Microsoft technology licensing, llc (20240135240). ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING simplified abstract

ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING

Organization Name

microsoft technology licensing, llc

Inventor(s)

Yafei Wei of Sunnyvale CA (US)

Andrew O. Hatch of Berkeley CA (US)

Keqing Liang of Cupertino CA (US)

Liyan Fang of Sunnyvale CA (US)

Ankan Saha of San Francisco CA (US)

ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135240 titled 'ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING

Simplified Explanation

The abstract of the patent application describes a technology that involves generating a reward score for an entity, determining a rate distribution using the reward score, generating a sampled rate value, generating a probability score for a pair of the entity and a user using the sampled rate value, determining a probability distribution using the probability score, generating a sampled probability value, and training a machine learning model using the sampled probability value.

• Generating reward score for an entity
• Determining rate distribution based on the reward score
• Generating sampled rate value by sampling the rate distribution
• Generating probability score for a pair of entity and user using the sampled rate value
• Determining probability distribution using the probability score
• Generating sampled probability value by sampling the probability distribution
• Training a machine learning model using the sampled probability value

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1. 1. 1. Potential Applications of this Technology

- Personalized recommendations - Targeted advertising - Fraud detection systems

1. 1. 1. Problems Solved by this Technology

- Improving user experience - Enhancing security measures - Increasing efficiency in decision-making processes

1. 1. 1. Benefits of this Technology

- Customized user interactions - Enhanced data analysis capabilities - Improved accuracy in predictions

1. 1. 1. Potential Commercial Applications of this Technology
         1. Optimizing User Engagement Strategies

- Using machine learning to tailor content based on user preferences

1. 1. 1. Possible Prior Art

There may be prior art related to machine learning models used for generating probability values based on rate distributions and reward scores.

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1. 1. 1. 1. Unanswered Questions

1. 1. 1. How does this technology handle privacy concerns related to user data?

This article does not delve into the specifics of data privacy measures implemented in the technology.

1. 1. 1. What are the potential limitations of using machine learning models in this context?

The article does not address any potential drawbacks or limitations of utilizing machine learning models for generating probability values.

Original Abstract Submitted

embodiments of the disclosed technologies include generating a reward score for an entity. a rate distribution is determined using the reward score. a sampled rate value is generated by sampling the rate distribution. a probability score is generated for a pair of the entity and a user using the sampled rate value. a probability distribution is determined using the probability score. a sampled probability value is generated by sampling the probability distribution. a machine learning model is trained using the sampled probability value.