Machine Learning Insight Summaries With Metric Validation
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Solution Overview
Problem
The manual creation of metrics by data analysts is time-consuming and inefficient, leading to duplicate efforts and a lack of automated generation of metrics with additional business context, while existing large language models can introduce inaccuracies in data summaries.
Innovation Solution
A system using a machine learning model, such as a generative pre-trained transformer, automatically generates metric definitions and summaries by leveraging metadata and domain-specific knowledge to suggest and validate metrics, providing natural language insights and summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual creation of metrics is used, then business users can obtain customized metrics, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service metric creation by allowing business users to automatically generate metrics through natural language queries. The natural language processing model interprets user intent and automatically creates appropriate metrics without requiring manual configuration or data analyst intervention, thus resolving the contradiction between ease of operation and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual process of metric creation with an automated natural language processing system. Instead of manually selecting data sources, defining calculations, and configuring parameters, users simply type natural language queries that the system automatically translates into executable metrics, eliminating the time-consuming manual steps while maintaining ease of use.
2Productivity
If manual creation of metrics is used, then metrics can be customized, but duplicate efforts occur and efficiency is reduced
Solution Approach 1:
The system automatically copies and reuses existing metric definitions and patterns when generating new metrics. When a user requests a metric, the system searches for similar existing metrics and automatically adapts their definitions, data sources, and calculations, preventing duplicate work while maintaining customization. This copying mechanism significantly improves productivity by eliminating redundant metric creation efforts.
3Loss of information
If summaries of multiple insights are generated using concatenation, then comprehensive coverage is achieved, but the output is repetitive, lengthy, and less legible
Solution Approach 1:
The patent replaces the mechanical concatenation process with an intelligent natural language generation system. Instead of simply joining individual insight strings together, the system uses a language model to understand the semantic relationships between insights, synthesize them into a coherent narrative, and produce a condensed summary that maintains completeness while improving legibility and eliminating repetition.
4Ease of operation
If large language models are used to summarize insights, then legibility is improved, but inaccuracies and hallucinations are introduced
Solution Approach 1:
The system implements a feedback loop where the generated summary is validated against the original data and insights. The verification process checks whether the summary accurately reflects the source material, and any hallucinated or inaccurate information is detected and corrected. This feedback mechanism maintains high legibility while ensuring reliability by preventing the propagation of errors.
Solution Approach 2:
The patent introduces an intermediary verification layer between the language model and the final output. This intermediary component acts as a mediator that checks the accuracy of the language model's generation against the original insights and data, correcting any hallucinations before presenting the final summary to the user, thus maintaining both legibility and accuracy.
Data Source
AI summary
A system obtains a bundle of insights generated based on insight templates and provides the bundle of insights as input to a machine learning model. The system then generates a summary of the bundle of insights using the machine learning model.