AI Scientific Document Authoring With NLP Editing Workflow
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Solution Overview
Problem
The manual process of authoring scientific documents, such as clinical study reports, is time-consuming and prone to errors, requiring substantial effort in content extraction, editing, and adherence to regulatory guidelines.
Innovation Solution
An AI-enabled system using machine learning and natural language processing automatically extracts and generates scientific documents, reducing manual effort by configuring templates, extracting content from source documents, and performing editing functions with minimal user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual authoring process is used, then writers can exercise judgment and adaptability in document creation, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service by automatically extracting content from source documents and populating the clinical study report template without requiring manual intervention for each section. The AI model autonomously performs content extraction, mapping, and document generation, allowing the system to serve itself rather than relying on manual writer input for routine tasks.
Solution Approach 2:
The patent replaces the mechanical manual process of copying, pasting, and editing content with an AI-based automated system. The machine learning model substitutes the manual mechanical operations of writers with intelligent algorithms that automatically extract, map, and integrate content from multiple source documents into the final report.
2Reliability
If manual editing and correction are performed, then quality control can be maintained, but the process is substantially difficult and time-consuming
Solution Approach 1:
The system implements feedback mechanisms where the AI model generates the document, which is then reviewed and corrected by writers. The corrected portions are fed back into the system to improve future generations. This closed-loop feedback process maintains quality while reducing the time required for editing, as the AI handles routine corrections and the writers focus on higher-level quality assurance.
Solution Approach 2:
The AI model performs preliminary actions by automatically extracting content, mapping it to the appropriate template sections, and generating the initial document draft before human review. This preliminary automation handles the time-consuming routine editing tasks, allowing writers to focus on higher-level quality control and strategic decisions.
3Loss of information
If comprehensive content extraction from multiple source documents is performed, then document completeness is improved, but the complexity of mapping and integrating content increases
Solution Approach 1:
The system segments the complex task of content extraction and mapping into distinct modular components: content extraction from source documents, section mapping to template structure, and document assembly. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining comprehensive content extraction from multiple source documents.
Solution Approach 2:
The patent introduces an intermediary section mapping algorithm that acts as a mediator between the extracted content from multiple source documents and the target document template. This intermediary layer simplifies the integration process by providing a structured mapping mechanism that handles the complexity of matching diverse source content to the standardized template structure.
4Productivity
If AI automation is implemented, then manual effort and time are reduced, but the initial system complexity and development requirements increase
Solution Approach 1:
The AI system is designed with universal multi-functionality to handle various document types, source document formats, and template structures through a single unified platform. This universality reduces the need for multiple specialized systems, thereby reducing overall implementation complexity while maintaining high productivity across different clinical study report scenarios.
Data Source
AI summary
A system and a method for automatically authoring a scientific document using a machine learning model and natural language processing (NLP) with minimal user intervention are provided. The system configures a scientific document template including multiple sections based on scientific document requirements. The system maps the sections in the scientific document template with content from the source documents by executing a section mapping algorithm and automatically generates the scientific document. The mapping includes matching the sections of the scientific document template with sections extracted from the source documents, and predicting appropriate sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model and historical scientific document information. The system executes one or more content editing functions, for example, tense conversion, additional information fetch and display, post-text to in-text conversion, etc., on the scientific document using NLP.