18008604. DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES simplified abstract (Google LLC)
Contents
- 1 DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES
Organization Name
Inventor(s)
Arne Mauser of Los Altos Hills CA (US)
DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18008604 titled 'DISTRIBUTING DIGITAL COMPONENTS BASED ON PREDICTED ATTRIBUTES
Simplified Explanation
The patent application describes methods, systems, and apparatus for selecting and distributing digital components based on predicted user attributes of users.
- Obtaining data indicating content categories of content accessed by the user
- Making a determination for an aggregate measure of each content category
- Obtaining user attribute prediction data
- Predicting user attributes for the current visit of the user
- Selecting digital components for display based on predicted user attributes
Potential Applications
This technology could be applied in personalized advertising, content recommendation systems, and targeted marketing campaigns.
Problems Solved
This technology helps in improving user engagement, increasing click-through rates, and enhancing user experience by providing relevant content to users.
Benefits
The benefits of this technology include increased user engagement, higher conversion rates, improved user satisfaction, and better targeting of digital components to users.
Potential Commercial Applications
Potential commercial applications of this technology include online advertising platforms, e-commerce websites, content publishing platforms, and digital marketing agencies.
Possible Prior Art
One possible prior art for this technology could be personalized recommendation systems used in e-commerce websites and streaming platforms to suggest products or content to users based on their browsing history and preferences.
What are the potential limitations of this technology in accurately predicting user attributes?
One potential limitation of this technology could be the accuracy of the prediction algorithms used to determine user attributes. The system may not always accurately predict user preferences or behavior based on past data.
How can the effectiveness of this technology be measured in terms of user engagement and conversion rates?
The effectiveness of this technology can be measured by tracking metrics such as click-through rates, time spent on the website, conversion rates, and user feedback. A comparison of these metrics before and after implementing the technology can help evaluate its impact on user engagement and conversion rates.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components based on predicted user attributes of users are described. In one aspect, a method includes obtaining data indicating content categories of content of the content pages accessed by the user during the user visits. A determination is made for an aggregate measure of each content category based on a quantity of user visits to content pages of the electronic resource of the publisher that included content classified as belonging to the content category. User attribute prediction data indicating previously predicted user attributes of the user is obtained. User attributes are predicted for the current visit of the user to the electronic resource of the publisher that is further used to select digital components for display with the electronic resource on a client device during the current visit.
