Mathematical Reading Technology for AI Model Training
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Solution Overview
Problem
Artificial intelligence (AI) models struggle with learning and solving mathematical equations due to the grammatical structure of mathematical equations differing from natural language, leading to degraded learning performance.
Innovation Solution
A method of training AI models by converting mathematical equation grammar into a reading format similar to natural language, allowing for the generation of learning data and improved model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mathematical equations are stored using Tex grammar or MathML, then the equations can be represented in a standardized format, but the grammatical structure becomes complicated and difficult for AI models to learn
Solution Approach 1:
The patent introduces mathematical reading technology as an intermediary that converts complex mathematical equation formats (Tex grammar, MathML) into natural language reading format. This intermediary transformation layer allows AI models to process mathematical equations through familiar natural language structures, resolving the contradiction between standardized representation and learning complexity
Solution Approach 2:
The patent changes the parameter of equation representation from formal mathematical grammar (Tex/MathML) to natural language reading format. This parameter transformation makes the equations accessible to AI models that are pre-trained on natural language, improving learning performance without losing the mathematical meaning
2Reliability
If mathematical equations are converted to reading format for AI training, then learning performance improves, but the ability to perform exact mathematical search may be compromised
Solution Approach 1:
The patent segments the equation data into two distinct parts: the original mathematical equation format (for precise search and display) and the natural language reading format (for AI training and semantic search). This segmentation allows each format to serve its specific purpose without compromising the other
Solution Approach 2:
The patent uses mathematical reading technology as an intermediary that creates a bridge between the original equation format and natural language processing. This intermediary enables both precise mathematical search (through the original format) and semantic understanding (through the reading format) to coexist
3Manufacturing precision
If mathematical equations are stored in complex grammar formats, then they can be precisely represented, but text and voice search capabilities are limited
Solution Approach 1:
The patent introduces natural language reading format as an intermediary that enables text and voice search on mathematical equations. This intermediary translation allows search queries in natural language to match against equations, significantly improving search accessibility while the original equation format remains intact for precise representation
Solution Approach 2:
The patent makes the equation storage system multi-functional by maintaining both the original precise format and the natural language reading format. This universality allows the system to perform both precise mathematical operations and accessible text/voice search operations through the same stored data
Data Source
AI summary
A method of training, by a terminal, a mathematics-related artificial intelligence (AI) model, includes collecting mathematical words for training the mathematics-related AI model, converting the mathematical words into a reading sentence by using a mathematical reading technology, generating learning data for training the mathematics-related AI model based on the reading sentence, and training the mathematics-related AI model by using the learning data. The mathematical words may include a natural language part and a mathematical part.