaction to proceed.

Abstract: A method and apparatus for controlling the temperature of a disk drive, wherein: a sensor element measures the temperature of a disk drive; a heating element heats the disk drive; and a logic device coupled to the sensor element and the heating element monitors the temperature measured by the sensor element and controls the heating element to cause the heating element to heat the disk drive when the temperature is lower than a predetermined temperature threshold.