Abstract: In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.

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.

Abstract: Systems are disclosed to improve data-driven decision-making in an enterprise by discovering intent that is applicable to an enterprise domain.

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: 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.