International business machines corporation (20240256994). NEURAL NETWORK FOR RULE MINING AND AUTHORING simplified abstract
Contents
NEURAL NETWORK FOR RULE MINING AND AUTHORING
Organization Name
international business machines corporation
Inventor(s)
Ketan Gupta of Dera Bassi (IN)
Santosh Suryawanshi of Pune (IN)
Keerthana Sharath of Bangalore (IN)
Karthick Ramanujam of Chennai (IN)
NEURAL NETWORK FOR RULE MINING AND AUTHORING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256994 titled 'NEURAL NETWORK FOR RULE MINING AND AUTHORING
Simplified Explanation: The patent application describes a computer-implemented process for automatically generating business rules using external data sources, contextual analysis, and machine learning.
- **Identifying External Data Sources:** The process identifies multiple external data sources for receiving data updates.
- **Obtaining Data Updates:** Relevant data updates are obtained from the external sources.
- **Contextual Analysis:** A contextual analysis of the data updates is performed using a contextual analysis engine.
- **Generating Business Rules:** Based on the contextual analysis, updates to the collection of business rules are generated using a machine learning engine.
- **Feedback Modification:** The machine learning engine is modified based on feedback received on the updated business rules.
- **Forwarding to Management System:** The updated collection of business rules is forwarded to the business rule management system.
Potential Applications: This technology can be applied in various industries such as finance, healthcare, and e-commerce to automate the generation of business rules based on real-time data updates.
Problems Solved: This technology addresses the manual and time-consuming process of updating business rules by automating the generation process using external data sources and machine learning.
Benefits: The benefits of this technology include increased efficiency, accuracy, and adaptability in generating and updating business rules, leading to improved decision-making and operational performance.
Commercial Applications: Automated Business Rule Generation Technology for Enhanced Decision-Making and Operational Efficiency
Prior Art: Prior art related to this technology may include research papers, patents, or existing software solutions in the field of business rule management systems and machine learning algorithms.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms, contextual analysis techniques, and business rule management systems to enhance the capabilities of this technology.
Questions about Automated Business Rule Generation Technology: 1. How does this technology improve the efficiency of updating business rules compared to manual methods? 2. What are the potential challenges in implementing this technology across different industries?
Original Abstract Submitted
a computer-implemented process for automatically generating business rules to be employed by a business rule management system includes the following operations. a plurality of external data sources from which to receive data updates are identified. a data update relevant to a collection of business rules is obtained from at least one of the plurality of external data sources. using a contextual analysis engine, a contextual analysis the data update is performed. using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules is generated to form an updated collection of business rules. the machine learning engine is modified based upon feedback received on the update to the collection of business rules. the updated collection of business rules is forwarded to the business rule management system.
