Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: A computer-implemented method of executing a programmed spend management computer system. The computer system comprises a data pre-processor that is communicatively coupled to a plurality of the application instances and accesses historic transaction data from any of the instances and thereby has access to a large community of data across all tenants. The data pre-processor is programmed to normalize transaction descriptions and determine line spend values, unit price values, quantity values, and buyer country data for a plurality of commodities, and to store the data in item sets in digital storage. A statistical processor is coupled to the digital storage to access the item sets and executes statistical calculation on the item sets to generate pricing insight data. Pricing insights and/or prescriptions are generated automatically under stored program control and provided to a presentation processor for output to and/or rendering to an end-user device.

Abstract: Techniques for using a graphical user interface to automatically generate expense lines from images are described herein. According to an embodiment, a server computer causes storing in a data repository, data identifying a plurality of users, the data comprising feature data extracted from one or more images of the users. The server computer receives a digital photographic image captured using a camera in a mobile computing device. The server computer identifies one or more faces in the digital photographic image and determines that the one or more faces in the digital photographic image match a particular user. The server computer generates and stores an expense line in an expense data table in the data repository, the expense line comprising an identification of the particular user.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: Techniques for using a graphical user interface to automatically generate expense lines from images are described herein. According to an embodiment, a server computer causes storing in a data repository, data identifying a plurality of users, the data comprising feature data extracted from one or more images of the users. The server computer receives a digital photographic image captured using a camera in a mobile computing device. The server computer identifies one or more faces in the digital photographic image and determines that the one or more faces in the digital photographic image match a particular user. The server computer generates and stores an expense line in an expense data table in the data repository, the expense line comprising an identification of the particular user.

Abstract: Systems and methods for guaranteed redemption of sale items. The systems and methods can include a payment system for issuing a payment instrument to a customer when the sale item is not available in a retail location. The systems and methods can further include a redemption system for redeeming the payment instrument for the sale item.

Abstract: Systems and methods for guaranteed redemption of sale items. The systems and methods can include a payment system for issuing a payment instrument to a customer when the sale item is not available in a retail location. The systems and methods can further include a redemption system for redeeming the payment instrument for the sale item.

Abstract: Control or prevention of usage of an online ecommerce system to transfer funds by evaluating proximity between a location where cash payment is made and a location where a cash refund is requested. When the proximity exceeds a distance threshold, the cash refund at the second location is prohibited, and when the proximity is less than the distance threshold, the cash refund is permitted. Various embodiments ask the customer to specify a choice of refund stores, evaluate whether the person who made the cash payment for the order is different than the person who requested the refund, require identification, check publicly available Internet social site information for a relationship between the people, check for a pattern of requesting refunds, limit the refund to store credit, or a combination thereof.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.

Abstract: Disclosed are systems and methods for managing transactions in which an order is specified online and payment is received at a point of sale (POS). Methods are disclosed for managing payment for such transactions at a POS, including transactions involving payment for both in-store purchases and online orders. Methods are also disclosed for managing inventory and price changes for such transactions where payment can occur at any time in a pay period following order creation. Also disclosed are methods for processing refunds for online orders for which payment was made at a POS. Finally, methods for preventing fraud and abuse as well as restricting the availability of this payment method for certain items are disclosed.