Forming a signature of parameters extracted from information

• US 6,920,440 B1
• Filed: 07/03/1997
• Issued: 07/19/2005
• Est. Priority Date: 01/21/1997
• Status: Expired due to Term

• Current Assignee: Cerebrus Solutions Ltd (Neural Technologies Limited)
• Original Assignee: Cerebrus Solutions Ltd (Neural Technologies Limited)
• Inventors: Barson, Paul Colin
• Primary Examiner(s): STARKS, WILBERT L
• Application Number:
  US08/888,361
• Time in Patent Office: 2,938 Days
• Field of Search: 706/21, 706/33
• US Class Current: 706/21
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: H04M 15/00 Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
• CPC Class Codes: H04M 15/47 Fraud detection or prevention means
• CPC Class Codes: H04M 2215/0148 Fraud detection or prevention means
• CPC Class Codes: H04M 3/36 Statistical metering, e.g. recording occasions when traffic exceeds capacity of trunks
• CPC Class Codes: H04M 3/533 Voice mail systems

Abstract

A method of storing information relating to the transmission of messages by an entity over a given time period comprises the step of creating a signature comprising a plurality of parameters related to the transmission of messages over that time period wherein the parameters comprise at least one parameter related to the transmission of messages over a portion of the period and also related to the position of the portion in the period, to enable output data to be derived from the stored information. The signature may be updated by a weighted averaging process with other more recent signatures. Application in fraud detection where signature representing information in many call detail records from a particular subscriber is fed to a neural network.

Claims

1. A method of forming a classifier, the method comprising:

providing a first time period,

providing a distinct second time period wherein the distinct second time period is consecutive to the first time period, shorter than the first time period, and more recent than the first time period,

forming a first signature from data collected during a period based on the first time period,

forming a distinct second signature from data collected during a period based on the distinct second time period,

training a first neural network based on the first signature and the distinct second signature.

2. A method according to claim 1, further comprising providing an architecture for the first neural network.

3. A method according to claim 1, further comprising evaluating the performance of the neural network.

4. A method according to claim 1, further comprising retraining the first neural network.

5. A method according to claim 1, further comprising:

providing a performance threshold, and,

retraining the first neural network based on a comparison of the performance threshold and a performance evaluation of the neural network.

6. A method according to claim 1, further comprising updating the first signature based on a weighted average of the distinct second signature.

7. A method according to claim 1, further comprising,

training a distinct second neural network based on an updated version of the first signature.

8. A method according to claim 7, further comprising

evaluating the distinct second neural network, and

based on the evaluation, utilizing the distinct second neural network as a replacement for the first neural network if the distinct neural.

9. A method according to claim 1, wherein at least one of forming a first signature and forming a distinct second signature include collecting call detail records (CDRs).

10. A method according to claim 1, wherein at least one of forming a first signature and forming a distinct second signature include specifying parameters upon which at least one of the first signature and the distinct second signature are to be formed.

11. A method according to claim 1, wherein at least one of the first signature and the distinct second signature is based on at least one of percentage of calls made and position of a portion in the time period during which the data is received.

12. A method according to claim 1, further comprising,

training a distinct second neural network based on an updated version of the first signature.

13. A method according to claim 12, further comprising

evaluating the distinct second neural network, and

based on the evaluation, utilizing the distinct second neural network as a replacement for the first neural network.

14. A method of classifying data, the method comprising:

training a first neural network based on the first signature and the distinct second signature, the first signature based on data from a first time period, the distinct second signature based on data from a distinct second time period that is con consecutive to the first time period, shorter than the first time period, and more recent than the first time period,

forming a recent signature based on data collected during a recent time period of the same duration as the distinct second time period, and,

presenting the recent signature to the first neural network.

15. A method according to claim 14, further comprising retraining the first neural network.

16. A method according to claim 14, further comprising:

providing a performance threshold, and,

retraining the first neural network based on a comparison of the performance threshold and a performance evaluation of the neural network.

17. A method according to claim 14, further comprising updating the first signature based on at least one of a weighted average of the distinct second signature and a weighted average of the recent signature.

18. A method according to claim 14, wherein at least one of the a first signature and the distinct second signature are formed based on call detail records (CDRs).

19. A method according to claim 14, further including specifying parameters upon which at least one of the first signature and the distinct second signature are to be formed.

20. A method according to claim 14, wherein at least one of the a first signature and the distinct second signature are formed based on at least one of percentage of calls made and position of a portion in the time period during which the data is received.