Machine Learning Systems and Methods for Automatic Detection of Attorney-Client Privilege in Enterprise Data

1 . A machine learning system for automatically detecting attorney-client privilege in enterprise data, comprising:

a processor; and

a memory in communication with the processor, the memory storing computer-readable instructions which, when executed by the processor, cause the processor to:

capture an electronic communication transmitted across one or more computer networks in communication with the processor;

process the electronic communication to search for a name or an e-mail address matching at least one pre-defined event identifier;

detect whether the at least one pre-defined event identifier exists in the electronic communication;

if the at least one pre-defined event identifier is not detected in the electronic communication, release the electronic communication to a recipient on the one or more computer networks; and

if the at least one pre-defined event identifier is detected in the electronic communication, perform the steps of either: (1) enhancing and releasing the communication, or (2) blocking the electronic communication from being transmitted to a recipient.

2 . The system of claim 1 , wherein processor captures the electronic communications using one or more of a network firewall, a network analyzer, a packet sniffer, a packet capturer, e-mail monitoring software, or a filter.

3 . The system of claim 1 , wherein the processor searches for the name or the e-mail address using a matching algorithm or a machine learning algorithm.

4 . The system of claim 1 , wherein the processor obtains at least one consent from a user of the one or more computer networks prior to capturing the electronic communication.

5 . The system of claim 1 , wherein the electronic communication comprises one or more of a keystroke, a click, an electronic mail transmission, a website access, or a file download.

6 . The system of claim 1 , wherein the processor enhances the communication by adding an attorney as a recipient of the electronic communication.

7 . The system of claim 1 , wherein the processor enhances the communication by adding an attorney-client disclaimer to the electronic communication.

8 . The system of claim 1 , wherein after blocking the electronic communication, the processor transmits the electronic communication to an attorney for review.

9 . The system of claim 8 , wherein the processor generates a graphical user interface for allowing the attorney to review the electronic communication.

10 . The system of claim 8 , wherein the electronic communication is released to the recipient by the processor after the attorney reviews the electronic communication.

11 . The system of claim 1 , wherein after blocking the electronic communication, the processor processes the electronic communication using a machine learning algorithm to score the electronic communication.

12 . A machine learning method for automatically detecting attorney-client privilege in enterprise data, comprising:

capturing by a processor an electronic communication transmitted across one or more computer networks in communication with the processor;

processing the electronic communication to search for a name or an e-mail address matching at least one pre-defined event identifier;

detecting whether the at least one pre-defined event identifier exists in the electronic communication;

if the at least one pre-defined event identifier is not detected in the electronic communication, releasing the electronic communication to a recipient on the one or more computer networks; and

if the at least one pre-defined event identifier is detected in the electronic communication, performing the steps of either: (1) enhancing and releasing the communication, or (2) blocking the electronic communication from being transmitted to a recipient.

13 . The method of claim 12 , wherein capturing the electronic communication includes utilizing one or more of a network firewall, a network analyzer, a packet sniffer, a packet capturer, e-mail monitoring software, or a filter.

14 . The method of claim 12 , further comprising searching for the name or the e-mail address using a matching algorithm or a machine learning algorithm.

15 . The method of claim 12 , further comprising obtaining at least one consent from a user of the one or more computer networks prior to capturing the electronic communication.

16 . The method of claim 12 , wherein the electronic communication comprises one or more of a keystroke, a click, an electronic mail transmission, a website access, or a file download.

17 . The method of claim 12 , further comprising enhancing the communication by adding an attorney as a recipient of the electronic communication.

18 . The method of claim 12 , further comprising enhancing the communication by adding an attorney-client disclaimer to the electronic communication.

19 . The method of claim 12 , further comprising transmitting the electronic communication to an attorney for review.

20 . The method of claim 19 , further comprising generating a graphical user interface for allowing the attorney to review the electronic communication.

21 . The method of claim 19 , further comprising releasing the electronic communication to the recipient after the attorney reviews the electronic communication.

22 . The method of claim 12 , further comprising processing the electronic communication using a machine learning algorithm to score the electronic communication.