Method and system for heuristically designing and managing a network

• US 6,363,420 B1
• Filed: 06/04/1998
• Issued: 03/26/2002
• Est. Priority Date: 06/04/1998
• Status: Expired due to Term

• Current Assignee: RPX Clearinghouse LLC (RPX Corporation)
• Original Assignee: Mortel Networks Limited
• Inventors: Coward, Leslie Andrew
• Primary Examiner(s): Rinehart, Mark H.
• Assistant Examiner(s): Thompson, Marc D.
• Application Number:
  US09/092,411
• Time in Patent Office: 1,391 Days
• Field of Search: 709/220, 709/223, 709/224, 709/221, 709/226, 709/239, 709/246
• US Class Current: 709/221
• CPC Class Codes: H04L 41/0816 the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0826 for reduction of network costs H04L41/0833 takes precedence

Abstract

A method for performing combinations of functions which can heuristically modify the functional components of the machine, separate such functional components down to the device level, and optimize the distribution of information between the functional components, by defining the domains for the network, acquiring data for each domain at each of a plurality of points in time, creating repetition clusters by looking for combinations of data that recur, identifying action to be performed based on the repetition clusters; and acquiring data from the consequences of actions performed.

Claims

1. A method performing complex combinations of functionality on a system, and for heuristically defining and modifying such functionality, the method comprising:

a) acquiring data about the system, at each of a plurality of points in time, from each of at least one data domain through which data is available from the system;

b) identifying combinations of data which recur;

c) associating a repetition cluster with each combination of data which recurs;

d) executing an action associated with a corresponding repetition cluster;

e) acquiring from each of the at least one data domains data corresponding to the consequences of executing each of the actions;

f) determining from data derived from the consequences of actions associated with corresponding repetition clusters of data which actions are desirable; and

g) increasing the probability that a desirable action will be executed when a combination of data is acquired which substantially corresponds to a repetition cluster associated wit the desirable action.

2. The method of claim 1 further comprising modulating the relative probability that a particular action will be determined to be desirable.

3. The method of claim 1 further comprising the step of entering the machine into a sleep mode wherein the distribution of information in the system is minimized.

4. The method of claim 1 wherein the system is a telecommunications network.

5. The method of claim 1 wherein the system is a military battlefield.

6. The method of claim 1 wherein the data comprises verbal speech generated by at least one human.

7. The method of claim 1 wherein the data comprises verbal speech generated by at least one human, and the actions comprise transcribing the speech into symbols readable by a computer.

8. The method of claim 1 wherein the data comprises verbal speech generated by at least one human, and the actions comprise transcribing the speech into symbols on printed matter.

9. The method of claim 1 wherein the data comprises verbal speech generated in a first language by at least one human, and the actions comprise translating the speech from the first language into a second language.

10. A machine having a functional architecture configured for performing complex, heuristically defined functions on a system, and which architecture permits the machine to be readily copied, created, and repaired, and the functionary to be readily modified, the machine comprising:

a) at least one clustering function connected for acquiring data about the system, at each of a plurality of points in time, from each of at least one data domain of the system, the at least one clustering function having an array of first devices arranged in an hierarchical organization of repetition definition and detection clusters within the machine for identifying repetitions of information conditions from the data domains and generating at least one action recommendation from the identified repetitions of information conditions; and

b) at least one competition function connected to the at least one clustering function for receiving the at least one action recommendation, the at least one competition function having an array of second devices arranged in an hierarchical organization of clusters, which second devices are adapted for associating the at least one action recommendation with system actions, and for selecting a single action recommendation according to predetermined criteria of desirability, the at least one competition function being further connected for transmitting the selected action to the network.

11. The machine of claim 10 further comprising means for entering the machine into a sleep mode to minimize the required distribution of information between devices and clusters in the clustering function.

12. The machine of claim 10 wherein the at least one clustering function further comprises a plurality of superclusters into which the array of first devices are arranged, the superclusters being connected for receiving the data about the system and for generating one of the at least one action recommendations.

13. The machine of claim 10 wherein the at least one clustering function further comprises a plurality of superclusters connected for receiving the data about the system and for generating one of the at least one action recommendations, each which superclusters comprises a plurality of clusters into which the array of first devices are arranged.

14. The machine of claim 10 wherein the at least one competition function further comprises a plurality of channels into which the array of second devices are arranged, each channel being connected for one action recommendation from the at least one cluster function, each of which devices in one channel being connected for stimulating the generation of outputs from the same channel and for inhibiting the generation of outputs from all other channels, until only a single channel produces a action recommendation.

15. The machine of claim 10 wherein each device of the array of first devices and each device of the array of second devices comprises a plurality of input lines connected for receiving one of data or output generated by another device, an output line for sending an output signal, and a memory unit to store a threshold value which must be exceeded by input received on the input lines prior to generating a signal on the output lines.

16. The machine of claim 10 wherein the array of first devices are arranged in layers within clusters within the at least one clustering function, the array of second devices are arranged in layers within clusters within the at least one competition function, and each device of the array of first devices and each device of the array of second devices comprises at least one plurality of input lines connected for receiving one of data or output generated by another device, an output line for sending an output signal, and a memory unit to store a threshold value which must be exceeded by input received on the input lines prior to generating a signal on the output lines.

17. The machine of claim 10 further comprising a structure configured within the clusters for providing and optimizing subfunctions of the cluster function.

18. The machine of claim 10 further comprising at least one device mechanism for enabling the clustering function at all levels.

19. The machine of claim 10 further comprising at least one mechanism to simulate the effects of implementing actions to improve system actions.

20. The machine of claim 10 further comprising at least one functional device which utilizes a clustering function and a competition function to prioritize actions and to provide feedback of consequences of actions.

21. The machine of claim 10 further comprising at least one functional device which utilizes a clustering function and a competition function to manage machine resources.

22. The machine of claim 10 wherein the system is a telecommunications network, and the action recommendations further comprise designing and building telecommunications networks.

23. The machine of claim 10 wherein the action recommendations are strategic maneuvers implemented on a battlefield.

24. The machine of claim 10 wherein the action recommendations are to interpret human speech.

25. The machine of claim 10 wherein the action recommendations are to interpret and slate human speech.