System and method for fingerprinting datasets

• US 9,679,042 B2
• Filed: 01/31/2014
• Issued: 06/13/2017
• Est. Priority Date: 03/28/2012
• Status: Active Grant

• Current Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Original Assignee: Interactive Intelligence Group Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Wyss, Felix Immanuel, Vlack, Kevin
• Primary Examiner(s): Patel, Hemant
• Application Number:
  US14/169,218
• Publication Number:
  US 20140195501A1
• Time in Patent Office: 1,229 Days
• Field of Search: 379 671- 8828, 4554121-420, 704270-278
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G10L 19/018 Audio watermarking, i.e. embedding inaudible data in the audio signal
• CPC Class Codes: G10L 25/54 for retrieval
• CPC Class Codes: H03M 7/3059 Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression
• CPC Class Codes: H03M 7/3066 by means of a mask or a bit-map
• CPC Class Codes: H03M 7/3082 Vector coding for television signals, see H04N19/94
• CPC Class Codes: H04M 2203/2027 Live party detection
• CPC Class Codes: H04M 3/5158 in combination with automated outdialling systems devices for signalling identity of wanted subscriber H04M1/26

Abstract

Systems and methods for the matching of datasets, such as input audio segments, with known datasets in a database are disclosed. In an illustrative embodiment, the use of the presently disclosed systems and methods is described in conjunction with recognizing known network message recordings encountered during an outbound telephone call. The methodologies include creation of a ternary fingerprint bitmap to make the comparison process more efficient. Also disclosed are automated methodologies for creating the database of known datasets from a larger collection of datasets.

Claims

1. A method for creating a weighted compressed representation of a dataset, the method comprising the steps of:

a. computing a compressed representation of the dataset;

b. applying a transformation to the dataset to create a transformed dataset;

c. after step (b), computing another compressed representation of the transformed dataset;

d. repeating steps (b) and (c) a plurality of times to create a plurality of compressed representations of the dataset; and

e. combining the plurality of compressed representations into a weighted compressed representation of the dataset, where each weight in the weighted compressed representation is computed as a function of elements in the plurality of compressed representations.

2. The method of claim 1, wherein said compressed representation is a two-dimensional vector of first numbers.

3. The method of claim 2, wherein said first numbers are each represented with a first number of bits.

4. The method of claim 3, wherein said first number of bits is one.

5. The method of claim 1, wherein said weighted compressed representation is a two-dimensional vector of second numbers.

6. The method of claim 5, wherein said second numbers are represented with a second number of bits each.

7. The method of claim 1, wherein said function computes said each weight as a correlation coefficient of the elements in said plurality of compressed representations.

8. The method of claim 7, wherein said correlation coefficient is computed as follows:

(1) If an element is below a first predetermined threshold in a first predetermined number of said plurality of compressed representations, set the correlation coefficient to a first value;

(2) If an element is above a second predetermined threshold in a second predetermined number of said plurality of compressed representations, set the correlation coefficient to a second value;

(3) Otherwise, set the correlation coefficient to a third value.

9. The method of claim 8, wherein said first value is +1, said second value is −

1, and said third value is 0 (zero).

10. The method of claim 8, wherein said first predetermined threshold is 1 (one) and said second predetermined threshold is 0 (zero).

11. The method of claim 8, wherein said first predetermined number comprises all of the plurality of compressed representations and said second predetermined number comprises all of the plurality of compressed representations.

12. The method of claim 1, wherein said transformation comprises deleting a first number of elements from said dataset.

13. The method of claim 12, wherein said first number is one.