Abstract: Methods for analyzing data are disclosed. One disclosed method includes defining an intent language model for domain specific meaning behind historical enterprise data produced during operation of an enterprise; applying the historical enterprise data to build the intent language model; extracting intent element features of interest from the enterprise data to generate domain specific intent metadata; and storing the domain specific intent metadata into a database. The historical enterprise data includes findings and observations by one or more human experts in one or more service records and data associated with a problem.

Abstract: Systems and methods are described herein to improve data-driven decision-making in an enterprise by discovering intent applicable to enterprise domain.

Abstract: A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.