Natural Language Processing for KPI-Based Defect Detection
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Solution Overview
Problem
Existing computing systems face challenges in accurately translating high-level performance objectives into specific quantitative metrics, leading to inefficient selection and measurement of Key Performance Indicators (KPIs, which can result in unrecognized performance degradation due to unreliable data and incorrect KPI measurements.
Innovation Solution
Utilizing natural language processing (NLP) to analyze user inputs and identify relevant KPIs, determining appropriate metrics, and evaluating performance data through machine learning, enabling precise and efficient measurement of computing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to translate high-level performance objectives into specific quantitative metrics, then the process requires significant human effort and expertise, but the translation accuracy and speed are limited and error-prone
Solution Approach 1:
The patent replaces manual mechanical translation processes with an automated NLP-based system that uses machine learning models to translate high-level performance objectives into specific quantitative KPIs. The system automatically parses natural language inputs, identifies relevant metrics, and maps them to measurable data sources, eliminating human effort while improving accuracy and speed.
Solution Approach 2:
The patent introduces an NLP-based intermediary system that acts as a mediator between high-level performance objectives and specific quantitative metrics. This intermediary automatically translates vague performance goals into precise, measurable KPIs by analyzing semantic meaning and mapping to appropriate metrics, thereby improving both accuracy and efficiency of the translation process.
2Reliability
If comprehensive data collection is performed to ensure accurate KPI measurement, then measurement reliability improves, but system complexity and data processing overhead increase
Solution Approach 1:
The patent segments the data collection and processing system into modular components that are automatically selected and configured based on the specific KPIs being measured. Instead of implementing a monolithic comprehensive data collection system, the NLP model identifies only the relevant data sources and metrics needed for each specific performance objective, reducing system complexity while maintaining reliability.
Solution Approach 2:
The patent applies partial action by collecting and processing only the specific data portions that are relevant to the identified KPIs, rather than implementing comprehensive data collection across all possible data sources. The NLP system determines the minimum necessary data set required for accurate KPI measurement, reducing processing overhead while maintaining measurement reliability.
3Adaptability or versatility
If multiple potential KPIs are considered to cover all performance aspects, then performance monitoring completeness improves, but difficulty in selecting and measuring the appropriate KPIs increases
Solution Approach 1:
The patent implements a dynamic KPI selection process where the NLP model adapts its identification and selection of KPIs based on the specific performance objectives provided in natural language inputs. The system dynamically determines which KPIs are relevant to each specific performance goal, providing versatile coverage across different performance aspects while simplifying the selection process through automated semantic analysis and context-aware metric mapping.
Data Source
AI summary
An example implementation may involve: receiving a textual input indicating a performance objective, wherein the performance objective is associated with a computing platform; obtaining a semantic value associated with the performance objective; mapping the semantic value to a first performance metric, wherein the first performance metric characterizes the computing platform; obtaining performance data based on the first performance metric; and assessing the performance data to determine an evaluation of the performance objective.