Mathematical Chat Bot Non-Numerical Input Processing
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Solution Overview
Problem
Current chatbots are limited to simple task-driven conversations and cannot perform mathematical equations with non-numerical inputs, such as images or text, failing to provide logical results for inputs that are not directly numerical.
Innovation Solution
A system and method for automated mathematical chatting that converts non-numerical inputs into vectors, performs mathematical equations, decodes results into natural language features, and searches databases for outputs, enabling chatbots to respond to queries with non-numeric inputs by encoding features into vectors, executing equations, and selecting answers based on probability scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If chatbots use simple keyword matching and database retrieval methods, then they can operate with simple architecture and fast response, but they are limited to task-driven conversations and cannot perform mathematical equations with non-numerical inputs
Solution Approach 1:
The patent introduces multiple intermediary components to bridge non-numerical inputs and mathematical processing: a feature extractor that converts images/text into feature representations, a mathematical knowledge graph that maps concepts to operations, and a vectorizer that transforms features into numerical vectors. These intermediaries enable the chatbot to handle non-numerical inputs without requiring complete architectural redesign.
Solution Approach 2:
The system segments the complex task of mathematical reasoning with non-numerical inputs into distinct modular components: input processing module, feature extraction module, mathematical operation module, and output generation module. Each module handles a specific aspect of the processing pipeline, allowing the system to manage complexity through functional decomposition.
2Reliability
If chatbots implement sophisticated natural language processing and mathematical reasoning capabilities, then they can provide logical results for non-numerical inputs, but they increase computational complexity and processing time
Solution Approach 1:
The system performs preliminary actions by pre-building a mathematical knowledge graph that stores relationships between mathematical concepts, operations, and their properties. This pre-computed knowledge structure allows the chatbot to quickly retrieve and apply mathematical rules during inference without performing complex real-time calculations, thereby improving reliability while managing computational complexity.
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching systems with sophisticated but optimized computational processes including neural network-based feature extraction, vector space modeling, and knowledge graph reasoning. These substitutions enable accurate mathematical reasoning while leveraging efficient algorithms to manage computational complexity.
3Adaptability or versatility
If chatbots convert non-numerical inputs into vectors and perform mathematical equations, then they can provide logical mathematical results, but they require complex feature extraction and vector encoding processes
Solution Approach 1:
The patent implements universal feature extraction mechanisms that can handle multiple types of non-numerical inputs (images, text, diagrams) through a unified processing pipeline. The feature extractor and vectorizer are designed to process diverse input formats and convert them into a common numerical representation, enabling the system to perform mathematical operations on various input types without requiring separate specialized processing paths for each input modality.
Data Source
AI summary
Systems and methods for automated mathematical chatting. The systems and methods convert any identified non-numerical inputs into vectors and then perform the mathematical equation utilizing the vectors instead of the nonnumeric inputs along with any other identified numeric inputs to obtain a numerical vector result. The systems and methods decode the numerical vector result into a result feature and then search one or more databases for output based on the result feature. The systems and methods provide the selected output from the one or more databases in response to the mathematical query.