18295596. Adaptive Updating of Dynamically Changing Analytical Model simplified abstract (SAP SE)
Contents
Adaptive Updating of Dynamically Changing Analytical Model
Organization Name
Inventor(s)
Sai Sunanda D of Bangalore (IN)
Devicharan Vinnakota of Bangalore (IN)
Priyanka Kommanapalli of Bangalore (IN)
Adaptive Updating of Dynamically Changing Analytical Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 18295596 titled 'Adaptive Updating of Dynamically Changing Analytical Model
The patent application describes systems and methods for updating analytical models that are changing over time, particularly in response to evolving privacy regulations like GDPR.
- Synchronization is performed upon receiving a model creation request to update metadata reflecting the current state of the model.
- If there is a change in the status of the model metadata, the user is prompted with a sensitive metadata field and an empty context metadata field.
- The user must fill in the context metadata field (e.g., with an appropriate ID) to provide authorization to access the data of the model.
- Once the context metadata field is successfully inputted, a model view containing sensitive information can be passed to the user.
- If the context metadata field is not filled in correctly, error messages are returned to the user, and the model view will not be passed.
Potential Applications: - Data privacy compliance in analytical models - Efficient updating of models to meet evolving regulations
Problems Solved: - Ensuring compliance with changing privacy regulations - Providing secure access to sensitive information in models
Benefits: - Enhanced data privacy protection - Streamlined model updating process
Commercial Applications: Title: Data Privacy Compliance Solutions for Analytical Models This technology can be utilized by companies that handle sensitive data and need to ensure compliance with privacy regulations. It can be marketed as a tool to streamline the process of updating analytical models to meet evolving privacy standards, thus enhancing data security and regulatory compliance.
Questions about Data Privacy Compliance Solutions for Analytical Models: 1. How does this technology address the challenges of keeping analytical models compliant with changing privacy regulations? This technology addresses the challenges by providing a systematic approach to updating models based on evolving privacy regulations, ensuring ongoing compliance.
2. What are the key features that differentiate this solution from traditional methods of updating analytical models for privacy compliance? This solution stands out by prompting users to provide context metadata for authorization, thereby ensuring secure access to sensitive information within the model.
Original Abstract Submitted
Embodiments provide systems and methods that update analytical models which are changing over time. Upon receipt of a model creation request, a synchronization is performed to receive updated metadata reflecting a current state of a model. Where such updating indicates a change in status (e.g., privacy) of the model metadata, the user is prompted with a sensitive metadata field, and an empty context metadata field. The user must fill in the context metadata field (e.g., with an appropriate ID) thereby providing authorization to access data of the model. Then, a model view including sensitive information can be passed to the user. Absent successfully inputting the context metadata field, error message(s) are returned to the user, and the model view will not be passed. Embodiments may find particular use in efficiently updating analytical models to ensure their continuing compliance with evolving privacy regulation (e.g., ongoing revision and/or interpretation of GDPR language).
