Automated Name Identification via Attribute Clustering and ML Evaluation
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Solution Overview
Problem
Automated generation of user-intelligible labels in computerized processes is inefficient and prone to semantic collisions due to overly formulaic approaches.
Innovation Solution
A system and method using a processor and memory to identify an attribute cluster from entity data, determine a component word set, and then use machine learning models to identify a name and create a visual element data structure, incorporating intelligibility and appeal ratings for candidate names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated label generation uses formulaic approaches for ease of use, then ease of operation is improved, but manufacturing precision deteriorates due to semantic collisions
Solution Approach 1:
The patent segments the label generation process into distinct stages: entity data analysis, attribute clustering, component word generation, candidate name formation, and evaluation. This segmentation allows each stage to be optimized independently, improving overall label precision while maintaining automated operation.
Solution Approach 2:
The system implements feedback mechanisms through evaluation metrics (intelligibility rating, appeal rating) that assess candidate names and guide the selection process. This feedback loop ensures high-quality labels are generated automatically without semantic collisions.
2Productivity
If automated label generation is simplified for efficiency, then productivity is improved, but reliability deteriorates due to semantic collisions
Solution Approach 1:
The patent performs preliminary actions by pre-processing entity data to identify attributes and cluster them before generating candidate names. This preliminary structuring enables efficient automated generation while ensuring reliability through systematic evaluation of candidates against established criteria.
Solution Approach 2:
The system uses self-service mechanisms where the automated process evaluates its own candidate names using intelligibility and appeal ratings, selecting the best candidates without human intervention. This maintains both high productivity and reliability through consistent automated quality assessment.
3Manufacturing precision
If complex machine learning models are used to generate intelligible names, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex naming task into separate machine learning models: one for generating component words from attribute clusters, another for forming candidate names, and evaluation models for intelligibility and appeal. This segmentation manages system complexity while achieving high naming precision.
Solution Approach 2:
The system employs universal machine learning models that perform multiple functions: attribute clustering, component word generation, candidate name formation, and evaluation. These multi-functional models reduce overall system complexity while maintaining high precision in name generation.
Data Source
AI summary
Systems and methods for identifying a name are disclosed herein. In some embodiments, an apparatus may determine an attribute and/or attribute cluster. In some embodiments, an apparatus may determine a component word set as a function of an attribute and/or attribute cluster. In some embodiments, an apparatus may determine a candidate name by combining component words. In some embodiments, an apparatus may determine an intelligibility rating and/or an appeal rating for a candidate name.