Dynamic Personal Data Ranking via Usage Signals
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Solution Overview
Problem
Current personal data management systems, such as address books and financial portfolios, require manual updates and maintenance, which is time-consuming and cumbersome, and lack efficient methods for automatically generating and ranking relevant data based on user needs.
Innovation Solution
A computer-implemented method and system that dynamically ranks personal data entries by assigning ranking signals based on metadata, usage, and context, allowing for automatic generation and maintenance of personal data books, such as address books, using structured search data and browsing activity, and storing these entries with associated signals for future updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual updates and maintenance are used for personal data, then data accuracy can be maintained, but user time and effort are significantly consumed
Solution Approach 1:
The system automatically generates and maintains personal data by monitoring user's browsing activity, email interactions, and search queries. The address book, stock portfolio, and other personal data are updated without user intervention, with the system self-managing data collection, validation, and storage operations.
Solution Approach 2:
The system continuously monitors user interactions with data (viewing, editing, searching) and uses this feedback to dynamically rank and prioritize data entries. This feedback loop ensures that frequently accessed or important data is maintained with higher accuracy while reducing manual maintenance burden.
2Reliability
If personal data is manually maintained, then data privacy and control can be ensured, but the process becomes cumbersome and reduces productivity
Solution Approach 1:
The system automatically collects data from public sources and user interactions without requiring manual input. Users simply benefit from the automated maintenance while retaining control through selective viewing and editing capabilities, thus improving productivity without sacrificing privacy.
Solution Approach 2:
The system acts as an intermediary between various data sources (browsers, emails, search engines) and the user's personal data storage. It automatically processes and validates data through this intermediary layer, ensuring privacy control while eliminating cumbersome manual maintenance processes.
3Device complexity
If personal data entries are stored in alphabetical order, then data organization is simple, but accessing specific data becomes time-consuming
Solution Approach 1:
The system dynamically reorganizes data entries based on multiple factors including frequency of access, recency of updates, user preferences, and contextual relevance. This dynamic ranking transforms static alphabetical ordering into an adaptive structure that automatically optimizes access time while maintaining organizational simplicity through automated management.
Solution Approach 2:
The system pre-ranks data entries based on historical usage patterns and metadata before user queries are submitted. By performing this ranking action in advance, the system prepares data in an optimal access order, significantly reducing retrieval time while keeping the organizational mechanism simple and automated.
4Adaptability or versatility
If comprehensive personal data is automatically collected, then data relevance and usefulness are improved, but privacy concerns and data security risks increase
Solution Approach 1:
The system collects data through intermediary processes that automatically filter and validate information from multiple sources before storing in personal data books. This intermediary layer ensures comprehensive data collection for relevance while implementing security checks and privacy protections, reducing security risks through automated validation and controlled data processing.
Data Source
AI summary
Techniques are disclosed for automatically generating and maintaining personal data, such as an address book, a financial portfolio, a discussion groups or blogs book, or other types of personal data stores, based on a person's structured search data and/or usage data (e.g., browsing) and/or other sources of personal data (e.g., emails the user receives). Related metadata can also be used in the generating and/or maintaining of the personal data. Dynamic personal data ranking and/or autocomplete functions are also provided, which can be used in conjunction with the automatic generation and maintenance of the user's personal data, to further ease the user's burden in managing and/or handling such data.