Contextual Commerce Systems and Methods

• US 20110153452A1
• Filed: 03/01/2011
• Published: 06/23/2011
• Est. Priority Date: 05/20/2004
• Status: Active Grant

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Flinn, Steven Dennis, Moneypenny, Naomi Felina
• Granted Patent: US 8,380,579 B2
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

A system and method for context-sensitive, adaptive commerce is described that features recommendations of products and/or services based on system usage behaviors and supplier contextual information such as inventory levels. Adaptive commerce may include the bundling of a plurality of products and generating an associated price based, at least in part, on usage behavior information. Other contextual information related to the customer or supplier may also be applied in generating recommended products, bundles of products, product configurations, and corresponding prices.

Claims

1. An adaptive commerce system, comprising:

a plurality of captured usage behaviors associated with one or more users of a computer-implemented system;

an inventory level; and

a function implemented on a processor-based computing device that generates a recommendation of one or more products based, at least in part, on the plurality of usage behaviors and the inventory level.

2. The system of claim 1, further comprising:

commerce contextual information, the commerce contextual information being selected from a group consisting of a customer context, a purchase history, a product lifespan, and a product attribute.

3. The system of claim 1, further comprising:

a pricing function, wherein the pricing function generates a price for the one or more recommended products.

4. The system of claim 1, further comprising:

a product bundling function, wherein the product bundling function determines a plurality of products to be recommended.

5. The system of claim 1, further comprising:

a product configuration-generating function.

6. The system of claim 1, further comprising:

a price discovery function.

7. The system of claim 1, further comprising:

supplier contextual information, the supplier contextual information being selected from a group consisting of a production cost, a production plan, and supply chain considerations.

8. An adaptive commercial solutions system, comprising:

a plurality of captured usage behaviors associated with one or more users of a computer-implemented system;

a product bundling function that generates a bundle comprising a plurality of products based, at least in part, on the plurality of usage behaviors; and

a pricing function implemented on a processor-based computing device that generates a price for the bundle based, at least in part, on the plurality of usage behaviors.

9. The system of claim 8, further comprising:

a price discovery function, the price discovery function being selected from a group consisting of a price elasticity function, a pricing experimental design function, and a collective price formation process function.

10. The system of claim 8, further comprising:

supplier contextual information.

11. The system of claim 8, further comprising:

customer contextual information.

12. The system of claim 8, further comprising:

a recommended bundle comprising the bundle and the price.

13. The system of claim 8, further comprising:

a first usage behavior corresponding to a first usage behavior category and a second usage behavior corresponding to a second usage behavior category.

14. An adaptive commerce method, comprising:

accessing a plurality of usage behaviors associated with one or more users interacting with a processor-based computing device; and

generating by application of a computer-implemented function a recommendation of one or more products based, at least in part, on the plurality of usage behaviors and an inventory level.

15. The method of claim 14, further comprising:

applying commerce contextual information, the commerce contextual information being selected from a group consisting of a customer context, a purchase history, a product lifespan, and a product attribute.

16. The method of claim 14, further comprising:

applying a pricing function, wherein the pricing function generates a price for the one or more recommended products.

17. The method of claim 14, further comprising:

applying a product bundling function, wherein the product bundling function determines a plurality of products to be recommended.

18. The method of claim 14, further comprising:

applying a product configuration-generating function.

19. The method of claim 14, further comprising:

applying a price discovery function.

20. The method of claim 14, further comprising:

applying supplier contextual information, the supplier contextual information being selected from a group consisting of a production cost, a production plan, and supply chain considerations.