Location-Aware Adaptive Systems and Methods

• US 20070073554A1
• Filed: 11/13/2006
• Published: 03/29/2007
• Est. Priority Date: 04/08/2005
• Status: Abandoned Application

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Moneypenny, Naomi, Flinn, Steven
• Application Number:
  US11/559,204
• Publication Number:
  US 20070073554A1
• Time in Patent Office: Days
• US Class Current: 705/1.100
• 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

Abstract

A system and method for location-aware adaptive commercial processes is disclosed. Location-aware adaptive systems enable recommendations of products or services to be generated and delivered to a potential customer that are contextualized for the potential customer'"'"'s location, as well as in accordance with inferences derived from captured usage behaviors associated with the potential customer. Multiple instances of the location-aware adaptive systems can collectively adapt over time. Novel sales processes can be based on the application of location-aware adaptive systems.

Claims

1. A location-aware collectively adaptive system, comprising:

captured usage behaviors, wherein the usage behaviors are associated with one or more users;

commercial contextual information;

a function to automatically determine the location of a user; and

a function to generate an adaptive recommendation based, at least in part, on the automatically determined location of the user, a previously captured usage behavior, and an item of the commercial contextual information.

2. The system of claim 1, wherein captured usage behaviors, wherein the usage behaviors are associated with one or more users comprises:

usage behaviors associated with categories of usage behaviors, the categories of usage behaviors being selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, physical location patterns, attention patterns, and usage temporal patterns.

3. The system of claim 1, wherein commercial contextual information comprises:

a commerce context function, the commerce context function being selected from a group consisting of a customer context function, a purchase history function, and a product or service attribute function.

4. The system of claim 1, wherein a function to generate an adaptive recommendation based, at least in part, on the automatically determined location of the user, a previously captured usage behavior, and an item of the commercial contextual information comprises:

a recommendation, the recommendation selected from a group consisting of a product, a service, a product or service bundle, a price corresponding to one or more products or services, and an advertisement.

5. The system of claim 1, further comprising:

a first computer-based adaptive system instance; and

a second computer-based adaptive system instance.

6. The system of claim 5, further comprising:

a means for the first adaptive system instance to establish commercial context for interacting with the second adaptive system; and

a means for the first location-aware adaptive system to influence the adaptive recommendations of the second adaptive system.

7. The system of claim 5, further comprising:

a means for the first adaptive system instance to establish a commercial context for interacting with the second adaptive system; and

a means for the adaptive recommendations of the first location-aware adaptive system to be influenced by the second adaptive system.

8. The system of claims 6 or 7, further comprising:

one or more mobile location-aware adaptive systems.

9. A location-aware collectively adaptive method, comprising:

capturing usage behaviors, wherein the usage behaviors are associated with one or more users;

determining automatically the location of a user;

determining commercial contextual information; and

generating an adaptive recommendation based, at least in part, on the automatically determined location of the user, a previously captured usage behavior, and an item of the commercial contextual information.

10. The method of claim 9, wherein capturing usage behaviors, wherein the usage behaviors are associated with one or more users comprises:

storing usage behaviors associated with categories of usage behaviors, the categories of usage behaviors being selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, physical location patterns, attention patterns, and usage temporal patterns.

11. The method of claim 9, wherein determining commercial contextual information comprises:

determining commercial context information, the commercial context information being selected from a group consisting of customer context information, purchase history information, and product or service attribute information.

12. The method of claim 9, wherein generating an adaptive recommendation based, at least in part, on the automatically determined location of the user, a previously captured usage behavior, and an item of the commercial contextual information comprises:

generating a recommendation, the recommendation selected from a group consisting of a product, a service, a product or service bundle, a price corresponding to one or more products or services, and an advertisement.

13. The method of claim 9, further comprising:

applying a first computer-based adaptive system instance; and

interacting with a second computer-based adaptive system instance.

14. The method of claim 13, further comprising:

establishing a commercial context for the first adaptive system instance to interact with the second adaptive system; and

enabling the first adaptive system to influence the adaptive recommendations of the second adaptive system.

15. The method of claim 13, further comprising:

establishing a commercial context for the first adaptive system instance to interact with the second adaptive system; and

enabling the adaptive recommendations of the first location-aware adaptive system to be influenced by the second adaptive system.

16. The method of claims 14 or 15, further comprising:

applying one or more mobile location-aware adaptive systems.

17. An adaptive sales method, comprising:

monitoring automatically the location of a mobile computer-based system associated with a potential customer;

determining one or more usage behaviors of the potential customer;

generating a recommendation based, at least in part, on the automatically determined location of the user and the one or more usage behaviors; and

delivering the recommendation to the potential customer.

18. The method of claim 17, wherein determining one or more usage behaviors of the potential customer comprises:

determining usage behaviors associated with categories of usage behaviors, the categories of usage behaviors being selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, physical location patterns, attention patterns, and usage temporal patterns.

19. The method of claim 17, further comprising:

determining commercial context information, the commercial context information being selected from a group consisting of customer context information, purchase history information, and product or service attribute information.

20. The method of claim 17, wherein generating a recommendation based, at least in part, on the automatically determined location of the user and the one or more usage behaviors comprises:

generating a recommendation, the recommendation selected from a group consisting of a product, a service, a product or service bundle, a price corresponding to one or more products or services, and an advertisement.