Controllable Natural Language Generation System
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Solution Overview
Problem
Existing natural language generation systems lack the ability to generate complex language based on context, fail to provide users with control over the meaning of generated text, and are limited to operating on short text segments without contextual analysis.
Innovation Solution
The development of a controllable natural language generation system that uses semantically infused language models, trained to predict semantic features of masked tokens conditioned by their surrounding context, allowing for the generation of unique and contextually relevant text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical models are used to generate text based on word probabilities, then text generation capability is provided, but control over the meaning of generated text is lost
Solution Approach 1:
The patent introduces an intermediary layer between the statistical language model and the user interface. This intermediary processes user input, extracts semantic intent, and guides the text generation process to ensure the output aligns with user-defined meaning constraints while maintaining automation.
Solution Approach 2:
The system dynamically adjusts generation parameters and constraints based on user feedback and input analysis. The meaning control mechanisms are flexible and adaptive, allowing users to refine their requirements during the generation process rather than being locked into static constraints.
2Productivity
If language models are trained on surface-level statistics, then text generation is achieved, but contextual understanding and word sense disambiguation are insufficient
Solution Approach 1:
The patent segments the text generation process into distinct stages: contextual analysis, semantic interpretation, generation planning, and output formulation. This segmentation allows each stage to focus on specific aspects of understanding and generation, improving both efficiency and contextual accuracy.
Solution Approach 2:
The system performs preliminary contextual analysis and semantic disambiguation before text generation. By pre-processing the input to extract meaning, identify context, and resolve ambiguities ahead of time, the generation phase can proceed more efficiently with accurate guidance.
3Reliability
If systems are tailored to generate language from predefined datasets, then specific use cases are addressed, but adaptability to general purposes is reduced
Solution Approach 1:
The patent designs a universal text generation framework that can handle multiple types of tasks and domains through a single system. The core architecture remains the same, but it adapts to different use cases through configurable parameters, prompts, and contextual analysis rather than requiring separate tailored systems.
4Manufacturing precision
If automatic account for dictionary spellings and grammar rules is implemented, then language correctness is improved, but complexity of the system increases
Solution Approach 1:
The system incorporates self-correction mechanisms that automatically detect and fix spelling and grammar errors during the generation process. Rather than requiring complex external verification systems, the language model itself performs quality control, reducing overall system complexity while maintaining high language correctness.
Data Source
AI summary
The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to perform a method. The method may include initiating a writing assistant application in response to input received from a user; receiving a first user input, wherein the first user input includes a collection of two or more words that convey at least one idea; automatically constructing, using one or more trained models providing a natural language generation function, a first complete sentence option that expresses the at least one idea; causing the first complete sentence option to be shown to the user via the display; receiving a second user input, and, in response to the received second input; and causing a second complete sentence option, different from the first complete sentence option, to be shown on the display.