Computer-implemented system and method for assigning concept classification suggestions

• US 10,083,396 B2
• Filed: 06/22/2015
• Issued: 09/25/2018
• Est. Priority Date: 07/28/2009
• Status: Active Grant

• Current Assignee: Nuix North America Inc. (Nuix Ltd.)
• Original Assignee: FTI Consulting Incorporated
• Inventors: Knight, William C.
• Primary Examiner(s): Choi, Carol
• Application Number:
  US14/746,725
• Publication Number:
  US 20150286957A1
• Time in Patent Office: 1,191 Days
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A computer-implemented system and method for assigning concept classification suggestions is provided. One or more clusters of concepts is accessed. The concepts include uncoded concepts and one or more reference concepts selected from a set of reference concepts. Each reference concept is associated with a classification code. One of the uncoded concepts is selected for classification in one such cluster. At least one of the reference concepts located closest to the selected uncoded concept is identified. The classification code of the closest located reference concept is assigned to the selected uncoded concept as a suggested classification code.

Claims

1. A computer-implemented system for assigning concept classification suggestions, comprising:

a database to store one or more clusters of concepts comprising uncoded concepts and one or more reference concepts selected from a set of reference concepts, wherein each reference concept is associated with a classification code; and

a processor to execute modules, comprising;

a display module to represent each uncoded concept and reference concept in one such cluster via a shape; and

an assignment module to perform the following;

assign to one of the uncoded concepts in the cluster the classification code of the closest located reference concept to the uncoded concept as a suggested classification code;

fill a portion of the shape representing the uncoded concept with a color representing the suggested classification code; and

receive from a user a confirmation of the suggested classification code for the uncoded concept; and

completely fill the shape representing the uncoded concept with the same color upon confirmation of the suggested classification code from the user by filling a remaining portion of the shape with the same color;

assign a further classification code to a further uncoded concept in the cluster as a further suggested classification code;

fill at least a portion of the shape representing the further uncoded concept with a color representing the further suggested classification code;

receive from the user a different classification code for the further uncoded concept; and

display the further uncoded concept with the color of the further suggested classification code and a color of the different classification code received from the user.

2. The system according to claim 1, further comprising:

a classification display module to display at least one of the classification codes for each reference concept with respect to the shape for that reference concept.

3. The system according to claim 1, further comprising:

a similarity module to determine a confidence level for the suggested classification code and to accept the suggested classification code for the uncoded concept when the confidence level satisfies a predetermined threshold for similarity.

4. The system according to claim 3, further comprising:

a similarity determination module to determine the similarity via at least one of a measure of similarity and a measure of dissimilarity between the uncoded concept and the closest located reference concept.

5. The system according to claim 1, further comprising:

a database to store the uncoded concept and accepted classification code with the set of reference concepts.

6. The system according to claim 1, wherein each of the reference concepts and uncoded concepts represents at least one document.

7. The system according to claim 6, further comprising:

a document assignment module to assign the suggested classification code of the uncoded concept to the at least one document represented by the uncoded concept as a suggested classification code.

8. The system according to claim 1, wherein the closest located reference document is most similar to the uncoded document.

9. A computer-implemented method for assigning concept classification suggestions, comprising the steps of:

accessing one or more clusters of concepts comprising uncoded concepts and one or more reference concepts selected from a set of reference concepts, each reference concept associated with a classification code;

representing each uncoded concept and reference concept in one such cluster via a shape;

assigning to one of the uncoded concepts the classification code of the closest located reference concept to the uncoded concept as a suggested classification code;

filling a portion of the shape representing the uncoded concept with a color representing the suggested classification code;

receiving from a user a confirmation of the suggested classification code for the uncoded concept;

completely filling the shape representing the uncoded concept with the same color upon confirmation of the suggested classification code from the user by filling a remaining portion of the shape with the same color;

assigning a further classification code to a further uncoded concept in the cluster as a further suggested classification code;

filling at least a portion of the shape representing the further uncoded concept with a color representing the further suggested classification code;

receiving from the user a different classification code for the further uncoded concept; and

displaying the further uncoded concept with the color of the further suggested classification code and a color of the different classification code received from the user,

wherein the steps are performed by a suitably programmed computer.

10. The method according to claim 9, further comprising:

displaying the classification code for each reference concept with respect to the shape for that reference concept.

11. The method according to claim 9, further comprising:

determining a confidence level for the suggested classification code;

accepting the suggested classification code for the uncoded concept when the confidence level satisfies a predetermined threshold for similarity.

12. The method according to claim 11, further comprising:

determining the similarity via at least one of a measure of similarity and a measure of dissimilarity between the uncoded concept and the closest located reference concept.

13. The method according to claim 9, further comprising:

storing the uncoded concept and accepted classification code with the set of reference concepts.

14. The method according to claim 9, wherein each of the reference concepts and uncoded concepts represents at least one document.

15. The method according to claim 14, further comprising:

assigning the suggested classification code of the uncoded concept to the at least one document represented by the uncoded concept as a suggested classification code.

16. The method according to claim 9, wherein the closest located reference document is most similar to the uncoded document.