18395311. METHODS AND APPARATUS FOR RECOMMENDATION SYSTEMS WITH ANONYMIZED DATASETS simplified abstract (Intel Corporation)

METHODS AND APPARATUS FOR RECOMMENDATION SYSTEMS WITH ANONYMIZED DATASETS

Organization Name

Intel Corporation

Inventor(s)

Chendi Xue of Austin TX (US)

Jian Zhang of Shanghai (CN)

Poovaiah Manavattira Palangappa of San Jose CA (US)

Rita Brugarolas Brufau of Hillsboro OR (US)

Ke Ding of Saratoga CA (US)

Ravi H. Motwani of Fremont CA (US)

Xinyao Wang of Shanghai (CN)

Yu Zhou of Shanghai (CN)

Aasavari Dhananjay Kakne of Santa Clara CA (US)

METHODS AND APPARATUS FOR RECOMMENDATION SYSTEMS WITH ANONYMIZED DATASETS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18395311 titled 'METHODS AND APPARATUS FOR RECOMMENDATION SYSTEMS WITH ANONYMIZED DATASETS

Simplified Explanation

The patent application describes a system and method for preserving privacy in a user dataset by selecting a subset of user data features for a feature selection training model.

• Interface circuitry, machine readable instructions, and programmable circuitry are used to determine data usage types for user data features.
• The data usage types are classified into feature categories and feature engineering mechanisms are applied to these categories.
• A subset of user data features is selected based on the application of feature engineering for a feature selection training model.
• The output is a second dataset with fewer user data features than the original dataset.

Potential Applications

This technology could be applied in various industries such as healthcare, finance, and marketing where privacy of user data is crucial.

Problems Solved

This technology addresses the issue of preserving privacy in user datasets while still allowing for effective data analysis and modeling.

Benefits

The system allows for the selection of relevant user data features while minimizing the risk of privacy breaches, leading to more accurate and efficient data analysis.

Potential Commercial Applications

Potential commercial applications include data analytics companies, healthcare organizations, financial institutions, and marketing firms looking to enhance their data analysis capabilities while maintaining user privacy.

Possible Prior Art

One possible prior art could be the use of feature selection techniques in data analysis to improve model performance and reduce overfitting.

How does this technology compare to existing methods of feature selection in data analysis?

This article does not provide a direct comparison to existing methods of feature selection in data analysis.

What are the potential limitations or challenges of implementing this system in real-world applications?

The article does not address the potential limitations or challenges of implementing this system in real-world applications.

Original Abstract Submitted

Systems, apparatus, articles of manufacture, and methods are disclosed to preserve privacy in a user dataset including interface circuitry, machine readable instructions, and programmable circuitry to determine a data usage type for each one of a plurality of user data features in a first dataset, classify the data usage type associated with each user data feature of the plurality of user data feature into a feature category, apply at least one feature engineering mechanism to feature categories of the data usage types of the plurality of user data features, select, based on application of feature engineering, a subset of the plurality of user data features for a feature selection training model, and output a second dataset based on the subset of the plurality of user data for the feature selection training model, the second dataset to include fewer user data features than the first dataset.