Interactive Dynamic Mapping Engine for Adaptive Data Retrieval
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Solution Overview
Problem
Existing technologies struggle to dynamically adjust to individual user query patterns, leading to static and ineffective data retrieval processes that require domain knowledge and are time-consuming for ordinary users.
Innovation Solution
An interactive dynamic mapping engine (iDME) that uses machine learning to collect user inputs, build user query profiles, and integrate them with a base model, enabling self-learning and adaptation to individual user patterns for tailored data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static algorithms and pre-created queries are used for data retrieval, then system complexity is reduced and ease of operation is improved, but adaptability to individual user patterns deteriorates and information completeness is lost
Solution Approach 1:
The patent implements a machine learning model that dynamically adapts to individual user query patterns by continuously learning from user interactions. The system transitions from static pre-created queries to dynamic pattern-based query generation, allowing the algorithm to evolve and customize responses based on each user's specific needs and behavior over time.
Solution Approach 2:
The machine learning model performs self-learning by automatically analyzing user query patterns and improving its own performance without requiring manual reconfiguration. The system serves itself by continuously training on user interaction data, automatically adjusting to user preferences and query styles while maintaining ease of operation.
2Ease of manufacture
If static reports are used for data presentation, then ease of manufacture is improved and device complexity is reduced, but information completeness deteriorates and user-specific data availability is lost
Solution Approach 1:
The patent applies local quality by customizing report content and presentation according to each user's specific needs and query patterns. Instead of uniform static reports, the system generates tailored responses that highlight locally relevant information for each user, ensuring that each user receives the specific data subset most valuable to their role and objectives.
Solution Approach 2:
The machine learning model performs preliminary analysis of user query patterns and anticipates user information needs before queries are submitted. By pre-learning user preferences and data priorities, the system can proactively prepare and prioritize information retrieval, reducing the need for users to manually search through comprehensive but unfocused reports.
3Measurement precision
If domain knowledge and SQL development skills are required for data retrieval, then measurement precision is improved and data accuracy is enhanced, but ease of operation deteriorates and productivity is reduced
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between users and the complex data retrieval system. This intermediary automatically handles the translation of user-friendly queries into precise data retrieval operations, eliminating the need for users to possess domain knowledge or SQL skills while maintaining high data retrieval accuracy through learned patterns.
Solution Approach 2:
The system replaces manual mechanical processes of query construction and data mapping with automated machine learning-based retrieval. Instead of requiring users to manually craft SQL queries and understand data schemas, the ML model automatically performs these functions by learning from user interactions, significantly improving productivity while maintaining precision.
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 user devices from sources and schema unknown to the user devices.