18009487. Scalable Mixed-Effect Modeling and Control simplified abstract (Google LLC)
Contents
Scalable Mixed-Effect Modeling and Control
Organization Name
Inventor(s)
Ali Nasiri Amini of Redwood City CA (US)
Zheng Zhao of San Jose CA (US)
Di-Fa Chang of Cupertino CA (US)
Scalable Mixed-Effect Modeling and Control - A simplified explanation of the abstract
This abstract first appeared for US patent application 18009487 titled 'Scalable Mixed-Effect Modeling and Control
The present disclosure describes a method for analyzing session data in a networked environment using a mixed effects model.
- Obtaining session data descriptive of user sessions in the networked environment
- Initializing a mixed effects model to describe first and second effects on the distribution of the session data
- Optimizing a weighted objective over subsets of the session data to adjust the contribution of the second effect with respect to the first effect
- Updating the mixed effects model based on the optimized weighted objective
Potential Applications: - Data analysis in networked environments - User behavior modeling - Predictive analytics
Problems Solved: - Efficiently analyzing session data - Understanding the impact of different effects on user behavior
Benefits: - Improved insights into user behavior - Enhanced decision-making based on data analysis
Commercial Applications: - Marketing analytics - E-commerce platforms - Social media platforms
Questions about the Technology: 1. How does the mixed effects model improve the analysis of session data? 2. What are the key advantages of optimizing the weighted objective in this method?
Frequently Updated Research: - Stay updated on advancements in mixed effects modeling for data analysis in networked environments.
Original Abstract Submitted
In an example aspect, the present disclosure provides for an example method including obtaining session data descriptive of one or more user sessions in the networked environment; initializing a mixed effects model configured to describe a first effect and a second effect on a distribution of the session data; optimizing a weighted objective over a plurality of subsets of the session data, the weighted objective comprising a weighting parameter configured to adjust, respectively for the plurality of subsets of the session data, a contribution of the second effect with respect to the first effect; and updating the mixed effects model based on the optimized weighted objective.
