Microsoft technology licensing, llc (20240135015). BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA simplified abstract
Contents
- 1 BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA - 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 Original Abstract Submitted
BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA
Organization Name
microsoft technology licensing, llc
Inventor(s)
David Benjamin Levitan of Bothell WA (US)
Robert Alexander Sim of Bellevue WA (US)
Julia S. Mcanallen of Seattle WA (US)
Huseyin Atahan Inan of Redmond WA (US)
Girish Kumar of Redmond WA (US)
BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135015 titled 'BUILDING ANNOTATED MODELS BASED ON EYES-OFF DATA
Simplified Explanation
The patent application describes a method for building annotated models based on eyes-off data, where a synthetic data generation model is trained in an eyes-off environment using an anonymity technique on confidential data. The synthetic data generated is then used to train a target model in an eyes-on environment, which is later deployed back in the eyes-off environment to classify the confidential data.
- Synthetic data generation model trained in an eyes-off environment using an anonymity technique on confidential data.
- Synthetic data created closely representing confidential data without specific details that can be linked back to the confidential data.
- Annotated synthetic data used to train a target model in an eyes-on environment.
- Target model deployed back in the eyes-off environment to classify the confidential data.
Potential Applications
The technology can be applied in various fields such as healthcare, finance, and cybersecurity for building models based on confidential data without compromising privacy.
Problems Solved
This technology addresses the challenge of training models on sensitive data while maintaining data privacy and confidentiality.
Benefits
The benefits of this technology include improved model accuracy, enhanced data privacy, and the ability to leverage confidential data for training without exposing sensitive information.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of secure data analytics platforms for industries dealing with sensitive information.
Possible Prior Art
One possible prior art could be the use of differential privacy techniques in machine learning to protect sensitive data during model training.
What are the potential limitations of this technology in real-world applications?
The potential limitations of this technology in real-world applications could include the scalability of the synthetic data generation model, the accuracy of the target model trained on synthetic data, and the computational resources required for deploying the target model back in the eyes-off environment.
How does this technology compare to existing methods for training models on confidential data?
This technology differs from existing methods by utilizing synthetic data generation and annotation techniques to train models on confidential data in a secure and privacy-preserving manner. Existing methods may rely on data masking or encryption techniques, which may not provide the same level of privacy protection as the approach described in the patent application.
Original Abstract Submitted
systems and methods are directed to building annotated models based on eyes-off data. specifically, a synthetic data generation model is trained and used to further train a target model. the synthetic data generation model is trained within an eyes-off environment using an anonymity technique on confidential data. the synthetic data generation model is then used to create synthetic data that closely represents the confidential data but without any specific details that can be linked back to the confidential data. the synthetic data is then annotated and used to train the target model within an eyes-on environment. subsequently, the target model is deployed back within the eyes-off environment to classify the confidential data.
