ML Tag Generation for Unstructured Genealogy Feedback
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Solution Overview
Problem
Identifying relatives in large-scale genealogy databases is computationally infeasible due to the sheer amount of unstructured data, and manually analyzing user sentiment from user feedback is inefficient for genealogy systems.
Innovation Solution
A system that uses machine learning models to generate embedding vectors from survey responses, cluster them, and extract key phrases, while performing sentiment analysis on user feedback to identify aspects requiring updates in genealogy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to extract user sentiment from feedback, then analysis accuracy can be maintained, but productivity is severely reduced due to the insurmountable volume of unstructured text data
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses natural language processing to extract sentiment from unstructured text. The system employs trained models to classify user feedback sentiment automatically, eliminating the need for human analysts to manually process each piece of feedback while maintaining analytical accuracy through sophisticated algorithmic approaches.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between raw unstructured user feedback and actionable sentiment insights. This intermediary layer processes the unstructured text through trained models, transforming it into structured sentiment data that can be easily analyzed and acted upon, thereby enabling high-volume processing without sacrificing analytical depth.
2Productivity
If machine learning models are deployed to process unstructured text at scale, then productivity is dramatically improved, but device complexity increases due to the need for sophisticated NLP infrastructure
Solution Approach 1:
The patent segments the complex NLP processing task into distinct modular components: text preprocessing modules, sentiment analysis models, and result aggregation systems. Each component handles a specific aspect of the processing pipeline, allowing for independent optimization, easier maintenance, and scalable deployment. This modular architecture reduces overall system complexity by breaking down the monolithic NLP system into manageable, specialized units.
3Measurement precision
If embedding vectors are generated and clustered to identify key phrases, then measurement precision of sentiment categorization is improved, but use of energy increases due to computational requirements of vector operations
Solution Approach 1:
The patent applies partial action by processing only the most relevant features and dimensions of the embedding vectors rather than performing exhaustive computations on all possible vector components. The system identifies and focuses computational resources on the most discriminative features for sentiment classification, achieving high accuracy while reducing overall energy consumption by avoiding unnecessary computations on less relevant data dimensions.
Data Source
AI summary
Disclosed herein relates to a method that analyzes the sentiment of user feedback for a genealogical system and identifies key phrases that may relate to novel themes in the user feedback. Sentiment analysis and novel theme prediction systems, methods, and computer-program products are described. Sentiment analysis of user feedback may include dividing user-generated unstructured text files into sections. The method classifies each section to an aspect of the genealogical system from a predetermined list of aspects monitored by the genealogical system. The method inputs the text belonging to the classified section to a supervised machine learning model and determines a sentiment associated with the classified section. In other embodiments, a method generates embedding vectors representing survey responses from users of a genealogical system. The method extracts a subset of survey responses having embedding vectors grouped into one cluster. The method extracts key phrases that may indicate a novel theme.