Keyword Data Analysis Optimization via Sales Context
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Solution Overview
Problem
Conventional systems for analyzing keyword data in quality and claim management often result in false alerts due to the inability to distinguish between increases in keyword frequency related to product sales and actual issues, lacking a consistent and accurate method for optimizing keyword data analysis.
Innovation Solution
The proposed solution involves incorporating supplemental fixed form time series data, such as sales and failure rates, into keyword time series analysis to validate the significance of keyword data, using correction parameters to calibrate and combine these datasets, and calculating a similarity score to select optimized keyword data for quality management purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If keyword frequency analysis is used to detect quality issues, then managers can identify potential problems, but false alerts occur when keyword increases are due to sales growth rather than actual issues
Solution Approach 1:
The patent introduces sales data and product lifecycle data as intermediary variables that mediate between keyword frequency and quality alert generation. These intermediaries provide context to interpret keyword increases, distinguishing between sales-driven increases and issue-driven increases, thereby reducing false alerts while maintaining reliable detection of actual quality problems
Solution Approach 2:
The patent changes the parameters used for analysis by incorporating multiple data dimensions (sales volume, product lifecycle stage, keyword frequency) rather than relying solely on keyword frequency. By adjusting these parameters and their relationships through mathematical models, the system accurately distinguishes between benign keyword increases and genuine quality issues
2Reliability
If manual analysis is used to correct keyword data, then false alerts can be prevented, but the process lacks consistency and accuracy
Solution Approach 1:
The patent implements a self-service system where the automated model performs data correction and validation without requiring manual intervention. The system automatically compares keyword data against sales data and product lifecycle information, self-correcting false alerts through algorithmic analysis rather than human judgment, thereby ensuring both accuracy and consistency
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from the relationship between keyword data, sales data, and actual quality outcomes. This feedback loop refines the mathematical models over time, improving the consistency and accuracy of keyword data correction while eliminating the variability inherent in manual analysis
3Device complexity
If only keyword frequency data is analyzed, then the system remains simple, but the system cannot distinguish between sales-related increases and issue-related increases
Solution Approach 1:
The patent merges keyword frequency data with sales data and product lifecycle data into a unified analysis framework. By combining these previously separate data sources through mathematical relationships, the system gains the precision needed to distinguish between sales-related and issue-related keyword increases while maintaining operational simplicity through automated integration
Data Source
AI summary
Techniques for analyzing keyword data for quality management purposes are provided. One or more keywords are selected. Each of the one or more keywords represent a category of quality management. A keyword time series is prepared for each of the one or more selected keywords. A set of fixed form time series is prepared for each of the one or more selected keywords. The set of fixed form time series comprises one or more fixed form time series representing statistical data related to the one or more selected keywords. One or more correction sets comprising one or more correction parameters are obtained. Each of the one or more correction parameters correspond to one of the one or more fixed form time series within each set of fixed form time series. A set of corrected time series is generated for each of the one or more correction sets. The set of corrected time series comprises a combination of the keyword time series and the set of fixed form time series for each of the one or more selected keywords, the combination being in accordance with the one or more correction sets. A similarity score is calculated for each set of corrected time series. The set of corrected time series with the highest similarity score is selected. The selected set of corrected time series comprises optimized keyword data for quality management purposes.