System and method for electronic chat production

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 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.

16. The electronic discovery system of claim 15 , wherein receiving the electronic chat comprising the set of electronic chat messages is based on a chat query criterion.

17. The electronic discovery system of claim 15 , 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.

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 18 , 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 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.

21. The electronic discovery system of claim 15 , 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.