Extended Memory System Context Indexing
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Solution Overview
Problem
Conventional file systems are inadequate for managing the vast and dynamic data generated by users, particularly from social networking applications, as they are not well-suited for retention and organization, leading to difficulties in remembering where data is located and what it refers to.
Innovation Solution
An extended memory system that indexes computer-readable data using contextual data, allowing for efficient retrieval and organization by associating data with its context, such as time, location, and application usage, facilitating quick searches and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional file systems are used to organize data, then data can be stored in folders with naming conventions, but users cannot easily remember where particular data is located or what it refers to
Solution Approach 1:
The system automatically captures contextual information (location, time, application, device) at the moment data is generated or accessed, before the user needs to retrieve it. This preliminary capture of context eliminates the need for users to manually organize or remember data locations later.
Solution Approach 2:
The system introduces an intermediary layer between the user and the data storage system. This intermediary automatically tracks and associates contextual metadata with data items, serving as a bridge that connects users to their data through context-based queries rather than manual file navigation.
2Adaptability or versatility
If data from multiple applications is retained in manually generated folders, then data can be organized, but it becomes tedious especially for applications like social networking that generate large amounts of dynamic data
Solution Approach 1:
The system enables self-service organization by automatically capturing and associating contextual metadata with data from multiple applications without requiring user intervention. The system serves itself by autonomously tracking data provenance, application context, and temporal information, eliminating manual organization tasks.
Solution Approach 2:
The system provides a universal organization framework that works across multiple applications and data types simultaneously. By capturing application-agnostic contextual information (device, location, time), the system creates a unified organization mechanism that adapts to diverse data sources without requiring application-specific organization logic.
3Adaptability or versatility
If users employ many different types of applications, then functionality is enhanced, but it becomes difficult to remember which application generated data and what the data references
Solution Approach 1:
The system captures application context and data provenance information at the moment data is generated, before the user needs to retrieve or understand it later. This preliminary recording of which application generated the data and what it references eliminates the need for users to remember this information.
Solution Approach 2:
The system introduces an intermediary layer that transparently tracks and associates application context with generated data. This intermediary automatically records which application produced each data item and what entities the data references, serving as a bridge between diverse applications and user memory.
Data Source
AI summary
Described herein are technologies that are configured to assist a user in recollection information about people, places, and things. Computer-readable data is captured, and contextual data that temporally corresponds to the computer-readable data is also captured. In a database, the computer-readable data is indexed by the contextual data. Thus, when a query is received that references the contextual data, the computer-readable data is retrieved.