Granted Patent B2

US 12,314,658 · App. 17/389,194 · Granted May 27, 2025

Inventor: Jan Stadermann (Rheinbach, DE)

Assignee: Open Text Holdings, Inc.

G06F40/20G06F18/24155G06N20/00H04L51/04

Abstract

Claims (74)

1. A method of electronic chat production in an electronic discovery system, comprising:

receiving, by the electronic discovery system executing on a computer processor, an electronic chat comprising a set of electronic chat messages, each of the electronic chat messages having a timestamp;

determining a set of time gaps between the electronic chat messages in the set of electronic chat messages, the set of time gaps determined as respective time gaps between respective adjacent electronic chat messages in the set of electronic chat messages;

determining a set of models that model the set of time gaps, wherein determining the set of models comprises:

determining, using the set of time gaps, a Gaussian mixture model representing a mixture of Gaussian distributions;

selecting an optimum model from the set of models;

based on selecting the Gaussian mixture model as the optimum model and a text analysis of the electronic chat, performing an adaptive splitting of the set of electronic chat messages, comprising:

splitting the set of electronic chat messages into a set of conversations based on the Gaussian mixture model;

performing the text analysis on the set of conversations based on a chat subject matter identified in the set of electronic chat messages;

splitting the set of conversations based on the chat subject matter; and

storing the split set of conversations.

2. The method of claim 1 , wherein the chat subject matter is a set of chat subject matters within a parent chat subject matter grouping and wherein receiving the electronic chat comprising a set of electronic chat messages is based on a chat query criterion identifying the parent chat subject matter grouping.

3. The method of claim 1 , wherein determining a Gaussian mixture model representing a mixture of Gaussian distributions comprises:

learning the Gaussian mixture model by modeling a mixture of Gaussian distributions.

4. The method of claim 3 , wherein learning the Gaussian mixture model further comprises:

setting a maximum number of Gaussian components; and

modeling a set of Gaussian distributions from 2 through the maximum number of Gaussian components.

5. The method of claim 3 , wherein learning the Gaussian mixture model further comprises:

using an expectation maximization technique to learn the Gaussian distributions of the Gaussian mixture model.

6. The method of claim 1 , wherein selecting an optimum model from the set of models further comprises:

determining a Bayesian information criterion for each model in the set of models and selecting the optimal model from the set of models based on the Bayesian information criteria for the set of models.

7. The method of claim 1 , wherein the chat subject matter is a plurality of chat subject matters, the method further comprising, by the electronic discovery system;

applying, by a text mining and classification engine, a text analysis on the electronic chat to derive the plurality of chat subject matters for the electronic chat; and

splitting the set of conversations by identifying corresponding chat messages characterized by the chat subject matter.

8. A computer program product comprising a non-transitory, computer-readable medium storing thereon a set of computer-executable instructions, the set of computer-executable instructions comprising instructions for:

receiving, by an electronic discovery system executing on a computer processor, an electronic chat comprising a set of electronic chat messages, each of the electronic chat messages having a timestamp;

determining a set of time gaps between the electronic chat messages in the set of electronic chat messages, the set of time gaps determined as respective time gaps between respective adjacent electronic chat messages in the set of electronic chat messages;

determining a set of models that model the set of time gaps, wherein determining the set of models comprises:

determining, using the set of time gaps, a Gaussian mixture model representing a mixture of Gaussian distributions;

selecting an optimum model from the set of models;

based on selecting the Gaussian mixture model as the optimum model and a text analysis of the electronic chat, performing an adaptive splitting of the set of electronic chat messages, comprising:

splitting the set of electronic chat messages into a set of conversations based on the Gaussian mixture model;

performing the text analysis on the set of conversations based on a chat subject matter identified in the set of electronic chat messages;

splitting the set of conversations based on the chat subject matter; and

storing the split set of conversations.

9. The computer program product of claim 8 , wherein the chat subject matter is a set of chat subject matters within a parent chat subject matter grouping and wherein receiving the electronic chat comprising a set of electronic chat messages is based on a chat query criterion identifying the parent chat subject matter grouping.

10. The computer program product of claim 8 , wherein determining a Gaussian mixture model representing a mixture of Gaussian distributions comprises:

learning the Gaussian mixture model by modeling a mixture of Gaussian distributions.

11. The computer program product of claim 10 , wherein learning the Gaussian mixture model further comprises:

setting a maximum number of Gaussian components; and

modeling a set of Gaussian distributions from 2 through the maximum number of Gaussian components.

12. The computer program product of claim 10 , wherein learning the Gaussian mixture model further comprises:

using an expectation maximization technique to learn the Gaussian distributions of the Gaussian mixture model.

13. The computer program product of claim 8 , wherein selecting an optimum model from the set of models further comprises:

determining a Bayesian information criterion for each model in the set of models and selecting the optimal model from the set of models based on the Bayesian information criteria for the set of models.

14. The computer program product of claim 8 , wherein the chat subject matter is a plurality of chat subject matters, and wherein the set of computer-executable instructions comprises instructions for:

applying, by a text mining and classification engine, the text analysis on the electronic chat to derive the plurality of chat subject matters for the electronic chat; and

splitting the set of conversations by identifying corresponding chat messages characterized by the chat subject matter.

15. An electronic discovery system comprising:

a processor;

a non-transitory, computer-readable medium storing thereon a set of computer-executable instructions executable by the processor, the set of computer-executable instructions comprising instructions for:

receiving an electronic chat comprising a set of electronic chat messages, each of the electronic chat messages having a timestamp;

determining a set of time gaps between the electronic chat messages in the set of electronic chat messages, the set of time gaps determined as respective time gaps between respective adjacent electronic chat messages in the set of electronic chat messages;

determining a set of models that model the set of time gaps, wherein determining the set of models comprises:

determining, using the set of time gaps, a Gaussian mixture model representing a mixture of Gaussian distributions;

selecting an optimum model from the set of models;

based on selecting the Gaussian mixture model as the optimum model and a text analysis of the electronic chat, performing an adaptive splitting of the set of electronic chat messages, comprising:

splitting the set of electronic chat messages into a set of conversations based on the Gaussian mixture model;

performing the text analysis on the set of conversations based on a chat subject matter identified in the set of electronic chat messages;

splitting the set of conversations based on the chat subject matter; and

storing the split set of conversations.

16. The electronic discovery system of claim 15 , wherein the chat subject matter is a set of chat subject matters within a parent chat subject matter grouping and wherein receiving the electronic chat comprising a set of electronic chat messages is based on a chat query criterion identifying the parent chat subject matter grouping.

17. The electronic discovery system of claim 15 , wherein determining a Gaussian mixture model representing a mixture of Gaussian distributions comprises:

learning the Gaussian mixture model by modeling a mixture of Gaussian distributions.

18. The electronic discovery system of claim 17 , wherein learning the Gaussian mixture model further comprises:

setting a maximum number of Gaussian components; and

modeling a set of Gaussian distributions from 2 through the maximum number of Gaussian components.

19. The electronic discovery system of claim 17 , wherein learning the Gaussian mixture model further comprises:

using an expectation maximization technique to learn the Gaussian distributions of the Gaussian mixture model.

20. The electronic discovery system of claim 15 , wherein selecting an optimum model from the set of models further comprises:

determining a Bayesian information criterion for each model in the set of models and selecting the optimal model from the set of models based on the Bayesian information criteria for the set of models.

21. The electronic discovery system of claim 15 , wherein the chat subject matter is a plurality of chat subject matters, and wherein the set of computer-executable instructions comprises instructions for:

applying, by a text mining and classification engine, the text analysis on the electronic chat to derive the plurality of chat subject matters for the electronic chat; and

splitting the set of conversations by identifying corresponding chat messages characterized by the chat subject matter.

Assignments (2)

Continuity (2)

References Cited (40)