18122229. Guiding a Generative Model to Create and Interact with a Data Structure simplified abstract (Microsoft Technology Licensing, LLC)
Contents
Guiding a Generative Model to Create and Interact with a Data Structure
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Moshe Randall Lutz of Bellevue WA (US)
Guiding a Generative Model to Create and Interact with a Data Structure - A simplified explanation of the abstract
This abstract first appeared for US patent application 18122229 titled 'Guiding a Generative Model to Create and Interact with a Data Structure
Simplified Explanation: The technique described in the patent application uses a machine-trained pattern-completion engine to extract items of interest from unstructured data, categorize them, identify relations, and generate a structured database based on the information found.
Key Features and Innovation:
- Leveraging a pattern-completion engine to extract and categorize items of interest from unstructured data.
- Creating a structured database based on the identified information.
- Using the structured database to perform various application tasks.
- Extracting supplemental information from the structured database to answer queries.
- Presenting the supplemental information and queries to the pattern-completion engine to address queries.
Potential Applications: This technology can be applied in various fields such as data analysis, information retrieval, natural language processing, and knowledge management.
Problems Solved: The technology addresses the challenges of extracting valuable information from unstructured data, categorizing it, and using it to answer queries effectively.
Benefits: The benefits of this technology include improved data analysis, enhanced information retrieval, and more efficient query answering processes.
Commercial Applications: This technology can be utilized in industries such as finance, healthcare, e-commerce, and research institutions for data analysis, customer service automation, and knowledge management systems.
Prior Art: Researchers can explore prior art related to machine-trained pattern-completion engines, data extraction techniques, and structured database generation methods.
Frequently Updated Research: Stay updated on advancements in machine learning, natural language processing, and data analysis techniques relevant to this technology.
Questions about the Technology: 1. How does the pattern-completion engine improve the extraction of items of interest from unstructured data? 2. What are the potential limitations of using a structured database generated by this technique?
Original Abstract Submitted
A technique leverages a machine-trained pattern-completion engine to successively extract items-of-interest from unstructured data, categorize the items-of-interest, and identify relations in the unstructured data. The technique then generates a structured database based on the information it has identified. In some cases, the items-of-interest represent facts expressed by the unstructured data. The technique also leverages the structured database to perform various application tasks. In one approach, in the course of answering a query, the technique extracts supplemental information from the structured database. The technique then feeds the query and the supplemental information to the pattern-completion engine, and, in response thereto, receives output information that addresses the query. In some cases, the query is part of lengthy prompt information. Here, the technique first involves creating the structured database based on the prompt information, and then presenting the supplemental information extracted from the structured database and the query to the pattern-completion engine.
