Dynamic Keyword Dictionary for Accurate Document Summarization
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Solution Overview
Problem
Existing document analysis systems face challenges in accuracy, adaptability, and continuous improvement due to reliance on traditional NLP techniques, lack of dynamic keyword updates, and limited integration with advanced AI models, leading to suboptimal summarization and inefficient handling of large document volumes.
Innovation Solution
A device and system utilizing a dynamic keyword dictionary and AI-driven analysis, enhanced by a private language model, that extracts key sections, prioritizes information, removes noise, and continuously updates based on user feedback to improve accuracy and relevance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional NLP techniques and fixed keyword lists are used for document analysis, then the system is simple to implement, but the accuracy and adaptability to new terminology deteriorate
Solution Approach 1:
The patent implements a dynamic keyword dictionary that automatically updates with new terms and terminology encountered in processed documents. This dynamic mechanism allows the system to adapt to new industry-specific language and evolving document content without manual intervention, thereby improving summarization accuracy while maintaining system simplicity
Solution Approach 2:
The system incorporates feedback loops where processed documents and user interactions continuously refine the keyword dictionary. This feedback mechanism enables the system to learn from new data, improve its understanding of domain-specific terminology, and enhance summarization accuracy over time while keeping the implementation straightforward
2Device complexity
If fixed keyword lists are used for text extraction, then the system structure is simple, but the adaptability to new or industry-specific terminology deteriorates
Solution Approach 1:
The patent transforms the static keyword list into a dynamic keyword dictionary that automatically evolves by incorporating new terms from processed documents. This dynamic structure maintains relative simplicity while dramatically improving adaptability to new and industry-specific terminology through continuous automatic updates
Solution Approach 2:
The system performs self-updating of its keyword dictionary without external intervention. It automatically identifies, extracts, and incorporates new terminology from processed documents, enabling the system to adapt to domain-specific language and new concepts while maintaining a simple overall structure
3Productivity
If traditional systems process large volumes of documents, then the document throughput capacity is limited, but the processing time and memory usage increase
Solution Approach 1:
The patent extracts and stores frequently occurring terms and important keywords into a dynamic keyword dictionary during initial processing. This extraction mechanism enables faster subsequent processing of large document volumes by leveraging the pre-built vocabulary, thereby increasing throughput without proportionally increasing processing time
Solution Approach 2:
The system performs preliminary processing to build and update the keyword dictionary before handling large batches of documents. This preliminary action creates a reusable vocabulary resource that accelerates subsequent document processing, enabling high throughput while minimizing the time cost per document
4Ease of manufacture
If keyword-based approaches are used for text extraction, then the method is simple, but the system gets misled by irrelevant keywords and misses important information
Solution Approach 1:
The patent assigns different weights and priorities to keywords based on their relevance and frequency in the dynamic dictionary. This local quality differentiation allows the system to distinguish between important and irrelevant keywords, improving information extraction reliability while maintaining the simplicity of the keyword-based approach
Solution Approach 2:
The system dynamically adjusts keyword weights and parameters based on their occurrence patterns and relevance in processed documents. This parameter change mechanism enables the system to adapt to domain-specific language and improve extraction reliability while keeping the overall method simple
Data Source
AI summary
The present invention discloses a device (100), a system (200), and a method (300) for analysing a document received from one or more users. The invention includes a system (200) for document analysis. The system (200) comprises a user interface (101) to interact with users. The user interface (101) generates queries and receives documents from users. The documents are then transmitted to a device (100) equipped with processors (102) for analysis. The processors (102) extract key sections from the document, prioritize them, remove noisy data, and generate a summary. An interactive tool (105) within the user interface (101) facilitates user feedback on the analysis. The user feedback is used to update a keyword dictionary (104) at predetermined intervals, allowing the system (200) to continuously improve its document analysis capabilities.