Time Machine Service for Social Network Information Classification
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Solution Overview
Problem
Social network services (SNS) currently share fragmented information, lacking high-value added content such as quality images, videos, expert evaluations, and predictive information, making it inconvenient for users to find relevant and reliable information related to past, present, and future aspects.
Innovation Solution
A method and apparatus that classify high-value added information into past, present, and future categories based on user input, using keyword extraction and metadata analysis to provide rich content linked to SNS information, enabling efficient collection and dissemination of high-quality information across SNS platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SNS users share information through social network service, then information sharing capability is improved, but information quality and completeness deteriorate due to fragmentary present and past information only
Solution Approach 1:
The patent segments information into three distinct time-based categories: past information, present information, and future information. This segmentation allows the system to organize fragmented SNS data into structured temporal segments, improving both the completeness and quality of shared information while maintaining the versatility of SNS platforms.
Solution Approach 2:
The patent performs preliminary actions by pre-collecting and organizing rich contents from multiple sources before SNS users need them. The system proactively gathers high-quality information, evaluates its temporal characteristics, and prepares it for delivery, thereby ensuring information quality is maintained while supporting seamless sharing capabilities.
2Adaptability or versatility
If SNS platforms provide diverse information types, then information variety is improved, but information value and reliability deteriorate due to lack of high value added information
Solution Approach 1:
The patent introduces an intermediary information processing system that acts as a mediator between raw SNS data and users. This intermediary system collects, evaluates, and enriches fragmentary SNS information with high-value added content from multiple sources, then delivers the enhanced information to users, thereby preserving information variety while preventing loss of high-value content.
Solution Approach 2:
The patent applies parameter changes by transforming information quality parameters through temporal classification. By evaluating and categorizing information based on temporal parameters (past, present, future), the system enhances the value of diverse information types while maintaining variety, effectively converting unstructured diverse data into structured high-value information.
3Reliability
If users search for high value added information, then information completeness is improved, but search convenience and time efficiency deteriorate
Solution Approach 1:
The patent performs preliminary classification and organization of information by temporal characteristics before users need to search. By pre-evaluating and categorizing information as past, present, or future content, the system prepares information in an easily accessible format, thereby improving information completeness while minimizing the time users need to spend searching.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user information needs and adjusts the delivery of temporally-classified information accordingly. This feedback loop ensures that complete high-value information is provided efficiently, reducing search time while maintaining information completeness through adaptive information delivery.
Data Source
AI summary
Disclosed are a method for providing a time machine service based on an SNS (social network service), including: extracting keywords based on SNS information input by SNS users and collecting rich contents relating to the SNS information based on the keywords; dividing points of past, present, and future, based on a predetermined present time period and classifying the rich contents into any one of past information, present information, and future information, based on the division result; and providing the classified rich contents to the SNS users.