18285613. MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM simplified abstract (GOOGLE LLC)
Contents
- 1 MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Questions about the Technology
- 1.10 Original Abstract Submitted
MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM
Organization Name
Inventor(s)
Bharath Pattabiraman of Santa Clara CA (US)
MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18285613 titled 'MACHINE LEARNING-BASED AUTOMATED TARGETING EXPANSION SYSTEM
Simplified Explanation
The patent application describes systems and methods for evaluating the relevance of search queries using machine learning techniques. The computing device determines if a search query is suitable for displaying content, assigns a category to the query, and identifies a relevant content item. It also analyzes additional queries with a relaxed threshold to assess relevance, captures user engagement data with the content item, and updates the machine learning model accordingly.
- Uses machine learning techniques to assess the relevance of search queries
- Determines if a search query is suitable for displaying content
- Assigns a category to the search query
- Identifies a relevant content item associated with the category
- Analyzes additional queries with a relaxed threshold to assess relevance
- Captures user engagement data with the content item
- Updates the machine learning model based on the engagement data
Potential Applications
This technology can be applied in various fields such as online advertising, e-commerce, content recommendation systems, and search engine optimization.
Problems Solved
This technology addresses the challenge of accurately determining the relevance of search queries and improving user engagement with displayed content.
Benefits
The benefits of this technology include more relevant search results, increased user engagement, improved content recommendation accuracy, and enhanced user experience.
Commercial Applications
- Online advertising platforms can use this technology to display more relevant ads to users.
- E-commerce websites can improve product recommendations based on search query relevancy.
- Content recommendation systems can enhance user engagement by suggesting more relevant content.
- Search engine optimization strategies can be optimized based on the analysis of search query relevancy.
Questions about the Technology
How does this technology improve user engagement with displayed content?
This technology uses machine learning techniques to analyze search queries and capture user engagement data, allowing for the display of more relevant content that users are more likely to engage with.
What are the potential applications of this technology beyond search engines?
This technology can be applied in various fields such as online advertising, e-commerce, content recommendation systems, and search engine optimization to enhance user experience and improve content relevancy.
Original Abstract Submitted
Systems and methods for assessing the relevancy of search queries are described. A computing device uses various machine learning techniques to determine whether a search query presents an opportunity to display content as well as determine a category for the search query and a content item associated with the category. The computing device additional analyzes an additional search query using a machine learning model with a relaxed threshold to assess relevancy. The computing device captures engagement data associated with a user engaging with the content item and update the machine learning model with the engagement data.
