Transformer-Based Document Generation with Prompt Engineering
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Solution Overview
Problem
Current natural language generation systems are limited in producing variable output based on user-desired specifications, cannot handle input in various forms of text and specifications, and are not readily extendable, making them ineffective for generating tunable stylized text or content efficiently.
Innovation Solution
The AI architecture employs a transformer-based system for prompt-engineering, using seed landmark texts and images to generate expanded descriptions, combining milestone overview texts with component texts, and applying neural networks like GPT and BERT for context-sensitive text generation, while also providing a chatbot that detects user emotions and generates SEO-optimized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing natural language generation systems are used, then basic text generation is achieved, but variable output based on user-desired tunable specifications cannot be produced
Solution Approach 1:
The system segments the document generation process into distinct components: landmark text generation, component text generation, and assembly. Each segment can be independently controlled and tuned, allowing users to specify different parameters for different parts of the document while maintaining overall coherence.
Solution Approach 2:
The system dynamically adjusts generation parameters based on user specifications. The landmark texts and component texts are generated with可调 parameters that allow real-time modification of style, tone, length, and other characteristics to match user-desired specifications.
2Adaptability or versatility
If existing natural language generation systems are used, then text generation is achieved, but input in various forms of text and specifications cannot be handled
Solution Approach 1:
The system is designed to accept multiple input formats including landmark texts, outlines, keywords, and various specification types. The universal processing architecture handles different input types through a common generation framework, enabling flexible input while maintaining manageable processing complexity.
3Adaptability or versatility
If existing natural language generation systems are used, then basic content generation is achieved, but the systems are not readily extendable
Solution Approach 1:
The modular architecture segments the generation system into independent landmark text generation, component text generation, and assembly components. This segmentation enables easy extension by adding new generation modules or modifying existing ones without affecting the entire system, while keeping each module's complexity manageable.
4Productivity
If manual writing processes are used, then high-quality content can be produced, but valuable time is lost and productivity is reduced
Solution Approach 1:
The system performs preliminary generation of landmark texts and component texts that serve as structured templates. Users can then efficiently review and refine these pre-generated elements, significantly reducing the time required for manual writing while maintaining quality through targeted human input on key sections.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with generated content inform subsequent generation iterations. This allows the system to learn from user preferences and improvements, progressively enhancing content quality while maintaining high productivity through automated generation.
Data Source
AI summary
Computerized systems and methods are disclosed to generate a document with a transformer by prompt-engineering the transformer with a title and a summary to generate a description of the document; displaying a set of claims and allowing user editing of the set of claims; receiving one or more figures; receiving a part list with a plurality of element names for each figure; generating an expanded description of each element name through prompt engineering based on prior text in the document; selecting one or more boilerplate texts for major sections of the document; and organizing the document with the title, a background, the summary, a brief description of the drawings, and a detailed description.