Reasoning engines

• US 9,262,719 B2
• Filed: 03/22/2012
• Issued: 02/16/2016
• Est. Priority Date: 03/22/2011
• Status: Active Grant

• Current Assignee: Nant Holdings IP, LLC (NantWorks, LLC)
• Original Assignee: Patrick Soon-Shiong
• Inventors: Soon-Shiong, Patrick
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Kim, David H
• Application Number:
  US14/006,932
• Publication Number:
  US 20140129504A1
• Time in Patent Office: 1,426 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/041 Abduction
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06T 9/00 Image coding bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00
• CPC Class Codes: G06V 20/00 Scenes; Scene-specific elements control of digital cameras H04N23/60
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

Claims

1. A reasoning engine comprising:

a data interface configured to acquire environment data from sensors, the environment data representing a first and a second aspect of an environment;

at least one inference engine coupled within the data interface and configured to;

receive an inquiry relating to the first and second aspect of the environment;

recognize the first and second aspects as first and second target objects respectively, each having object attributes, as a function of the environment data;

select a reasoning rules set from available reasoning rules sets as a function of the environment data and object attributes of the first and second target objects, and

establish a hypothesis according to the selected reasoning rules set, the hypothesis representing that the first and second target objects have a suspected correlation; and

configure an output device to present the hypothesis along with a reasoning supporting the hypothesis.

2. The engine of claim 1, wherein the available reasoning rules sets include rules operating according to at least one of the following:

deductive reasoning, abductive reasoning, case-based reasoning, inductive reasoning, metaphorical mapping, and fuzzy logic.

3. The engine of claim 1, wherein the first and second aspects comprise scene objects in a real-world scene.

4. The engine of claim 3, wherein the object attributes of the target objects comprises attributes derived from a digital representation of the scene objects.

5. The engine of claim 1, wherein the environment data represents a real-world environment.

6. The engine of claim 1, wherein the environment data represents a virtual environment.

7. The engine of claim 1, wherein the environment data represents an augmented reality environment.

8. The engine of claim 1, wherein the environment data represents a data space.

9. The engine of claim 1, wherein the environment data comprises a previously generated hypothesis.

10. The engine of claim 1, wherein the environment comprises sensor data obtained from the sensors.

11. The engine of claim 10, wherein the sensor data represents human modalities.

12. The engine of claim 11, wherein the sensor data comprises at least one of the following modal data types:

audible data, visual data, kinesthetic data, olfactory data, and taste data.

13. The engine of claim 1, wherein the environment data represents non-human modalities.

14. The engine of claim 13, wherein the sensor data comprises at least one of the following types of data:

news data, weather data, experimental data, geological data, biometric data, accelerometer data, and magnetometer data.

15. The engine of claim 1, wherein the at least one inference engine comprises a networking node.

16. The engine of claim 14, wherein the networking node comprises at least one of the following types of nodes:

a switch, a router, a web server, a gateway, an access point, a search engine, and a firewall.

17. The engine of claim 1, wherein the at least one inference engine is a member of a distributed inference engine.

18. The engine of claim 1, wherein the data interface comprises at least one of the following:

a mobile phone, an appliance, a game system, a sensor web, and a garment.

19. The engine of claim 1, wherein the inference engine comprises an instance of a personalized inference engine.

20. The engine of claim 1, wherein the hypothesis comprises a representation of an intuition.

21. The engine of claim 1, wherein the hypothesis comprises a representation of an imaginative construct.

22. The engine of claim 1, wherein the hypothesis comprises a representation of a creative work.

23. The engine of claim 1, wherein the hypothesis comprises a representation of an emotion.

24. The engine of claim 1, wherein the environment comprises a global environment.

25. The engine of claim 24, wherein the first and the second aspects are associated with different local environments.

26. The engine of claim 25, wherein the different local environments are geographically separated.

27. The engine of claim 1, further comprising at least one validation module coupled with the inference engine and configured to:

request updated environment data with respect to the first and second aspect via the data interface and according to a validation plan derived from the suspected correlation,

generate a derived relationship between the first and second aspect as a function of the validation plan and the updated environment data, and

determine a validity of the hypothesis by comparing the derived relationship to the suspected correlation.

28. The engine of claim 27, wherein the data interface is further configured to control acquisition of the environmental data according to the validation plan.

29. The engine of claim 28, wherein the validation module couples with the data interface and configured to submit sensor commands to the data interface controlling acquisition of the environmental data according to the validation plan.

30. The engine of claim 27, further comprising a presentation module configured to inject data back into the environment via the data interface according to the validation plan.