×

Wholesale/trade-in pricing system, method and computer program product therefor

  • US 10,504,159 B2
  • Filed: 09/19/2013
  • Issued: 12/10/2019
  • Est. Priority Date: 01/29/2013
  • Status: Active Grant
First Claim
Patent Images

1. A system for determining pricing information, the system comprising:

  • a computer processor;

    a network interface coupled to the processor;

    a data storage device coupled to the processor, the data storage device storing a retail pricing model;

    an output device coupled to the processor;

    wherein the processor is configured to;

    collect, via the network interface, historical wholesale transaction records for a set of wholesale transaction for a set of vehicles from data sources external to the system and store the collected historical wholesale transactions on the data storage device, wherein each wholesale transaction includes a sale date, a sale price, a sale region, a vehicle identification and a set of vehicle attributes for a corresponding vehicle, the set of vehicle attributes for a corresponding vehicle comprising a vehicle year, make and model;

    provide, over a network, a set of user interface pages with controls to collect vehicle condition information for the vehicles in the set of vehicles, the vehicle condition information comprising first indicators for a plurality of conditions, the plurality of conditions comprising a title condition, a paint condition, a body condition and a windshield condition, and including free-form text;

    in a backend process;

    apply a first set of rules to bin vehicles in the set of vehicles into a plurality of bins based on make, model, year and region;

    extracting first indicators for the plurality of conditions from the free-form text by parsing the free-form text to determine one or more textual condition terms in the free-form text and transforming the one or more textual condition terms determined from the free-form text to first indicators for the plurality of conditions;

    classifying the first indicators into one of a set of clusters of second indicators representing the plurality of conditions, wherein the set of clusters is constructed utilizing a machine learning model trained to semantically analyze the free-form text using a training set correlating condition terms to the set of clusters;

    enhancing the historical transaction records with the second indicators for the plurality of conditions associated with each historical transaction record;

    map the second indicators for the plurality of conditions, to values for a set of condition features, wherein mapping the second indicators for the plurality conditions to values for the set of condition features comprises;

    assigning each vehicle in the set of vehicles a numerical value for each feature in the set of condition features;

    generate a price ratio model that models price ratio as a function of a set of variables that represent at least the set of vehicle attributes and the set of condition features for the vehicles in the vehicle set, the price ratio model comprising a set of regression coefficients applied to the set of variables, the set of regression coefficients based on a fit of the price ratio model to the historical wholesale transaction and set of condition features;

    in a frontend process;

    provide a web page to a user device, the web page having one or more input fields for a user to provide a user specified vehicle configuration;

    receive, based on user interaction with the web page, a request for a wholesale price of a user specified target vehicle of interest from the user device, the request comprising a set of target vehicle attributes, condition information for the target vehicle and a geographical location, wherein the condition information for the target vehicle indicates conditions associated with the target vehicle and the target vehicle attributes include a target vehicle make, model and year;

    determine a retail price for the target vehicle based on the retail pricing model;

    apply a second set of rules to determine a bin for the target vehicle from the plurality of bins based on the target vehicle make, model and year and geographical location;

    determine from a set of historical records containing wholesale transaction data, vehicles in the set of vehicles associated with the bin;

    for each vehicle associated with the bin, determine a predicted retail price based on the retail pricing model;

    determine an average ratio of wholesale price to predicted retail price for the bin;

    determine a set of target vehicle condition features from the set of condition features based on the condition information for the target vehicle, the set of target vehicle condition features representing conditions associated with the target vehicle;

    apply the price ratio model with the set of regression coefficients pre-calculated in the backend process to dynamically generate the wholesale price for the target vehicle of interest as a function of the retail price of the target vehicle, the average ratio of wholesale price to predicted retail price for the bin, the set of target vehicle attributes and the set of target vehicle features representing conditions associated with the target vehicle;

    respond to the request in real-time by generating a response interface and providing the response interface to the user device, the response interface configured to reconfigure a user interface at the user device to present the dynamically generated wholesale price for the target vehicle of interest.

View all claims
  • 2 Assignments
Timeline View
Assignment View
    ×
    ×