Automated Digital Document Generation via Topic Extraction
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Solution Overview
Problem
The generation of digital documents, such as eBooks, is hindered by the subjective determination of appropriate topics, leading to overlooked informative content, as existing methods rely on manual steps and limited keyword searches within digital content stores.
Innovation Solution
An automated system that processes user-provided topic-based textual data to generate seed topics, then identifies candidate topics from structured digital content, selecting relevant ones based on predefined rules to create a digital document, reducing manual labor and including more critical content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual topic determination and keyword search methods are used, then the digital document generation process is simple to implement, but relevant informative content is overlooked and the quality of the generated document deteriorates
Solution Approach 1:
The patent replaces manual topic determination and keyword search (mechanical human operation) with an automated system that uses natural language processing and machine learning algorithms to analyze user input, extract topics, and retrieve relevant content from digital content stores, thereby preventing information loss while reducing reliance on simple manual processes
Solution Approach 2:
The patent introduces an intermediary automated processing layer between the user's topic input and the final document generation. This intermediary system includes components for topic extraction, content retrieval, and document assembly that bridge the gap between simple user input and comprehensive document output, ensuring no relevant information is lost
2Reliability
If automated topic generation systems are implemented, then more relevant content is included and document quality improves, but the system complexity and development costs increase
Solution Approach 1:
The patent segments the automated document generation system into distinct functional modules: topic extraction module, content retrieval module, document assembly module, and formatting module. Each module performs a specific function, making the overall complex system manageable, maintainable, and easier to implement while delivering high-quality output
Solution Approach 2:
The patent designs a universal automated system that can handle multiple types of digital content stores (databases, file systems, web sources) and generate various document formats. This multi-functional approach consolidates multiple capabilities into a single system, improving reliability without proportionally increasing complexity
3Loss of information
If comprehensive content retrieval is performed across entire knowledge stores, then all relevant topics are captured, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary topic extraction and candidate topic identification before full content retrieval. By pre-processing user input to extract key topics and potential related topics, the system narrows down the search scope early in the process, ensuring comprehensive coverage of relevant information while minimizing the time spent on exhaustive searches of entire knowledge stores
Solution Approach 2:
The patent implements a multi-stage retrieval process where initially only essential content is retrieved based on extracted topics, then optionally expands to include supplementary content. This partial action approach ensures core information completeness while allowing flexibility to add more content if needed, balancing time consumption with information completeness
4Adaptability or versatility
If subjective manual topic selection is used, then the process is quick and requires minimal resources, but the adaptability to user needs and inclusion of critical content is reduced
Solution Approach 1:
The patent incorporates feedback mechanisms where the system analyzes user input, extracts topics, retrieves content, and generates documents that reflect user needs accurately. The system can learn from user interactions and preferences, improving its ability to adapt to specific user requirements while maintaining high generation efficiency through automated processes
Data Source
AI summary
The present subject matter relates to generating a digital document. In one example, the present subject matter includes generating one or more seed topics based on topic-based textual data. The present subject matter further includes determining a list of candidate topics based on the one or more seed topics, where each candidate topic is associated with content corresponding to the candidate topic. Further, the present subject matter includes selecting a candidate topic from amongst the candidate topics, where the candidate topic is selected based on a pre-defined selection rule, and generating the digital document based on content associated with the candidate topic selected from amongst the candidate topics.