Open Text Holdings, Inc.

Recommind, Inc.

Jan Stadermann

Jan Puzicha

Uri Zernik

Sangeetha Yanamandra

Ketan Deshpande

Chaitanya Muppala

Denis Jager

Ingo Thon

Stephan Symons

Systems, methods, and computer program products for electronic chat production are provided. One embodiment includes 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, 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, and storing the set of electronic chat messages as the single electronic chat.

System and method for electronic chat production

Systems, methods, and computer program products for adaptively splitting electronic chats are provided. One embodiment includes receiving, by an electronic discovery system, 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, based on selecting a Gaussian mixture model as a model of the time gaps, splitting the set of electronic chat message into a set of conversations based on the Gaussian mixture model; performing a text analysis on the set of conversations based on a chat subject matter identified in the set of electronic chat messages; and splitting the set of conversations based on the chat subject matter.

Systems, methods, and computer program products for adaptively splitting electronic chats are provided. An e-discovery system comprises a computer processor and a non-transitory, computer-readable medium embodying thereon a set of computer instructions executable by the computer processor. The set of computer instructions includes instructions for: sending a chat query to a remote electronic chat service; receiving an electronic chat responsive to the chat query, the electronic chat embodying a set of electronic chat messages; adaptively splitting the set of electronic chat messages into a set of conversations, each conversation in the set of conversations comprising a subset of electronic chat messages from the set of electronic chat messages; and storing each conversation from the set of conversations as a separate document.

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.

Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new data models applicable to a set of focus documents. In addition, an end user of the automated document review system can customize and create new extraction rules applicable to text extraction from the set of focus documents. The user-defined edits to the data model and extraction rules can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.

User-defined automated document feature modeling, extraction and optimization

Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new extractor taggers within data models applicable to a set of focus documents. The user-defined edits to the extractor taggers can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.

User-defined automated document feature extraction and optimization

Systems, methods, and media for extracting and processing entity data included in an electronic document are provided herein. Methods may include executing one or more extractors to extract entity data within an electronic document based upon an extraction model for the document, selecting extracted entity data via one or more experts, each of the experts applying at least one business rule to organize at least a portion of the selected entity data into a desired format, and providing the organized entity data for use by an end user.

Hierarchical information extraction using document segmentation and optical character recognition correction

Systems and methods for extracting table information from documents are provided herein. Exemplary methods may include annotating a document with annotations that identify table cell data included therein, generating a candidate table for each of a plurality of table models using the annotated table cell data, scoring each of the candidate tables, selecting a highest scoring candidate table, and annotating the highest scoring table to produce a final table.

Growing community of inventors

Rheinbach, Germany

Jan Stadermann