User-Driven Knowledge Bank for Contextual Content Recall
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face inefficiencies in retrieving and recalling data due to lack of contextual information in standard searches, leading to long search times and false positives, especially when maintaining folders and emails, which is costly and time-consuming.
Innovation Solution
A system with a knowledge bank that analyzes content from disparate applications, storing relevant information and providing contextual data for efficient recall, using content capture, analysis, and recall instructions, along with API interfaces to improve search specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users use standard search tools and folders to retrieve data, then they can access stored information, but search times are long and results are poor
Solution Approach 1:
The system performs preliminary analysis of content when it is first captured or updated, extracting entities, relationships, and contextual information before it is needed for search. This pre-processing of data structure and meaning enables fast, accurate retrieval without requiring users to manually organize or tag content, resolving the contradiction between search speed and search accuracy.
Solution Approach 2:
The system introduces an intermediary layer between raw data and user queries - a contextualization engine that automatically enriches data with metadata, relationships, and semantic information. This intermediary transforms unstructured data into searchable knowledge graphs, enabling both fast access and high accuracy without requiring users to maintain complex folder structures.
2Ease of operation
If users maintain folders and mark emails for storage, then they can organize data, but the overhead is costly and maintenance is time-consuming
Solution Approach 1:
The system performs self-service organization by automatically capturing content from applications, analyzing its structure and meaning, and placing it in appropriate contextual locations within the knowledge graph without user intervention. The system maintains its own indexing and retrieval structures, eliminating the need for users to manually create folders or tag items while still providing intuitive data organization.
Solution Approach 2:
The system merges multiple functions into a single unified knowledge graph: content storage, metadata extraction, relationship mapping, and search indexing are all combined into one structure. This eliminates the need for separate folder hierarchies and tagging systems, reducing maintenance overhead while providing comprehensive data organization across all applications.
3Productivity
If users perform keyword searches, then they can retrieve information, but false positive hits occur due to lack of contextual information
Solution Approach 1:
The system adds multiple dimensions to search beyond simple keywords: entity types, relationships, contextual metadata, and semantic meanings are included as additional search dimensions. When users search, the system queries across these multiple dimensions simultaneously, filtering out false positives that would result from keyword-only searches while maintaining fast retrieval through indexed relationships.
Solution Approach 2:
The system incorporates feedback loops where search results and user interactions refine the contextualization of data over time. The knowledge graph continuously learns from user behavior patterns to improve entity relationships and contextual associations, making search results increasingly accurate without requiring manual reconfiguration of search parameters.
Data Source
AI summary
Methods for directed analysis of content for storage and recall are performed by systems and apparatuses. The methods optimize search operations for content using a user-driven knowledge bank. A user selects content that is relevant or important to the user for addition to the knowledge bank, and content information about the content is determined based on user importance and context, and is also stored in the knowledge bank. Subsequent searches for the content by the user are optimized based on the content information improving accuracy of search results for the content based on more general queries and searches by the user, including natural language queries and searches. Searches and queries are performed via search engines, as well as digital assistants and search applications of user systems. The content can be added by a user from different applications, and recall of the content from different applications is performed using an API.