18008562. CONTENT DISTRIBUTION simplified abstract (Google LLC)
Contents
- 1 CONTENT DISTRIBUTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTENT DISTRIBUTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
CONTENT DISTRIBUTION
Organization Name
Inventor(s)
Timothy Chun-Wai Au of Milpitas CA (US)
CONTENT DISTRIBUTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18008562 titled 'CONTENT DISTRIBUTION
Simplified Explanation
The patent application describes methods, systems, and apparatus for adjusting distribution criteria of a digital component in one or more geographical regions using machine learning models.
- Data is obtained to quantify digital component distribution in different regions during specific time periods.
- A machine learning model is generated to predict outcomes based on correlations between digital component distribution in different regions.
- Predicted outcomes are compared with actual distribution data to adjust distribution criteria in the regions.
Key Features and Innovation
- Use of machine learning models to predict digital component distribution outcomes.
- Adjustment of distribution criteria based on predicted outcomes.
- Quantification of digital component distribution in different geographical regions.
Potential Applications
The technology can be applied in various industries such as e-commerce, digital marketing, and supply chain management to optimize distribution strategies.
Problems Solved
- Inefficient distribution of digital components in different regions.
- Lack of predictive tools to adjust distribution criteria effectively.
Benefits
- Improved accuracy in predicting digital component distribution.
- Enhanced efficiency in adjusting distribution criteria based on predicted outcomes.
Commercial Applications
Optimizing distribution strategies in e-commerce platforms, improving targeted marketing campaigns, and streamlining supply chain operations.
Prior Art
Readers can explore prior research on machine learning models for predictive analytics in distribution management.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms for predictive modeling in distribution optimization.
Questions about the Technology
How does the machine learning model predict digital component distribution outcomes?
The machine learning model uses correlations between digital component distribution in different regions to generate predictive outcomes.
What are the potential commercial applications of this technology?
This technology can be applied in e-commerce, digital marketing, and supply chain management for optimizing distribution strategies.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting distribution criteria of digital component in one or more geographical regions. Methods include obtaining data quantifying digital component distribution in a first and a second region during a first predetermined period of time. A machine learning model is generated to predict a first outcome quantifying digital component distribution in the first region based on a correlation between digital component distribution in a first and a second region. Data is obtained that quantifies digital component distribution in the first region during a second predetermined period of time and a predicted second outcome is generated that quantifies digital component distribution during the second predetermined period of time. The predicted second outcome is compared with the digital component distribution in the first region and distribution criteria is adjusted for the first region based on the comparison.
