Semantic Document Storage Using LNN Annotation and PSNN Retrieval
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Solution Overview
Problem
Traditional electronic storage systems face challenges in efficiently managing and retrieving large volumes of information, relying on outdated folder structures and lacking advanced annotation and retrieval methods.
Innovation Solution
A distributed document storage system utilizing Liquid Neural Networks (LNN), Internet of Things (IoT) elements, and Photonic Spiking Neural Networks (PSNN) with Blockchain technology for annotation, indexing, and data transactions, eliminating the need for dedicated indexing and optimizing storage and retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional folder structures are used to store and manage electronic information, then the system is simple to implement, but the system becomes hard to manage as the amount of information increases
Solution Approach 1:
The patent replaces traditional mechanical folder-based organization with a neural network-based semantic organization system. The LNN automatically analyzes document content, extracts keywords, and organizes documents semantically without manual folder structure management, enabling the system to handle large volumes of information adaptively.
Solution Approach 2:
The system employs self-service mechanisms where the LNN automatically annotates documents, generates keywords, and organizes storage without human intervention. The neural network continuously learns from new documents and improves organization automatically, eliminating the need for manual system configuration and management.
2Productivity
If advanced annotation and retrieval methods are implemented, then storage and retrieval efficiency is improved, but computational resources increase
Solution Approach 1:
The system performs preliminary annotation and keyword extraction using LNN during the document ingestion phase. By pre-processing and organizing documents semantically before retrieval operations, the system enables fast search and retrieval without requiring heavy computational resources during actual query operations.
Solution Approach 2:
The patent creates semantic copies and representations of documents through neural network annotations and keyword extraction. These compressed semantic representations are stored alongside original documents, allowing efficient retrieval operations to work with the smaller semantic copies rather than analyzing full document contents during search.
3Measurement precision
If dedicated indexing is implemented for document retrieval, then retrieval accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges the annotation function and indexing function into a single integrated LNN process. The neural network simultaneously extracts keywords, generates annotations, and creates organizational structures in one unified operation, eliminating the need for separate dedicated indexing systems while maintaining high retrieval accuracy through semantic understanding.
Data Source
AI summary
A document storage system includes a document digitizing device making an electronic copy of a document. A Liquid neural network (LNN) annotates the video files with document terms producing an annotated document. A semantic analyzer generates a relationship between document terms. A blockchain based data transaction forwards the annotated document to a server. A photonic spiking neural network (PSNN) server uploads and downloads annotated documents from the server. The interface will be changed automatically based on the individual's physical characteristics.