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Method, apparatus, and computer program product for forecasting demand using real time demand

  • US 10,685,362 B2
  • Filed: 06/05/2018
  • Issued: 06/16/2020
  • Est. Priority Date: 10/04/2012
  • Status: Active Grant
First Claim
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1. A method for forecasting demand, the method comprising:

  • accessing, from a database, a predicted demand for at least one promotion tuple, for a specified time period, wherein the predicted demand is representative of an estimated number of units to be sold during the specified time period, and wherein the promotion tuple comprises information indicative of a category or sub-category, a location, and a price range;

    calculating, via a processor, a real-time demand using data indicative of consumer activity,wherein the calculation of the real-time demand comprises;

    accessing user search data, the user search data captured, via a user interface, from an interaction, between a user device and a promotion and marketing service website or application, to identify a requested promotion, the user search data comprising at least location specific data and a category or sub-category;

    generating an identification pair for the user search data, the identification pair comprising a first classification and a second classification, the first classification identifying at least a category of promotion, and the second classification identifying a location identified by the location specific data,wherein the first classification is generated by;

    normalizing the user search data, supplying the normalized user search data to a classifying model as attribute data, wherein the classifying model is a trainable classifier adapted based on a training data set of exemplary data representing exemplary terms previously determined to be semantically related to particular categories; and

    distributing the real-time demand to multiple hyper-locations, multiple sub-categories, and multiple price points due to the capturing of the real-time demand being identified by or including a high level location and category or sub-category; and

    determining, via the processor, a total demand, on a per category or sub-category, per location, and per price range basis by summing the predicted demand and the real time demand.

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