Neural Document Generation From Transcripts Without Templates
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Solution Overview
Problem
Existing automated document generation techniques rely on pre-existing templates or manually-tagged text, are restrictive, and require significant human intervention, making them time-consuming and expensive.
Innovation Solution
A computerized tool using deep learning techniques, specifically neural networks, to generate structured documents from conversation transcripts without templates or manual tagging, by executing a chapter-generation neural network, clustering algorithm, and chapter-aggregation neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document generation is used, then document quality and structure can be controlled, but time consumption and cost increase excessively
Solution Approach 1:
The system enables automated document generation by having the neural network process conversation transcripts independently, extracting relevant information and generating structured documents without requiring manual intervention for each document creation task
Solution Approach 2:
The patent replaces the manual mechanical process of document creation with an automated neural network system that processes conversation transcripts and generates structured documents algorithmically, substituting human labor with computational processes
2Productivity
If pre-existing text templates are used for automated document generation, then generation speed improves, but adaptability and generalizability deteriorate
Solution Approach 1:
The neural network dynamically adjusts its processing parameters based on the input conversation transcript, allowing it to adapt to different document types, styles, and requirements without being constrained by fixed templates, thereby achieving both speed and adaptability
Solution Approach 2:
The system transitions from static template-based generation to dynamic neural network-based generation, where the document structure and content are adaptively determined by the input data and learned patterns, enabling generalization to various document types
3Manufacturing precision
If manually-tagged text extraction is used, then document accuracy can be maintained, but human intervention requirements and complexity increase
Solution Approach 1:
The neural network automatically extracts relevant information from conversation transcripts without requiring manual tagging or annotation, separating the information extraction function from human intervention and performing it autonomously through learned patterns
Solution Approach 2:
The neural network performs multiple functions including information extraction, relevance determination, and document structure generation in a single integrated system, eliminating the need for separate manual tagging processes and reducing overall system complexity
Data Source
AI summary
Systems and techniques that facilitate document generation by applying deep learning to conversation transcripts are provided. In various embodiments, a system can comprise a receiver component that can access a set of conversation transcripts. In various aspects, the system can further comprise a document component that can generate, via execution of one or more neural networks, a structured document based on the set of conversation transcripts. More specifically, the document component can generate a set of document chapters by executing a first neural network respectively on the set of conversation transcripts. The document component can further group, via execution of a clustering algorithm, the set of document chapters into one or more clusters of document chapters. The document component can then generate the structured document by executing a second neural network on a first cluster of document chapters from the one or more clusters of document chapters.