Autonomous biologically based learning tool

• US 8,190,543 B2
• Filed: 03/08/2008
• Issued: 05/29/2012
• Est. Priority Date: 03/08/2008
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Kaushal, Sanjeev, Patel, Sukesh Janubhai, Sugishima, Kenji
• Primary Examiner(s): Chen, Alan
• Assistant Examiner(s): Wong, Lut
• Application Number:
  US12/044,958
• Publication Number:
  US 20110131162A1
• Time in Patent Office: 1,543 Days
• Field of Search: 706/45
• US Class Current: 706/45
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.

Claims

1. A system, comprising:

a tool system that performs a task, the tool system includes a sensor component that generates data that characterizes the task;

an interaction component that receives the data, the interaction component includes an adaptor component that packages the data and conveys packaged data; and

an autonomous learning system that receives the packaged data and generates a knowledge that characterizes the packaged data and performance of the tool system, wherein the autonomous learning system comprises;

a processing platform that processes the packaged data, the processing platform includes a set of functional units that operate on the packaged data, wherein the set of functional units comprise;

an adaptive inference engine that analyzes the packaged data and infers an action to perform based at least in part on a process goal for the tool system; and

a goal component that evolves the process goal based at least in part on one of data or a context change; and

a memory platform that stores the knowledge, the memory platform includes a hierarchy of memories that includes a long term memory, a short term memory, and an episodic memory, wherein the long term memory stores a set of concepts that includes at least one of an entity, a relationship, or a procedure, and wherein a concept in the set of concepts includes a first numeric attribute that indicates relevance of a concept to a current state of the tool system, and a second numeric attribute that indicates a degree of difficulty to use the concept.

2. The system of claim 1, wherein the data includes at least one of intrinsic data or extrinsic data, and wherein the intrinsic data is generated through a functionality of the goal component that evolves the process goal and extrinsic data is received by the goal component.

3. The system of claim 1, wherein the interaction component further comprises an interaction manager that facilitates data exchange with an external actor.

4. The system of claim 3, wherein at least one of generated data or the data exchanged with the external actor includes training data.

5. The system of claim 4, wherein the training data further includes at least one of an identification of a tool variable associated with the task, a functional relationship among two or more tool variables associated with the task, a causal graph that includes a set of a priori probabilities associated with a set of tool variables related to the task and present in the causal graph and a set of conditional probabilities that relate one or more tool variables related to the task and present in the causal graph, or a set of parameters that describe a behavior of the tool system.

6. The system of claim 1, wherein a knowledge network facilitates communication among the hierarchy of memories in the memory platform, the set of functional units in the processing platform that processes the packaged data, or a combination thereof.

7. The system of claim 1, wherein the set of functional units includes at least one unit that identifies a degradation condition of the tool system.

8. The system of claim 7, wherein the at least one unit that identifies a degradation condition of the tool system further identifies a canonical pattern of data, and stores the canonical pattern of data in the memory platform.

9. The system of claim 8, wherein the at least one unit that identifies the canonical pattern of data compares two or more canonical patterns of data.

10. The system of claim 1, wherein the set of functional units includes at least one unit that identifies a set of relationships among the packaged data.

11. The system of claim 1, wherein the set of functional units includes at least one unit that identifies a performance condition of the tool system.

12. The system of claim 11, wherein the at least one unit that identifies a performance condition of the tool system ranks a set of points of failure through an assignment and propagation of a failure probability to each point of failure in the set.

13. The system of claim 1, wherein a content stored in the short term memory is temporary.