18008607. PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION simplified abstract (Google LLC)
Contents
- 1 PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION - 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
- 1.9.1 Unanswered Questions
- 1.9.2 How does the method determine the scores for each user attribute based on the quantity of user groups identified in the list of group identifiers?
- 1.9.3 What types of digital components are included in the set of digital components selected based on the data of the digital component request?
- 1.10 Original Abstract Submitted
PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION
Organization Name
Inventor(s)
Michael William Daub of Woodinville WA (US)
Robert F. Day of Bellevue WA (US)
Arthur Asuncion of Bothell WA (US)
PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18008607 titled 'PRIVACY PRESERVING GROUP-BASED CONTENT DISTRIBUTION
Simplified Explanation
The patent application describes a method for displaying digital components on client devices based on predicted user attributes of users.
- The method involves updating a list of user group identifiers for a user to include a particular user group identifier that identifies a specific user group.
- Scores are determined for each user attribute based on the quantity of user groups identified in the list of group identifiers for the user that include the user attribute.
- A digital component request is sent to a content distribution system based on the data representing the list of user-group identifiers, and digital component data identifying a set of digital components selected based on the request data is received.
Potential Applications
This technology could be applied in personalized advertising, content recommendation systems, and user experience customization.
Problems Solved
This technology solves the problem of delivering relevant digital components to users based on their predicted attributes, improving user engagement and satisfaction.
Benefits
The benefits of this technology include increased user engagement, personalized user experiences, and more effective content delivery.
Potential Commercial Applications
Potential commercial applications of this technology include targeted advertising platforms, personalized content delivery services, and user behavior analysis tools.
Possible Prior Art
One possible prior art for this technology could be personalized recommendation systems used in e-commerce platforms or streaming services.
Unanswered Questions
How does the method determine the scores for each user attribute based on the quantity of user groups identified in the list of group identifiers?
The method calculates scores for user attributes based on the number of user groups that include the attribute, but the specific algorithm or formula used for this calculation is not detailed in the abstract.
What types of digital components are included in the set of digital components selected based on the data of the digital component request?
The abstract mentions that a set of digital components is selected based on the request data, but it does not specify the criteria or factors used to determine which components are included in this set.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for displaying digital components on client devices based on predicted user attributes of users are described. In one aspect, a method includes updating, at a client device of a user, a list of user group identifiers for the user to include a particular user group identifier that identifies a particular user group. A determination is made, for each user attribute of multiple user attributes, a score based on a quantity of user groups identified in the list of group identifiers for the user that include, as a membership attribute, the user attribute. A digital component request including data representing the list of user-group identifiers is sent to a content distribution system. Digital component data identifying a set of digital components selected based on the data of the digital component request is received.
