Reasoning Graph Transliteration via Templating Libraries
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Solution Overview
Problem
The manual coding or use of custom software for translating reasoning graphs between programming languages is costly and time-consuming, hindering efficient translation and insight determination.
Innovation Solution
A method and system for automatically transliterating reasoning graphs using a transliteration library that correlates reasoning functions with templating functions, generating a templating language representation, and converting it into high-level programming or documentation language, ultimately producing machine executable code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coding or custom software is used for translating reasoning graphs between programming languages, then translation accuracy can be maintained, but translation time and cost increase significantly
Solution Approach 1:
The system enables reasoning graphs to translate themselves automatically through the transliteration library that correlates reasoning functions with templating functions, eliminating the need for manual coding while maintaining accuracy through structured correspondence rules
Solution Approach 2:
The patent replaces manual mechanical coding processes with an automated computational system that uses transliteration libraries and compilers to convert reasoning graphs between programming languages, significantly reducing translation time while preserving accuracy through systematic translation rules
2Manufacturing precision
If manual coding is used for translating reasoning graphs, then translation precision can be controlled, but productivity decreases
Solution Approach 1:
The transliteration library enables automatic self-translation of reasoning graphs by correlating reasoning functions with templating functions, maintaining precision through structured correspondence while dramatically improving productivity through automation
Solution Approach 2:
The system changes the state of reasoning graphs from manual code to templated representations that can be automatically processed through compilers, transforming the translation process from low-productivity manual coding to high-productivity automated processing while preserving precision through controlled translation parameters
3Speed
If automated transliteration is implemented, then translation speed increases, but system complexity increases
Solution Approach 1:
The patent segments the translation system into distinct components: reasoning graphs, transliteration libraries with templating functions, and compilers. This modular segmentation enables automated high-speed translation while managing complexity through separate, well-defined modules rather than a monolithic system
Solution Approach 2:
The transliteration library acts as an intermediary between reasoning graphs and target programming languages, providing standardized templating functions that simplify the translation process. This intermediary layer manages complexity by abstracting the translation logic while enabling fast automated conversion
4Reliability
If custom software translators are used, then translation reliability can be maintained, but development cost increases
Solution Approach 1:
The system eliminates the need for expensive custom software development by enabling reasoning graphs to translate themselves through automated transliteration libraries, reducing development costs while maintaining reliability through systematic translation correspondence rules
Solution Approach 2:
The patent uses templating functions that copy and adapt reasoning graph structures to target programming languages, providing reliable translation through reusable templates rather than custom-coded translators, significantly reducing development costs while maintaining translation reliability
Data Source
AI summary
Embodiments disclosed herein relate to methods and systems for transliterating reasoning graphs and using the same to determine insights.