System and method for electronic chat production

Patent metadata

Field	Value
Publication number	US-11700224-B2
Application number	US-202117389190-A
Country	US
Kind code	B2
Filing date	Jul 29, 2021
Priority date	Jul 9, 2021
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

Systems, methods, and computer program products for adaptively splitting electronic chats are provided. One embodiment includes 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 in the set of electronic chat messages having a timestamp; determining a set of time gaps between the electronic chat messages from the set of electronic chat messages; determining a set of models that model the set of time gaps, selecting an optimum model from the set of models; based on selecting the single Gaussian distribution as the optimum model, determining that the electronic chat comprises a single electronic chat message or based on selecting the Gaussian mixture model as the optimum model, performing an adaptive splitting of the set of electronic chat messages into a set of conversations based on the Gaussian mixture model.

First claim

What is claimed is:

1. A method of facilitating electronic chat production, comprising:
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 in the set of electronic chat messages having a timestamp;
determining a set of time gaps between the electronic chat messages from 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 a single Gaussian distribution of the set of time gaps; and
determining, using the set of time gaps, a Gaussian mixture model representing a mixture of Gaussian distributions;
selecting the Gaussian mixture model as an optimum model from the set of models; and
performing an adaptive splitting of the set of electronic chat messages into a set of conversations at least in part based on a Gaussian distribution represented by the Gaussian mixture model having the highest mean value.

2. The method of claim 1 , wherein receiving the electronic chat comprising the set of electronic chat messages is based on a chat query criterion.

3. The method of claim 1 , wherein determining the Gaussian mixture model representing the 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 the Gaussian mixture model as the 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 Gaussian mixture model from the set of models based on the Bayesian information criteria for the set of models.

7. The method of claim 1 , further comprising, by the electronic discovery system;
determining a highest mean value distribution from the mixture of Gaussian distributions of the Gaussian mixture model, wherein performing the adaptive splitting comprises:
adaptively splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model, comprising:
selecting a time gap from the set of time gaps;
determining a probability of the selected time gap for each Gaussian distribution in the mixture of Gaussian distributions to produce a set of probabilities for the selected time gap; and
based on a determination that a highest probability from the set of probabilities for the selected time gap is for the highest mean value distribution, splitting the electronic chat based on the selected time gap to produce the set of conversations.

8. A computer program product comprising a non-transitory, computer-readable medium storing a set of computer executable instructions, the set of computer executable instructions including 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 in the set of electronic chat messages having a timestamp;
determining a set of time gaps between the electronic chat messages from 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 a single Gaussian distribution of the set of time gaps; and
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 single Gaussian distribution as the optimum model, determining that the electronic chat comprises a single electronic chat message; and
based on selecting the Gaussian mixture model as the optimum model, performing an adaptive splitting of the set of electronic chat messages into a set of conversations at least in part based on a Gaussian distribution represented by the Gaussian mixture model having the highest mean value.

9. The computer program product of claim 8 , wherein receiving the electronic chat comprising the set of electronic chat messages is based on a chat query criterion.

10. The computer program product of claim 8 , wherein determining the Gaussian mixture model representing the 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 the 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 optimum 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 set of computer-executable instructions comprises instructions for determining, by the electronic discovery system, a highest mean value distribution from the mixture of Gaussian distributions of the Gaussian mixture model, wherein performing the adaptive splitting comprises:
adaptively splitting of the set of electronic chat messages into the set of conversations based on the Gaussian mixture model, comprising selecting a time gap from the set of time gaps;
determining a probability of the selected time gap for each Gaussian distribution in the mixture of Gaussian distributions to produce a set of probabilities for the selected time gap; and
based on a determination that a highest probability from the set of probabilities for the selected time gap is for the highest mean value distribution, splitting the electronic chat based on the selected time gap to produce the set of conversations.

15. An electronic discovery system comprising:
a processor;
a non-transitory, computer-readable medium storing a set of computer executable instructions that are executable by the processor, the set of computer executable instructions including instructions for:
receiving an electronic chat comprising a set of electronic chat messages, each of the electronic chat messages in the set of electronic chat messages having a timestamp;
determining a set of time gaps between the electronic chat messages from 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 a single Gaussian distribution of the set of time gaps; and
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 single Gaussian distribution as the optimum model, determining that t

Assignees

• Open Text Holdings Inc

Inventors

• Stadermann Jan

Classifications

• H04L12/1813

  for computer conferences, e.g. chat rooms (instant messaging H04L51/04; protocols for multimedia communication H04L65/1101; arrangements for multi-party communication H04L65/403; telephonic conference arrangements H04M3/56; television conference systems H04N7/15) · CPC title

• H04L51/216 Primary

  Handling conversation history, e.g. grouping of messages in sessions or threads · CPC title

• H04L51/04

  Real-time or near real-time messaging, e.g. instant messaging [IM] · CPC title

• G06F2201/835

  Timestamp · CPC title

• H04L51/21

  Monitoring or handling of messages · CPC title