Method and apparatus for collecting, aggregating and providing post-sale market data for an item

• US 8,036,929 B1
• Filed: 02/26/2007
• Issued: 10/11/2011
• Est. Priority Date: 03/22/2001
• Status: Active Grant

• Current Assignee: Convergent Media Solutions LLC (Strategic Intellectual Solutions, LLC)
• Original Assignee: Teleshuttle Tech2 LLC
• Inventors: Reisman, Richard
• Primary Examiner(s): Jeanty, Romain
• Application Number:
  US11/679,077
• Time in Patent Office: 1,688 Days
• Field of Search: 705/7.29
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Various systems for collecting individual post-sale market data for products or services, and determining aggregate post-sale market data therefrom, are provided. In such systems, a sale of a product or service may trigger a third party, such as a payment provider, to arrange for the collection of individual post-sale market data from the consumer. Alternatively, such post-sale market data may be directly provided by smart devices and the like. Dependent upon the type of product or service involved, a schedule for continuously submitting post-sale market data for the product or service may be provided and the consumer may be encouraged to submit post-sale market data in accordance therewith through a variety of incentives. The received, individual, post-sale market data may be aggregated by one or more sources to determine aggregate post-sale market data for the product or service. Individual post-sale market data may be normalized based on identifiable traits of the individual submitting the post-sale market data prior to such aggregation. The submitted individual data and/or the determined aggregate post-sale market data may then be provided to consumers in order to assist them in making purchasing decisions. The collection and dissemination of post-sale market data may be accomplished, for example, via a software agent, such as a shopping bot, which may provide desired post-sale market data to a consumer prior to a sale and which may initiate collection of individual post-sale market data after a sale is recognized. Various other on-line and off-line embodiments are proposed for the collection, aggregation and dissemination of such post-sale market data.

Claims

1. A computer-based method, comprising:

obtaining via a computer operational data for each of a multiplicity of items each equipped with measurement instrumentation, the operational data being based at least in part on at least one physical quantity other than time measured by the measurement instrumentation, wherein the multiplicity of items are used by a multiplicity of different respective individual end-user consumers remote from the computer, and the operational data is automatically obtained and automatically transmitted to the computer;

storing via the computer the operational data in a database;

receiving from a requesting individual end-user consumer via the computer a request for data based upon an analysis of at least a portion of the operational data corresponding to at least a plurality of the multiplicity of items; and

generating via the computer the data based upon the analysis,

wherein the computer is operated by a party whose interest in the use of the items includes none of ownership, operation, service, maintenance, legal regulation, manufacture, distribution, and insurance of the item, and wherein further, the data based upon the analysis is generated as a service to a requesting individual end-user consumer having an interest in using an item of a same group as the multiplicity of items.

2. The method of claim 1, wherein the generating is performed by a controller or a managing system.

3. The method of claim 1, wherein each of the multiplicity of items is a member of a group of items, the method further comprising:

correlating each of the multiplicity of items with the group of items.

4. The method of claim 1, wherein the at least one physical quantity is a quantitative property determined by measurement of a continuous quantity.

5. The method of claim 1, wherein the at least one physical quantity is a base quantity other than time or a derived quantity of one or more base quantities other than time, wherein the base quantity other than time is a length, a mass, an electric current, a thermodynamic temperature, an amount of substance, or a luminous intensity.

6. The method of claim 1, wherein the multiplicity of items are used by the multiplicity of different respective users in different respective environments or conditions of use, and the analysis is with respect to a particular environment or condition of use.

7. The method of claim 1, wherein the analysis relates to a prospective use of an item with respect to a particular environment or condition of use.

8. The method of claim 7, wherein the item to which the prospective use relates to is an additional item of the multiplicity of items.

9. The method of claim 7, wherein the multiplicity of items are components of respective composite items and the prospective usage relates to the expected performance of the respective composite items with regard to a specified environment or condition.

10. The method of claim 1, wherein the operational data comprises composite item operational data or multi-item-type operational data.

11. The method of claim 10, wherein the analysis relates to one or more selected item elements within the composite item operational data or multi-item-type operational data.

12. The method of claim 1, wherein the multiplicity of different respective consumers are owners or lessors of the multiplicity of items.

13. The method of claim 1, wherein the operational data is sensed by the instrumentation in a context of normal use and made available to the computer as a byproduct of such normal use.

14. The method of claim 1, wherein the multiplicity of items are motor vehicles.

15. The method of claim 1, wherein the multiplicity of items are at least one of appliances, smart devices, PDAs, or home environmental systems.

16. The method of claim 1, wherein at least a portion of the measurement instrumentation comprises at least a component of a PDA.

17. The method of claim 1, wherein at least a portion of the measurement instrumentation comprises an automotive computer.

18. The method of claim 1, wherein the operational data includes a speed.

19. The method of claim 1, wherein the operational data includes a load.

20. The method of claim 1, wherein the operational data includes at least one of an MPG, fuel economy, energy economy, fuel parameter, energy parameter.

21. The method of claim 1, wherein the operational data includes a driving condition parameter.

22. The method of claim 1, wherein the operational data includes a trip parameter.

23. The method of claim 1, wherein the request is received from a consumer'"'"'s PDA.

24. The method of claim 1, wherein the operational data is obtained from a consumer'"'"'s PDA.

25. The method of claim 1, wherein the request is received from an automotive computer.

26. The method of claim 1, wherein the operational data is obtained from an automotive computer.

27. The method of claim 1, wherein the request is received from a smart device.

28. The method of claim 1, wherein the operational data is obtained from a smart device.

29. The method of claim 1, wherein the operational data is obtained from an automotive computer or smart device and the analysis relates to a speed.

30. The method of claim 1, wherein the operational data is obtained from an automotive computer and the analysis relates to an MPG or economy or other fuel/energy parameter.

31. The method of claim 1, wherein the operational data is obtained from a PDA and the analysis relates to a speed.

32. The method of claim 1, wherein the operational data is obtained from a PDA and the analysis relates to at least one of an MPG, fuel economy, energy economy, fuel parameter, energy parameter.

33. The method of claim 1, wherein the operational data includes a metric of economy.

34. The method of claim 1, wherein the multiplicity of items are motor vehicles, appliances, smart devices, PDAs, or home environmental systems, and the operational data includes a metric of power, speed, or economy.

35. The method of claim 34, wherein the generating is with respect to a prospective context of use.

36. The method of claim 35, wherein the context includes demographic or psychographic or behavioral data.

37. The method of claim 36, wherein the context includes location data.

38. The method of claim 1, wherein the request relates to a speed of a motor vehicle in a specified prospective usage context.

39. The method of claim 38, wherein the generating and the requesting is via a PDA.

40. The method of claim 38, wherein the generating and the requesting is via an automotive computer.

41. The method of claim 1, wherein the multiplicity of items are vehicles, the operational data comprise fuel economy data relating to respective uses of the vehicles, the analysis relates to estimation of an expected fuel economy for a specified environment or condition of use, and wherein the specified environment or condition of use corresponds at least in part to a function of a duty cycle or a usage intensity or a usage duration or a usage factor or driving habits.

42. The method of claim 1, wherein the multiplicity of items are appliances, the operational data comprise power consumption data relating to respective uses of the appliances, the analysis relates to estimation of an expected power consumption for a specified environment or condition of use, wherein the specified environment or condition of use corresponds at least in part to a function of a duty cycle or a usage intensity or a duty duration or user habits or a usage factor.

43. The method of claim 1, wherein the multiplicity of items are vehicles, the operational data comprise cost of ownership data relating to respective vehicles, the analysis relates to estimation of an expected cost of ownership for a specified environment or condition of use, and wherein the specified environment or condition of use corresponds at least in part to a function of at least one of a duty cycle or a usage intensity or a usage duration or a usage factor or driving habits.

44. The method of claim 1, wherein the operational data comprise locations and times relating to vehicle trip components and the analysis relates to the vehicle trip components.

45. The method of claim 44, wherein the analysis further relates to an estimation of an expected speed relating to the vehicle trip components.

46. The method of claim 45, wherein the analysis further relates to a particular environment or condition of use corresponding at least in part to a particular time of a trip.

47. The method of claim 46, wherein the analysis further reflects a characterization of the behavior of a user making the trip relative to the behavior of users with respect to whom the trip data was collected, wherein the behavior comprises at least in part a characterization of driving habits.

48. A computer-based method, comprising:

obtaining via a computer operational data for each of a multiplicity of items each equipped with measurement instrumentation, over a communication network, the operational data being based at least in part on at least one physical quantity other than time measured by the measurement instrumentation, wherein the multiplicity of items are used by a multiplicity of different respective individual end-user consumers remote from the computer, and the operational data is automatically obtained and automatically transmitted to the computer;

generating via the computer an analysis of at least a portion of the operational data corresponding to at least a plurality of the multiplicity of items;

communicating data based on the analysis to a plurality of individual end-user consumers; and

wherein the computer is operated by a third party information provider whose interest in the use of the multiplicity of items includes none of ownership, operation, service, maintenance, legal regulation, manufacture, distribution, and insurance of at least one of the multiplicity of items, and wherein further, the data based on the analysis is generated as a service to a requesting individual end-user consumer having an interest in using an item of a same group as the multiplicity of items.