Abstract: A system may use sensor data from a facility to generate tentative values associated with an event, such as the identification of an item removed from a shelf of the facility. A confidence value associated with each of the tentative values may be less than a confidence threshold. In response, inquiry data seeking confirmation of a tentative value from an associate is generated and sent to one or more associates in the facility. Responses from the associates are collected to determine a selection of one of the tentative values. The selected tentative value is designated as output data for the system. Thereafter, the output data and the original sensor data are designated as training data, which can then be used to train or update machine learning systems. Subsequent use of the updated machine learning systems can yield more accurate results.

Abstract: This disclosure describes techniques for utilizing sensor data to automatically determine the results of events within a system. Upon receiving sensor data indicative of an event, the techniques may analyze the sensor data to determine a result of the event, such as that a particular user associated with a user identifier selected a particular item associated with an item identifier. Contents of a virtual shopping cart of the user may be maintained based on this automated analysis of sensor data. In some instances, when a confidence level associated with a result is less than a threshold, the sensor data may be sent to a client computing device for analysis by a human user. Further, when the result of an event changes from a first result to a second result, other events already processed may be reprocessed to ensure accuracy of the results.

Abstract: This disclosure describes techniques for utilizing sensor data to determine the results of events within a system. Upon receiving sensor data indicative of an event, the sensor data is analyzed to determine a result of the event. For instance, a user may identified upon entry to a facility, may pick items from shelves in the facility, and may be identified upon exit. Contents of a virtual shopping cart of the user may be maintained based on analysis of the sensor data as the user picks the items. When a user exits, a payment instrument of the user may be charged for a cost of the items and a receipt may be sent to a device of the user. A state-invariant time, representing a time before which all events have been processed, may be maintained such that no shopping sessions are finalized prior to the state-invariant time to avoid mistaken charges.

Abstract: This disclosure describes techniques for utilizing sensor data to automatically determine the results of events within a system. Upon receiving sensor data indicative of an event, the techniques may analyze the sensor data to determine a result of the event, such as that a particular user associated with a user identifier selected a particular item associated with an item identifier. Contents of a virtual shopping cart of the user may be maintained based on this automated analysis of sensor data. In some instances, when a confidence level associated with a result is less than a threshold, the sensor data may be sent to a client computing device for analysis by a human user.

Abstract: This disclosure describes techniques for utilizing sensor data to determine the results of events within a system. Upon receiving sensor data indicative of an event, the sensor data is analyzed to determine a result of the event. For instance, a user may identified upon entry to a facility, may pick items from shelves in the facility, and may be identified upon exit. Contents of a virtual shopping cart of the user may be maintained based on analysis of the sensor data as the user picks the items. When a user exits, a payment instrument of the user may be charged for a cost of the items and a receipt may be sent to a device of the user. A state-invariant time, representing a time before which all events have been processed, may be maintained such that no shopping sessions are finalized prior to the state-invariant time to avoid mistaken charges.

Abstract: This disclosure describes techniques for utilizing sensor data to automatically determine the results of events within a system. Upon receiving sensor data indicative of an event, the techniques may analyze the sensor data to determine a result of the event, such as that a particular user associated with a user identifier selected a particular item associated with an item identifier. Contents of a virtual shopping cart of the user may be maintained based on this automated analysis of sensor data. In some instances, when a confidence level associated with a result is less than a threshold, the sensor data may be sent to a client computing device for analysis by a human user. Further, when the result of an event changes from a first result to a second result, other events already processed may be reprocessed to ensure accuracy of the results.

Abstract: Described is an interactive data processing system configured to facilitate selection by a human associate of tentative values generated by an automated system from sensor data. In one implementation, an event may take place in a materials handling facility. The event may comprise a pick or place of an item from an inventory location, movement of a user, and so forth. The sensor data associated with the event is processed by an automated system to determine tentative values associated with the event. In some situations, an uncertainty may exist as to which of the tentative values accurately reflects the actual event. The sensor data and tentative values may be provided to a human associate. The associate may select one of the tentative values. The selected tentative value and the sensor data may be used to train the automated system.

Abstract: Described is a system and method for routing output information to one or more of a plurality of output devices. In one implementation, output information that includes information to be presented and a location information identifying where the output information is to be presented may be generated by an output host. The output information is received by an output router that determines, based on the location information, an output device and routes the output information to the output device for presentation.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for consumption based subscription frequency recommendations. A system configured to practice the example method first evaluates purchase statistics for an item to determine a consumption frequency. The system receives from a user a request for the item, and presents to the user a subscription recommendation based on the consumption frequency. The system can also provide recommendations for accessories by evaluating purchase statistics for an item to determine an accessory for the item, wherein a number of times the accessory is purchased with the item exceeds a threshold, receiving from a user a request for a subscription for recurring purchases of the item, and presenting to the user a recommendation to include the accessory as part of the subscription.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for consumption based subscription frequency recommendations. A system configured to practice the example method first evaluates purchase statistics for an item to determine a consumption frequency. The system receives from a user a request for the item, and presents to the user a subscription recommendation based on the consumption frequency. The system can also provide recommendations for accessories by evaluating purchase statistics for an item to determine an accessory for the item, wherein a number of times the accessory is purchased with the item exceeds a threshold, receiving from a user a request for a subscription for recurring purchases of the item, and presenting to the user a recommendation to include the accessory as part of the subscription.