Ncr Corporation

Ncr Voyix Corporation

Itamar David Laserson

Julie Dvora Katz Ohayon

Rotem Chudin

Moshe Shaharur

Avishay Farbstein

Tali Shpigel

Norman Leonard Trujillo

Matthew Robert Burris

Christopher John Costello

Mor Zimerman Nusem

Loran Halfon

Tamar Miriam Haizler

Shiran Abadi

Historical transaction data, events, replenishment activities, and terminal statuses for terminals of a given store are obtained. The data is preprocessed to include calculated terminal traffic patterns, cash usage patterns, and elapsed time until a replenishment activity for a given interval of time on per terminal. A machine-learning model (MLM) is trained on the modified data to configure itself for predicting the elapsed time per terminal. Real-time transaction data, events, and terminal statuses for the terminals of the store are obtained at the interval of time, and current traffic and usage patterns are inserted into the real-time data and fed as input to the MLM. The MLM returns terminal identifiers for the terminals and corresponding elapsed periods of time until a projected replenishment activity is likely to occur. The terminal identifiers and corresponding elapsed periods of time are rendered within an interface accessible to the store for media management.

Media replenishment predictor

Valuable media balancing data and transaction data for valuable media accounts are tagged into categories and a machine-learning model is derived by training the model with the data and actual actions taken to rectify account discrepancies. The model is provided real-time data and produces as output a probability that a given account can have a discrepancy rectified along with recommended actions for resolving the discrepancy and specific transactions that should be investigated with the actions. If different actions are taken to resolve the discrepancy and/or if different transactions were identified as a cause of the discrepancy, the different actions and different transactions are provided as feedback to the model for subsequent training sessions of the model.

Data-driven valuable media balance optimization processing

An unrecognized item code is identified during a transaction at a transaction terminal. When the unrecognized code is determined to be associated with a typographical error, alternative corrected item codes are supplied to the transaction terminal to replace the unrecognized code. When the unrecognized code is determined to be missing from a product catalogue, an ordered list of most likely corrected item codes for the unrecognized code is provided to the transaction terminal for selection of one of the corrected item codes by an operator.

Data-driven transaction terminal item code corrector

A culture is defined that spans multiple retailers. Transaction data from the multiple retailers are processed to map barcoded item codes to a culture item vector space. Any non-barcoded item for a given retailer associated with the culture is linked to a most similar barcoded item of that retailer based on a retailer-specific item vector space. The distances between the mapped barcoded item codes of the culture item vector space are processed to cluster the barcoded item codes into classifications within the culture vector space. Each retailers non-barcoded items are associated to the classifications of the culture item vector space based on their linkages to the retailers' specific barcoded items, which are already mapped within the culture item vector space. Each item code of a given retailer's item catalogue is linked to its corresponding classification.

Platform-based cross-retail product categorization

Item vectors representing transaction contexts for items are mapped to multidimensional space. A request is received for an alternative to a given item from a resource. The multidimensional space is evaluated to identify closest candidate items to the given item based on the corresponding item vectors. An optimal candidate item is selected from the candidate items based on the request. The association between the given item and the optimal candidate item is injected within a process workflow associated with the resource.

Alternative item booster service

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.

Data-driven segmentation and clustering

An item price that is noted in a transaction for an item is identified. Any discount or item price override is identified for the transaction. A catalogue price for the item is obtained. Similar items associated with the item are determined based on mapped transaction contexts for the item and the similar items within a multidimensional space. Similar item prices are obtained and a median price for the item and similar items is calculated. A real-time price alert is sent to a resource that is associated with processing or handling the transaction when the item price, adjusted for any discount or item price override, deviates (above or below) from the median price by a threshold amount.

Real-time alerts for abnormal item pricing

A cross-entity and cross-retailer platform is provided that captures transaction data (and/or browser history data associated with online browser-based transactions), indexes, and stores the data in a cloud-accessible data store. A cloud service is provided that custom processes retailer and entity-defined workflows based on purchase transactions using the data store. The service discovers and updates trends and patterns associated with item sales for a given retailer or for a given entity across channels associated with in-store and online item sales. The trends and patterns are dynamically reported to the corresponding entity or the corresponding retailer. The entities may comprise manufacturers of an item, a supplier of the item, a distributor of the item, and a Consumer Packaging Goods (CPG) company of the item.

Cross-entity channel integration services

A cross-entity and cross-retailer platform is provided that captures transaction data, indexes, and stores the data in a cloud-accessible data store. A service is provided that custom processes retailer and entity-defined workflows based on purchase transactions using the data store. The service also generates and maintains correlations between items, the transactions, geographical locations, retailers, and stores of the retailers. The correlations are provided as timely retail recommendations to the retailers and entities for suggested changes that are likely to optimize purchase transactions. The entities may comprise manufacturers of an item, a supplier of the item, a distributor of the item, and a Consumer Packaging Goods (CPG) company of the item.

Cross-entity recommendation services

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.

Image-based transaction processing

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.

Transaction exception and fraud processing

Transaction items for a transaction are received during a transaction. Any non-barcoded items are identified and processed within a retailer-specific vector space to identify a most-similar barcoded item offered by a corresponding retailer to the non-barcoded item. The transaction items are revised to include the most-similar barcoded item as a replacement for the non-barcoded item. The revised transaction item list is used to identify a recommended item based on a segment-specific vector space associated with a segment assigned to the transaction. The recommended item is provided in real time to a transaction service that processes the transaction for delivery to a customer associated with the transaction.

Segment-based recommendation engine

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.

Item affinity processing

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.

Data-driven partial rescan precision booster

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.

Growing community of inventors

Giv′at-Shmuel, Israel

Itamar David Laserson