Machine Learning String Generation via Random Walk Models
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Solution Overview
Problem
The process of generating unique strings is often time-consuming and resource-intensive, requiring users to specify a string and search for uniqueness, with repeated iterations if the string lacks sufficiency, leading to inefficiencies.
Innovation Solution
A system and method utilizing machine learning models to generate strings by training random walk models, where a seed string is input to derive next characters, and the system outputs a ranked order, with the ability to predict classes and optimize string uniqueness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually specify a string and search a data repository to compare with pre-existing strings, then uniqueness can be determined, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system pre-trains multiple machine learning models on different classification categories before actual string generation. This preliminary training enables the models to quickly generate unique strings without requiring real-time manual comparison with the entire data repository, thus reducing time while maintaining uniqueness determination accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of specifying strings and searching repositories with an automated machine learning-based system. The trained models automatically generate and evaluate string uniqueness by comparing against pre-existing data, eliminating manual intervention and significantly reducing time consumption
2Measurement precision
If users manually specify a string and search a data repository to compare with pre-existing strings, then uniqueness can be determined, but the process becomes resource-intensive
Solution Approach 1:
The system performs model training in advance during an offline phase, preparing multiple specialized machine learning models for different classification categories. This preliminary action shifts computational resource consumption from the online generation phase to the offline training phase, making the actual string generation process more efficient and less resource-intensive
Solution Approach 2:
The patent divides the data repository into multiple classification categories and trains separate machine learning models for each category. This segmentation allows the system to use only the relevant model for each string generation task, reducing overall computational resource consumption compared to using a single comprehensive model or manual full-repository searching
3Productivity
If multiple machine learning models are trained and random walk models are derived, then string generation efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of unique string generation into multiple specialized machine learning models, each trained on specific classification categories. This segmentation makes the overall system more manageable and allows for targeted model training, improving generation efficiency while making the complexity modular and organized rather than monolithic
Data Source
AI summary
Systems and methods for generating strings based on a seed string are disclosed. Machine learning models are trained using domain-specific training data. Random walk models are derived from the trained machine learning models. A seed string is input into each of the random walk models, and each of the random walk models iteratively generate one or more next characters for the seed string to generate at least one term from each of the random walk models. A predicted class for the at least one term generated by each of the random walk models can be determined, and a ranked order for the at least one term generated by each of the random walk models with the predicted classes can be output to a graphical user interface.