17960618. PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17960618 titled 'PRIVACY-PRESERVING RULES-BASED TARGETING USING MACHINE LEARNING
Simplified Explanation
The patent application describes techniques for privacy-preserving rules-based targeting using machine learning. Entities are ranked using a machine learning model, and their targetable features are ordered and sorted into bins based on their values. A targeting rule is established based on the selected bin for each feature.
- Entities are ranked using a machine learning model.
- Targetable features are ordered and sorted into bins based on their values.
- A targeting rule is established based on the selected bin for each feature.
Potential Applications
This technology could be applied in targeted advertising, personalized recommendations, and content filtering.
Problems Solved
This technology addresses the need for privacy-preserving targeting while still providing relevant content to users.
Benefits
The benefits of this technology include improved user privacy, more accurate targeting, and enhanced user experience.
Potential Commercial Applications
Potential commercial applications of this technology include digital marketing, e-commerce platforms, and online content providers.
Possible Prior Art
Prior art in this field may include techniques for targeted advertising, machine learning algorithms for ranking entities, and privacy-preserving data processing methods.
Unanswered Questions
How does this technology handle dynamic changes in user preferences over time?
The patent application does not provide information on how the system adapts to changes in user behavior and preferences.
What measures are in place to prevent bias in the machine learning model?
The patent application does not detail any specific methods to mitigate bias in the machine learning model used for ranking entities.
Original Abstract Submitted
Techniques are described herein that are capable of providing privacy-preserving rules-based targeting using machine learning. Ranks are assigned to entities using a machine learning model. Values of each targetable feature associated with the respective entities are ordered. For each targetable feature, the entities are sorted among bins based on the values of the feature associated with the respective entities. For each targetable feature, a bin is selected from the bins that are associated with the feature based on the selected bin including more entities having respective ranks that are within a designated range than each of the other bins that are associated with the feature. A targeting rule is established, indicating a prerequisite for targeting an entity. The prerequisite indicating that the value of each targetable feature associated with the entity is included in a respective interval associated with the selected bin for the feature.
