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Predicting performance for providing an item

  • US 10,460,332 B1
  • Filed: 02/20/2014
  • Issued: 10/29/2019
  • Est. Priority Date: 02/20/2014
  • Status: Active Grant
First Claim
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1. A computer-implemented method, comprisingstoring, by a computer system, performance metrics for item providers offering an item at an electronic marketplace, the performance metrics indicative of past performances of the item providers associated with providing units of the item to destinations and of past contexts and past conditions corresponding to the past performances, a past context associated with a particular source and a particular destination of one of the units of the item, a past condition associated with a past route between the particular source and the particular destination, the past context and the past condition stored in one or more databases;

  • generating, by the computer system, a performance prediction model based at least in part on the performance metrics, the performance prediction model comprising a machine learning algorithm trained to output, for an item provider of the item providers, expected performances associated with providing a unit of the item, the expected performances varying based at least in part on potential contexts and potential conditions, wherein the machine learning algorithm is trained based at least in part on a tuple that comprises a nested hierarchy of elements, wherein the elements comprise merchant identifiers, source locations, destination locations, contexts, and conditions, wherein, upon completion of training, the machine learning model outputs the expected performances corresponding to levels of the nested hierarchy;

    receiving, by the computer system from a computing device of a consumer, a web search request for information about the item based at least in part on a web site hosted by the computer system and on an access of the computing device to the web site;

    determining, by the computer system, a context associated with providing the unit of the item from a source location associated with the item provider to a destination location associated with the consumer, the destination location determined as a geo-location of the computing device based at least in part on the access to the web site;

    determining, by the computer system from the one or more databases, a condition associated with a route for providing the unit of the item to the destination location;

    generating, by the computer system based at least in part on input to the machine learning algorithm, a performance prediction associated with the item provider, the performance prediction comprising a predicted delivery time for providing the unit of the item and a likelihood for meeting the predicted delivery time, the input comprising an identifier of the provider, the context, and the condition, the performance prediction corresponding to a particular level of the nested hierarchy, wherein the particular level comprises the elements of the tuple;

    sending, by the computer system to the computing device of the consumer, a web page of the web site for presentation in response to the web search request, the web page comprising the performance prediction, the identifier of the item provider, the predicted delivery time, and the likelihood for meeting the predicted delivery time;

    initiating, by the computer system, the providing of the unit of the item via a carrier based at least in part on a selection of the item provider, the selection received from the computing device of the consumer based at least in part on the web page;

    sending, by the computer system to the computing device of the consumer, notifications about progress of the providing of the unit of item, the notifications generated based at least in part on location tracking of the unit of the item, the location tracking comprising receiving a time and a location from a subscription service based at least in part on a scan of a barcode associated with the unit of the item, the subscription service available based at least in part on a subscription with the carrier, the notifications generated based at least in part on times and locations;

    detecting, by the computer system based at least in part on the location tracking, a deviation between at least one of;

    the context and an updated context, or the condition and an updated condition, the updated context and the updated condition associated with the providing of the unit of the item;

    generating, by the computer system, an update to the performance prediction based at least in part on inputting the deviation to the machine learning algorithm, the update comprising at least one of;

    an updated predicted delivery time or an updated likelihood for meeting the predicted delivery time; and

    sending, by the computer system to the computing device of the consumer, a notification about the update to the performance prediction, the notification sent based at least in part on the deviation being detected and comprising a link to an updated web page, the updated web page comprising the updated predicted delivery time or the updated likelihood for meeting the predicted delivery time.

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