IP Library Granted Patent US 10,679,263
Granted Patent B2
US 10,679,263 · App. 16/272,396 · Granted Jun 9, 2020

System and method for the utilization of pricing models in the aggregation, analysis, presentation and monetization of pricing data for vehicles and other commodities

Inventors: Michael Swinson (Santa Monica, CA); Lin O'Driscoll (Mammoth Lakes, CA); Scott Painter (Bel Air, CA)
Assignee: TrueCar, Inc.
G06Q30/0278G06Q30/02
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Quick Facts
Patent No.
US 10,679,263
App. No.
16/272,396
Granted
Jun 9, 2020
Kind
B2
Abstract

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Claims (64)

1. A method, comprising:

receiving, by a vehicle data system having a processor and a non-transitory computer readable medium, a request from a user device, the request containing configuration information for a user-specified vehicle;

retrieving, by the vehicle data system from a data store, historical transaction data obtained from disparate data sources communicatively connected to the vehicle data system, the historical transaction data comprises historical transactions of a set of vehicles;

determining, by the vehicle data system, whether a number of the historical transactions which exists in the historical transaction data within a certain time period is less than a threshold;

responsive to the number of the historical transactions which exists in the historical transaction data within the certain time period being less than the threshold, determining, by the vehicle data system, a data scarcity model by traversing a decision tree having nodes, each node representing a data scarcity condition of the historical transaction data within the certain time period;

determining, by the vehicle data system, pricing data for the user-specified vehicle utilizing the data scarcity model determined by the vehicle data system for the user-specified vehicle; and

sending, by the vehicle data system, the pricing data for the user-specified vehicle to the user device.

2. The method according to claim 1 , further comprising:

determining most recent listing price data for each vehicle year, make, model, and trim for which an initial historical transaction exists in the historical transaction data;

producing an average listing price for the trim based on the most recent listing price data thus determined; and

creating the data scarcity model utilizing at least the historical transaction data and the average listing price for the trim.

3. The method according to claim 1 , further comprising:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of historical transactions available in the historical transaction data and for which there is less than a threshold number of list prices available.

4. The method according to claim 1 , further comprising:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of list prices available and for which there is historical data available.

5. The method according to claim 1 , further comprising:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is not a new model, for which there is less than a threshold number of historical transactions available, and for which there is a threshold number of list prices available.

6. The method according to claim 1 , further comprising:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is not a new model and for which there is historical data available for a comparable make or model from a previous year.

7. The method according to claim 1 , further comprising:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is a new model and there is no historical data available for a comparable make or model from a previous year.

8. A vehicle data system, comprising:

a processor;

a non-transitory computer readable medium; and

stored instructions translatable by the processor for:

receiving a request from a user device, the request containing configuration information for a user-specified vehicle;

retrieving, from a data store, historical transaction data obtained from disparate data sources communicatively connected to the vehicle data system, the historical transaction data comprises historical transactions of a set of vehicles;

determining whether a number of the historical transactions which exists in the historical transaction data within a certain time period is less than a threshold;

responsive to the number of the historical transactions which exists in the historical transaction data within the certain time period being less than the threshold, determining a data scarcity model by traversing a decision tree having nodes, each node representing a data scarcity condition of the historical transaction data within the certain time period;

determining pricing data for the user-specified vehicle utilizing the data scarcity model determined by the vehicle data system for the user-specified vehicle; and

sending the pricing data for the user-specified vehicle to the user device.

9. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

determining most recent listing price data for each vehicle year, make, model, and trim for which an initial historical transaction exists in the historical transaction data;

producing an average listing price for the trim based on the most recent listing price data thus determined; and

creating the data scarcity model utilizing at least the historical transaction data and the average listing price for the trim.

10. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of historical transactions available in the historical transaction data and for which there is less than a threshold number of list prices available.

11. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of list prices available and for which there is historical data available.

12. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is not a new model, for which there is less than a threshold number of historical transactions available, and for which there is a threshold number of list prices available.

13. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is not a new model and for which there is historical data available for a comparable make or model from a previous year.

14. The vehicle data system of claim 8 , wherein the stored instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is a new model and there is no historical data available for a comparable make or model from a previous year.

15. A computer program product comprising a non-transitory computer readable medium storing instructions translatable by a processor of a vehicle data system for:

receiving a request from a user device, the request containing configuration information for a user-specified vehicle;

retrieving, from a data store, historical transaction data obtained from disparate data sources communicatively connected to the vehicle data system, the historical transaction data comprises historical transactions of a set of vehicles;

determining whether a number of the historical transactions which exists in the historical transaction data within a certain time period is less than a threshold;

responsive to the number of the historical transactions which exists in the historical transaction data within the certain time period being less than the threshold, determining a data scarcity model by traversing a decision tree having nodes, each node representing a data scarcity condition of the historical transaction data within the certain time period;

determining pricing data for the user-specified vehicle utilizing the data scarcity model determined by the vehicle data system for the user-specified vehicle; and

sending the pricing data for the user-specified vehicle to the user device.

16. The computer program product of claim 15 , wherein the instructions are further translatable by the processor for:

determining most recent listing price data for each vehicle year, make, model, and trim for which an initial historical transaction exists in the historical transaction data;

producing an average listing price for the trim based on the most recent listing price data thus determined; and

creating the data scarcity model utilizing at least the historical transaction data and the average listing price for the trim.

17. The computer program product of claim 15 , wherein the instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of historical transactions available in the historical transaction data and for which there is less than a threshold number of list prices available.

18. The computer program product of claim 15 , wherein the instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which there are less than a threshold number of list prices available and for which there is historical data available.

19. The computer program product of claim 15 , wherein the instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a price model for instances in which the user-specified vehicle is not a new model, for which there is less than a threshold number of historical transactions available, and for which there is a threshold number of list prices available.

20. The computer program product of claim 15 , wherein the instructions are further translatable by the processor for:

creating a plurality of data scarcity models utilizing a global multivariable regression and the historical transaction data, wherein the plurality of data scarcity models comprises a first price model for instances in which the user-specified vehicle is not a new model and for which there is historical data available for a comparable make or model from a previous year and a second price model for instances in which the user-specified vehicle is a new model and there is no historical data available for a comparable make or model from a previous year.

Assignments (2)
SECURITY INTEREST Recorded Apr 9, 2021
From: TRUECAR, INC.
To: SILICON VALLEY BANK
Reel/Frame 055873/0083 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 11, 2019
From: SWINSON, MICHAEL; O'DRISCOLL, LIN; PAINTER, SCOTT
To: TRUECAR, INC.
Reel/Frame 048297/0156 →
Continuity (8)
Continuation 15674317 · Aug 10, 2017
Continuation 14068836 · Oct 31, 2013
Continuation 12896145 · Oct 1, 2010
Continuation In Part 12556109 · Sep 9, 2009
Provisional Application 61248083 · Oct 2, 2009
Provisional Application 61095550 · Sep 9, 2008
Provisional Application 61095376 · Sep 9, 2008
Related Publication 20190172103A1 · Jun 6, 2019