Microsoft technology licensing, llc (20240137427). SUPERPOSITION-BASED CONTENT SERVING simplified abstract

SUPERPOSITION-BASED CONTENT SERVING

Organization Name

microsoft technology licensing, llc

Inventor(s)

Tao Cai of Sunnyvale CA (US)

Albert Hwang of McKinney TX (US)

Jianlong Zhang of Sunnyvale CA (US)

Muhammad Hassan Khan of Dublin CA (US)

SUPERPOSITION-BASED CONTENT SERVING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240137427 titled 'SUPERPOSITION-BASED CONTENT SERVING

Simplified Explanation

The disclosed technologies involve receiving a request with a user identifier and metadata for an available slot, removing the user identifier to create anonymized request data, using a machine learning model to provide superposition data related to the anonymized request data, sending the superposition data to a real-time content-to-request matching process, receiving an identifier for a content distribution based on the superposition data, and initiating the selected content distribution to the slot in response to the request.

• Explanation of the patent:

 * Receive request with user identifier and slot metadata
 * Anonymize the request data by removing the user identifier
 * Use machine learning model to generate superposition data
 * Send superposition data to real-time content-to-request matching process
 * Receive identifier for content distribution based on superposition data
 * Initiate selected content distribution to the slot

1. 1. 1. Potential Applications

This technology could be applied in personalized content delivery systems, targeted advertising, and recommendation engines.

1. 1. 1. Problems Solved

This technology helps in matching content to user requests without compromising user privacy by anonymizing the data.

1. 1. 1. Benefits

The benefits of this technology include improved user experience, increased efficiency in content delivery, and enhanced privacy protection for users.

1. 1. 1. Potential Commercial Applications

A potential commercial application of this technology could be in online advertising platforms for delivering targeted ads to users based on their preferences and behavior.

1. 1. 1. Possible Prior Art

One possible prior art could be the use of machine learning models for content recommendation systems in online platforms.

1. 1. 1. 1. Unanswered Questions

1. 1. 1. 1. How does this technology handle real-time content updates and changes?

This article does not address how the system adapts to real-time changes in content availability or user preferences.

1. 1. 1. 1. What measures are in place to ensure the accuracy and relevance of the content distribution selected?

The article does not specify the quality control mechanisms or algorithms used to ensure the selected content distribution is accurate and relevant to the user's request.

Original Abstract Submitted

embodiments of the disclosed technologies receive a request including a user identifier and metadata associated with a slot available at a user system, remove the user identifier from the request to produce anonymized request data, receive, from a machine learning model, superposition data that correlates with the anonymized request data, send the superposition data for the anonymized request data to a real-time content-to-request matching process, receive, from the real-time content-to-request matching process, an identifier that identifies a content distribution selected based on the superposition data, and initiate the selected content distribution through the network to the slot in response to the request.