Data Management System Using Life Event Association for Context Identification
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Solution Overview
Problem
Existing data management systems struggle to efficiently identify and manage relevant data for various purposes, as they lack effective mechanisms to associate data with life events and provide context for data usage.
Innovation Solution
A data management system that utilizes a timeline to associate data with past life events, allowing for the identification and retrieval of relevant data based on life events, thereby enhancing data discrimination and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored without association with life events, then storage capacity is maximized, but data relevance and context identification accuracy deteriorate
Solution Approach 1:
The system performs preliminary classification of data into life event categories during data ingestion, creating associations between data and life events before retrieval is needed. This preliminary action enables accurate context identification without adding complexity to the retrieval process, as the associations are already established in the database structure.
Solution Approach 2:
The patent introduces life events as an intermediary layer between raw data and user queries. Instead of directly searching through all stored data, the system uses life events as mediators to filter and retrieve relevant data, improving context identification accuracy while maintaining manageable system complexity through this intermediate organizational layer.
2Measurement precision
If users manually identify relevant data, then data retrieval accuracy is improved, but user cognitive burden and time consumption increase
Solution Approach 1:
The system performs self-service by automatically classifying and organizing data according to life events without requiring user intervention. The database autonomously identifies and associates data with relevant life events, eliminating the need for users to manually search and filter data, thus reducing both time consumption and cognitive burden while maintaining high retrieval accuracy.
Solution Approach 2:
The system performs preliminary organization of data by life events during the data ingestion and storage phases. This preliminary classification work is completed before users need to retrieve data, so when users query the system, the relevant data is already organized and easily accessible, significantly reducing the time and effort users would otherwise need to spend on data identification.
3Productivity
If all data is stored in a single tier, then system complexity is reduced, but data access efficiency for different purposes deteriorates
Solution Approach 1:
The patent segments the storage system into multiple tiers (hot, warm, cold storage) based on data access frequency and life event relevance. Frequently accessed data related to recent life events is stored in hot storage for rapid access, while less frequently accessed data is moved to warmer or colder tiers. This segmentation improves data access efficiency for different purposes while maintaining manageable system complexity through standardized tier management.
Solution Approach 2:
Different storage tiers are assigned different quality characteristics optimized for their specific purposes. Hot storage provides rapid access for frequently needed data, warm storage provides balanced access for moderately frequent data, and cold storage provides cost-effective storage for archival data. This local quality optimization enables efficient data access for different purposes while keeping the overall system structure manageable through clear tier definitions.
Data Source
AI summary
Methods and systems for managing and use of data are disclosed. To manage data, the data may be classified with respect to topics that are relevant to a user of a data storage system. The topics that are relevant to the user may be identified based on digital recordings of conversations between the user and other persons. Over time, the topics that are relevant to the user may change. The changes in relevant topics may be used to identify occurrences of different events in the user's life. These life events and data classified for corresponding topics may be used to service information request by providing context for the data and a means of discriminating more relevant from less relevant data.