Cross-Platform Information Layer for Context-Aware Retrieval
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Solution Overview
Problem
Existing information management systems struggle with unstructured or differently formatted data across various applications, leading to difficulties in searching, retrieving, and storing information due to structure and formatting variations, often losing context and correlation.
Innovation Solution
A Dynamic Adaptation Information Management System (DAIMS) utilizing AI deep learning, rules, and filters for pattern recognition and frequency analysis to manage information across platforms, creating a system that learns relationships during storage and retrieval, allowing seamless information management and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If information is stored in various application formats with different structures and formatting, then information can be managed across multiple applications, but information becomes difficult to search, retrieve, and store due to structure and formatting variations
Solution Approach 1:
The patent introduces an intermediary layer (the information management system) that sits between various applications and the storage/retrieval mechanism. This intermediary automatically transforms, standardizes, and tags information from different applications into a unified format, enabling both cross-application management and efficient search/retrieval without requiring users to manually handle format conversions.
Solution Approach 2:
The system dynamically changes the parameters of information representation by automatically detecting the source application's formatting parameters and transforming them into a standardized parameter set. This includes converting different data structures, formatting styles, and organizational schemes into a universal parameter framework that maintains the original information's meaning while enabling consistent search and retrieval across all applications.
2Reliability
If information is stored with various structures and formatting across applications, then information can be preserved in its original form, but context and correlation information is lost, untagged, unorganized or otherwise not retrievable
Solution Approach 1:
The system performs preliminary actions by automatically analyzing and tagging information immediately when it is imported from various applications. This preliminary processing includes extracting context, identifying correlations, assigning metadata tags, and organizing information into a structured format before storage, ensuring that no context or correlation information is lost while preserving the original information content.
Solution Approach 2:
The patent segments information into multiple hierarchical layers: the original information content is preserved in its native format, while simultaneously creating segmented metadata layers that capture context, correlations, tags, and organizational structure. This segmentation allows the system to maintain the original information's integrity while making context and correlations separately accessible and retrievable.
3Productivity
If a system processes and organizes information from multiple applications with different formats, then information retrieval efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a universal information management system that performs multiple functions through a single integrated architecture. The system simultaneously handles information import, format detection, transformation, tagging, storage, search, and retrieval across diverse applications using a unified multi-functional platform, reducing the need for separate specialized systems while maintaining high retrieval efficiency.
Data Source
AI summary
A Dynamic Adaptation Information Management System (DAIMS) having a Listener module for monitoring information transfer activity and application communications on a user platform. A user interface allows the users to configure and select information to be viewed and specify information transfer and application communication events of interest to be tracked and processed. The system can also generate actions based on a Neural Network (NN) machine learning model, or Artificial Intelligence (AI), operating on user preferences in tracking and organizing information and transmitting this AI view to the client device.