17972408. SUPERPOSITION-BASED CONTENT SERVING simplified abstract (Microsoft Technology Licensing, LLC)

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 17972408 titled 'SUPERPOSITION-BASED CONTENT SERVING

Simplified Explanation

Embodiments of the disclosed technologies involve a process where a request with a user identifier and metadata for an available slot is received, the user identifier is removed to create anonymized request data, superposition data is obtained from a machine learning model based on the anonymized request data, the superposition data is sent to a real-time content-to-request matching process, an identifier for a content distribution is received from the matching process, and the selected content distribution is initiated to the slot in response to the request.

• Request processing:

   * Receive request with user identifier and slot metadata
   * Remove user identifier to create anonymized request data
   * Obtain superposition data from a machine learning model based on anonymized request data

• Content distribution:

   * Send superposition data to real-time content-to-request matching process
   * Receive identifier for content distribution selected based on superposition data
   * Initiate selected content distribution to the slot in response to the request

Potential Applications

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

Problems Solved

This technology helps in efficiently matching content to user requests, ensuring privacy by anonymizing user data, and improving the overall user experience by delivering relevant content.

Benefits

The benefits of this technology include improved content delivery accuracy, enhanced user privacy protection, and increased user engagement with tailored content.

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.

Possible Prior Art

One possible prior art could be the use of machine learning models for content recommendation systems in online platforms, where user data is anonymized for privacy protection.

How does this technology handle real-time content updates and changes in user preferences?

The article does not provide information on how the system adapts to real-time changes in content availability or user preferences.

What measures are in place to ensure the security and integrity of the anonymized user data during the processing and matching stages?

The article does not address the security protocols or encryption methods used to protect the anonymized user data during the various stages of processing and matching.

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.