18156764. Method for Automatically Identifying Change Contributors simplified abstract (GOOGLE LLC)
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 18156764 titled 'Method for Automatically Identifying Change Contributors
The disclosed technology presents a scalable method for deriving drivers of change for composite metrics in a time series dataset.
- Automated mechanism for identifying and deciphering drivers of change.
- Focus on composite metrics such as cost metrics and ratio metrics.
- Enables identification of the largest contributors to a composite metric change.
- Scalable approach to analyzing and understanding changes in metrics over time.
Potential Applications: - Financial analysis and forecasting. - Performance evaluation in various industries. - Risk management and mitigation strategies.
Problems Solved: - Simplifies the process of identifying key drivers of change in complex datasets. - Provides a scalable solution for analyzing composite metrics over time.
Benefits: - Enhanced decision-making based on a deeper understanding of metric changes. - Improved efficiency in identifying and addressing key factors influencing metrics. - Scalable approach allows for analysis of large datasets with ease.
Commercial Applications: Title: Scalable Metric Analysis Technology for Enhanced Decision-Making This technology can be utilized in financial institutions, healthcare organizations, and manufacturing companies to improve performance evaluation and forecasting accuracy.
Questions about Scalable Metric Analysis Technology:
1. How does this technology differ from traditional methods of analyzing metric changes?
- The technology automates the process of identifying key drivers of change in composite metrics, making it more efficient and scalable compared to manual analysis.
2. What industries can benefit the most from this scalable metric analysis technology?
- Industries such as finance, healthcare, and manufacturing can benefit from this technology by improving their decision-making processes and forecasting accuracy.
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.
