Permission-Based Physical Address Update System
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Solution Overview
Problem
Existing systems lack an efficient method for automatically updating physical addresses in databases, leading to potential delivery failures due to outdated or incorrect address information, and do not adequately manage user privacy in address sharing processes.
Innovation Solution
A permission-based automatic update system that uses machine learning to predict the most likely current physical address of a user by analyzing metadata from various data sources, while ensuring user consent through graphical user interfaces, and maintains address information in both actual and tokenized formats for secure sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic address update is implemented without permission requirements, then address accuracy and delivery reliability are improved, but user privacy and data security are compromised
Solution Approach 1:
The system performs preliminary actions by pre-establishing permission frameworks and consent management mechanisms before address updates occur. Users can pre-configure their privacy preferences and data sharing policies, which then automatically govern future address updates without requiring repeated manual intervention, thus maintaining both reliability and privacy protection
Solution Approach 2:
The system introduces permission-based access control and consent management as intermediary layers between the automatic address update mechanism and the final data usage. This intermediary layer mediates between the desire for automatic updates and user privacy concerns by filtering and authorizing access to address information based on user-defined permissions
2Object-affected harmful factors
If manual address verification is required for every update, then user privacy is protected, but system efficiency and update speed are reduced
Solution Approach 1:
The system enables self-service by allowing users to configure their own privacy preferences, data sharing policies, and permission levels. Once users set these parameters, the system automatically enforces them without requiring continuous manual verification for each address update, thus maintaining privacy protection while significantly improving update speed and efficiency
Solution Approach 2:
The system implements feedback mechanisms where users receive notifications about proposed address changes and can provide feedback or confirmation. This feedback loop maintains user control and privacy awareness while allowing automated processing to proceed at high speed, balancing privacy protection with update efficiency
3Measurement precision
If comprehensive data collection from multiple sources is performed, then address prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system applies segmentation by dividing the complex data collection and processing task into separate modular components: data collection modules for different sources, permission management modules, processing modules, and prediction modules. Each component handles specific aspects independently, reducing overall system complexity while maintaining comprehensive data collection for accurate predictions
Solution Approach 2:
The system implements partial action by selectively collecting and processing data from multiple sources based on user permissions and relevance thresholds. Rather than universally collecting all available data, the system processes only the necessary portion of data that has been authorized and is relevant to the prediction task, reducing processing requirements while maintaining adequate accuracy
Data Source
AI summary
A method utilizing a permission-based access functionality to obtain communication identifying metadata in electronic communications associated with a first and/or a second user. The communication identifying metadata is processed to generate first physical address metadata associated with the second user. Second physical addresses metadata associated with the second user is identified in data objects stored in electronic resources. The first and the second physical addresses metadata are inputted into a machine learning model which identifies related physical addresses, based on a change in a feature of the first and/or second physical addresses metadata and to predict a most likely current physical address of the second user. Upon receipt of the access permission to the current physical address of the second user, a database stored on a computing device of the first user is updated with the current physical address of the second user.