Google llc (20240249301). Method for Automatically Identifying Change Contributors simplified abstract
Contents
Method for Automatically Identifying Change Contributors
Organization Name
Inventor(s)
Michael Yang Liu of Santa Clara CA (US)
Richard A. Maher of Newport Beach CA (US)
Edward Chou of Eastvale CA (US)
Srirama Koneru of Redwood City CA (US)
Batool Nadeem Husain of Bothell WA (US)
Cheolmin Kim of San Francisco CA (US)
Method for Automatically Identifying Change Contributors - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249301 titled 'Method for Automatically Identifying Change Contributors
Simplified Explanation: The disclosed technology presents a scalable method to identify and analyze drivers of change for composite metrics in a time series dataset.
- Automated mechanism for deriving drivers of change for composite metrics
- Enables identification of largest contributors to metric changes
- Scalable approach for analyzing multiple drivers in a dataset
Key Features and Innovation:
- Scalable method for deriving drivers of change for composite metrics
- Automated mechanism for identifying and deciphering drivers in a time series dataset
- Focus on largest contributors to metric changes for better analysis
Potential Applications: This technology can be applied in financial analysis, performance evaluation, trend forecasting, and risk management.
Problems Solved: The technology addresses the challenge of efficiently identifying and analyzing drivers of change in composite metrics in a time series dataset.
Benefits:
- Improved understanding of factors influencing metric changes
- Enhanced decision-making based on identified drivers
- Scalable approach for analyzing multiple drivers simultaneously
Commercial Applications: Potential commercial applications include financial institutions, data analytics companies, and businesses seeking to optimize performance based on metric analysis.
Prior Art: While no specific prior art is mentioned in the abstract, researchers can explore existing literature on time series analysis, composite metrics, and driver identification for related information.
Frequently Updated Research: Researchers in the fields of data analytics, financial analysis, and performance evaluation may have ongoing studies related to driver identification in time series datasets.
Questions about Scalable Method for Deriving Drivers of Change: 1. How does the automated mechanism in this technology enhance the analysis of composite metrics? 2. What are the potential implications of using this scalable method in financial analysis?
Original Abstract Submitted
an aspect of the disclosed technology is a scalable method to derive drivers of change for composite metrics (e.g., cost metrics and ratio metrics) in a time series data set. the disclosed technology comprises an automated mechanism that enables identification and deciphering of one or more drivers, e.g., the largest contributors, or a composite metric change in a scalable manner.
