Patent ID: 11886796
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 15:
16. A non-transitory computer-readable medium comprising computer-readable instructions for collaborative matter management, wherein execution of said computer-readable instructions by one or more processors causes said one or more processors to:
continuously gather intelligence to a matter from all collaborative elements of the matter, wherein said gathering intelligence comprises listening and collecting user activities and data in the collaborative elements, wherein the collaborative elements comprise a group of at least one of an evidence database, feeds, witness lists, Master Files, Players, Syncing the Master File with an Electronic Court Docket, Automatic Document Linking, Authentication, Discovery Tools, video and transcript management, Confidentiality Designations, Spoliation Analysis, Text message Management, Calendar, Print to Cloud, and notes, wherein the evidence database comprises one or more key documents and wherein the notes are associated with the one or more key documents;
actively train a machine learning algorithm using the intelligence as training data, wherein the intelligence is updated on a real time basis;
continuously apply said trained machine learning algorithm to a plurality of documents in a review tool to identify and elevate one or more documents in said plurality of documents to a queue;
the machine learning algorithm assigns a confidence level and provides a justification for each of the one or more documents elevated to the queue, wherein the justification is an indication of a basis for elevation to the queue including similarities in language with highest rated documents that have already been promoted to the evidence database and relationship with at least two players already associated with documents in the evidence database; and
the machine learning algorithm continuously reviews and adjusts the confidence level of each document in the queue and order the documents in the queue based on the confidence level.