Inherited Machine Learning Model for Dynamic User Pattern Adaptation
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Solution Overview
Problem
Current machine learning algorithms and data retrieval systems are static and fail to dynamically adjust to individual user patterns, leading to unsatisfactory query responses and requiring domain knowledge for data processing, making it difficult for ordinary users to access meaningful information from large datasets.
Innovation Solution
An interactive dynamic mapping engine (iDME) that uses self-learning machine learning models to adapt to individual user patterns, allowing natural language queries and mapping them to specific data sources, enabling personalized responses without requiring users to understand technical details or domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static machine learning algorithms and pre-created queries are used, then system simplicity is maintained, but adaptability to individual user patterns deteriorates
Solution Approach 1:
The patent implements dynamic machine learning models that continuously learn and adapt to individual user query patterns and preferences. The system transitions from static pre-created queries to dynamic models that evolve based on user interactions, enabling personalized information retrieval while maintaining manageable complexity through automated learning processes.
Solution Approach 2:
The system employs self-learning mechanisms where the machine learning models automatically improve themselves by analyzing user feedback and query patterns without requiring manual reconfiguration. This self-service capability allows the system to adapt to individual users while keeping the overall system architecture relatively simple.
2Measurement precision
If domain knowledge and complicated database SQL development are required, then data retrieval accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediate layer of machine learning models that act as mediators between users and complex database systems. These models translate simple user queries into sophisticated data retrieval operations, shielding users from the need to understand domain knowledge or SQL while maintaining high data retrieval accuracy through learned patterns.
Solution Approach 2:
The system replaces manual domain knowledge and SQL development with automated machine learning mechanisms. The ML models automatically learn data mappings and retrieval strategies, substituting the need for human experts to manually program complex query logic while preserving retrieval accuracy.
3Loss of information
If static reports are used, then system simplicity is maintained, but information completeness deteriorates
Solution Approach 1:
The patent transforms static reports into dynamic, personalized information presentations. The machine learning models analyze individual user needs and query patterns to dynamically generate customized reports that contain only the most relevant information for each user, reducing information loss while keeping the reporting system manageable through automated personalization.
Data Source
AI summary
Methods, systems, and apparatuses, among other things, may provide for an interactive dynamic mapping engine (iDME) for business intelligence, which may interactively obtain information for users from sources and schema unknown to the users. As a further evolution of the disclosed subject matter, there may be an identification of core characteristics of an iDME ML model and these characteristics may be made inheritable as a standalone entity by itself, also referred herein as an inherited machine learning model.