RULES ENGINE AS A PLATFORM FOR MOBILE APPLICATIONS

• US 20140122396A1
• Filed: 03/15/2013
• Published: 05/01/2014
• Est. Priority Date: 10/29/2012
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Swaminathan, Ashwin, Kuhn, Lukas Daniel, Ding, Li, Patton, Evan
• Application Number:
  US13/835,053
• Publication Number:
  US 20140122396A1
• Time in Patent Office: Days
• US Class Current: 706/14
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

Disclosed are systems and methods to optimize a rules engine as a platform within a computing system. The computing system may identify a context of interest, such as environment or circumstance of the computing system or a user of the computing system. Based on the identified context of interest, the rules engine platform may selectively identify rules or sets of rules that are relevant to the context of interest. Accordingly, rules or sets of rules that are irrelevant to the context of interest may be omitted from evaluation. Therefore, resources of the computing system may not consumed in some embodiments by resolving conflicts between rules and evaluating rules that result in actions that are not suitable for the context of interest.

Claims

1. A method executed by a system associated with a user for adapting a rules engine, comprising:

determining a first context of the system based on at least one of a received sample and an expected sample from a first sampling module;

identifying at least one rule of a first plurality of rules, wherein the identified at least one rule is relevant to the first context;

evaluating the identified relevant rule for the first context; and

ignoring at least one rule remaining in the first plurality of rules that is irrelevant to the first context, such that the at least one rule is not evaluated for the first context.

2. The method of claim 1, wherein identifying the at least one relevant rule comprises:

identifying, from the first plurality of rules, a relevant rule set having a second plurality of rules that are relevant to the first context, including the at least one rule.

3. The method of claim 2, further comprising:

evaluating each rule of the relevant rule set.

4. The method of claim 2, further comprising:

identifying, from the first plurality of rules, a second rule set having a third plurality of rules that are relevant to the first context and are to be evaluated.

5. The method of claim 2, wherein the first context is an office of the user and the relevant rule set is relevant for an office context.

6. The method of claim 1, wherein the first context is one of a location and a geo-fence.

7. The method of claim 6, wherein the first sampling module is one of a cellular module, a Bluetooth module, a Wi-Fi module, a user input module, a near-field communication module, a satellite positioning system module, or an application module stored in memory of the system.

8. The method of claim 1, further comprising:

identifying at least one fact that is relevant to the first context.

9. The method of claim 8, further comprising:

decomposing the relevant rule to identify the at least one fact required by the relevant rule.

10. The method of claim 8, wherein identifying the at least one fact that is relevant to the first context comprises:

identifying a relevant fact set having a plurality of facts that are relevant to the first context, including the at least one fact.

11. The method of claim 8, further comprising:

asserting the at least one fact based on the first context.

12. The method of claim 11, wherein the first context indicates that the user is driving and the at least one fact is asserted to indicate that the user is transitioning based on the first context.

13. The method of claim 8, further comprising:

asserting a second fact that is not relevant to the first context to a default.

14. The method of claim 13, wherein asserting the second fact that is not relevant to the first context comprises:

identifying a second fact set having a plurality of facts that are not relevant to the first context, including the second fact; and

asserting each fact of the plurality of facts in the second fact set to a default.

15. The method of claim 1, further comprising:

determining a sampling requirement for at least one of a second received sample and a second expected sample that is provided by a second sampling module required to satisfy the relevant rule;

computing an optimization scheme based on the sampling requirement of the relevant rule; and

modifying a sampling rate of the second sampling module based on the optimization scheme.

16. The method of claim 15, wherein modifying the sampling rate comprises:

modifying a subscription to the second sampling module.

17. The method of claim 1, further comprising:

determining a plurality of sampling requirements required to satisfy a rule set that is relevant to the first context, wherein the rule set comprises a plurality of rules including the relevant rule;

computing an optimization scheme based on the plurality of sampling requirements of the relevant rule set; and

modifying, based on the optimization scheme, a sampling rate of a second sampling module that is to provide at least one sample for a respective sampling requirement of the plurality of sampling requirements.

18. The method of claim 17, wherein computing the optimization scheme comprises:

evaluating the plurality of sampling requirements;

determining, from the evaluation of the plurality of sampling requirements, that a first sampling requirement of a first rule requires samples from the second sampling module at a first rate and a second sampling requirement of a second rule requires the samples from the second sampling module at a second rate that is different from the first rate; and

computing an optimization parameter that specifies an optimal sampling rate of the second sampling module that is satisfactory for the first rule and the second rule.

19. The method of claim 1, comprising receiving a sample provided by the first sampling module, wherein determining comprises:

identifying a mapping between the sample that is to be provided and a stored context; and

determining that the first context includes the stored context.

20. The method of claim 19, wherein the sample is a Wi-Fi signature and the stored context is a room in a building.

21. The method of claim 1, further comprising:

deriving a relationship between the first context and a second context.

22. The method of claim 21, wherein the first context indicates that the user is at a home and the second context indicates that the user is at an office and the derived relationship includes a travel duration for the user to travel from the home to the office.

23. A computing system, comprising:

a plurality of modules, including at least one sampling module configured to send a sample associated with the sampling module; and

a processor operable to execute a rules engine platform including a rules repository configured to store a plurality of rules;

a context awareness engine configured to determine a context of interest of the computing system based on at least one of a received sample and an expected sample from the sampling module; and

a rules engine configured to identify, based on the context of interest, a first set of rules included in the plurality of rules that is relevant to the context of interest and evaluate at least one rule of the first set of rules.

24. The computing system of claim 23, wherein the processor is further configured to execute a respective module of the plurality of modules.

25. The computing system of claim 23, wherein the rules engine is further configured to ignore a second set of rules included in the plurality of rules that is not relevant to the context of interest so that no rules included in the second set are evaluated for the context of interest.

26. The computing system of claim 23, wherein the rules engine is further configured to identify a third set of rules included in the plurality of rules that is relevant to the context of interest.

27. The computing system of claim 26, wherein the rules engine is further configured to identify the at least one rule for evaluation according to a conflict resolution agenda where the at least one rule conflicts with a second rule that is included in one of the first and third sets of rules that are relevant to the context of interest.

28. The computing system of claim 23, wherein the context awareness engine is configured to determine the context of interest based on one of reception of at least one sample and absence of the at least one sample.

29. The computing system of claim 23, wherein the context of interest indicates that a user of the computing system is one of in a meeting, in a discussion, on a call, near a geo-fence, near a person, at home, will be late to a meeting, and driving.

30. The computing system of claim 23, wherein the context of interest is one of an instant context, an anticipated context and a frequent context.

31. The computing system of claim 23, wherein the sampling module is one of a cellular module, a Bluetooth module, a Wi-Fi module, a user input module, a near-field communication module, a global positioning system module, or an application module stored in memory of the computing system.

32. The computing system of claim 23, wherein the rules engine is further configured to identify, based on the context of interest, at least one fact that is relevant to the context of interest.

33. The computing system of claim 32, wherein the rules engine is configured to decompose the rules included in the first set of rules that are identified based on the context of interest to identify the at least one fact that is required by the at least one rule of the first set of rules.

34. The computing system of claim 32, wherein the rules engine is further configured to assert the identified fact from one of the context of interest, reception of at least one sample and absence of the at least one sample.

35. The computing system of claim 23, wherein the rules engine is further configured to assert a plurality of facts that are not relevant to the context of interest to one of null and false.

36. The computing system of claim 23, wherein the rules engine is further configured to subscribe to a second sampling module and to modify the subscription to the second sampling module based on the context of interest.

37. The computing system of claim 36, wherein the rules engine is configured to modify the subscription by modifying one of a rate at which the second sampling module is queried, a rate at which the second sampling module is polled and a rate at which a plurality of samples received in a sampling stream from the second sampling module are stored.

38. The computing system of claim 36, wherein the context of interest indicates that a user of the computing system is near a person and further wherein the rules engine platform is configured to modify the subscription to the second sampling module so that a sample including contact information for the person is received.

39. The computing system of claim 23, wherein the rules engine platform further includes an optimization engine that is configured to determine a plurality of sampling requirements required to satisfy rules of the first set that are relevant to the context of interest;

to compute an optimization scheme based on the plurality of sampling requirements; and

to modify, based on the optimization scheme, a sampling rate of a second sampling module that is configured to provide at least one required sample.

40. The computing system of claim 23, wherein the rules engine is further configured to derive a relationship between the context of interest and a second context.

41. A computing system, comprising:

means for storing a plurality of rules in a rules repository;

means for subscribing to a plurality of modules configured to provide a plurality of samples;

means for identifying a context of interest based on the subscribing means;

means for identifying a first set of rules that are relevant to the context of interest; and

means for evaluating at least one rule included in the first set of rules.

42. A non-transitory computer-readable storage medium having instructions stored therein, which when executed by a computing system, cause the computing system to perform a method, the method comprising:

determining a first context of the computing system based on at least one of a received sample and an expected sample from a first sampling module;

identifying at least one rule of a first plurality of rules, wherein the identified at least one rule is relevant to the first context;

evaluating the identified relevant rule for the first context; and

ignoring at least one rule remaining in the first plurality of rules that is irrelevant to the first context, such that the at least one rule is not evaluated for the first context.