Deriving Social Relations from Transaction Ledgers
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Solution Overview
Problem
Current systems lack the ability to effectively derive social relations between accounts based on transaction ledgers and provide a social media service that utilizes these relations for community activities, particularly in blockchain transactions, where calculating a weighted centrality scale for each account is challenging due to large population sizes and complex transaction networks.
Innovation Solution
An apparatus and method that include a data storage unit and processor to derive social relations between accounts based on transaction ledgers, calculate weighted centrality scales, and provide social media services by inquiring and processing social relation information, enabling transactions and community activities with differential fees and limits based on account values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If transaction ledgers are analyzed to derive social relations between accounts, then social media services and community activities can be supported, but the complexity of data processing and calculation increases significantly
Solution Approach 1:
The patent segments the complex task of social relation derivation into distinct modules: transaction data acquisition, social relation extraction, weighted centrality calculation, and service provision. This segmentation allows each module to handle specific aspects independently, reducing overall system complexity while enabling comprehensive social media services.
Solution Approach 2:
The patent introduces an intermediary apparatus that acts as a mediator between the transaction ledger and social media services. This intermediary derives social relations and calculates weighted centralities, transforming raw transaction data into structured social relation information that can be efficiently used by social media services without overwhelming the system.
2Measurement precision
If weighted centrality scales are calculated for all accounts in large blockchain networks, then accurate social relation metrics are obtained, but the calculation time and computational resources increase
Solution Approach 1:
The patent implements partial action by calculating weighted centralities selectively rather than for all accounts simultaneously. The system focuses calculations on accounts that are actively involved in transactions or queries, obtaining sufficient measurement precision for relevant social relations without the excessive computational burden of processing the entire network.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing social relation information and weighted centralities during idle periods or in parallel with transaction processing. This allows the system to have measurement data ready when needed, reducing actual query response time while maintaining accuracy.
3Adaptability or versatility
If social relation information is derived and stored for all account pairs, then comprehensive social media services are enabled, but the storage requirements and data management complexity increase
Solution Approach 1:
The patent applies local quality by storing social relation information selectively based on local needs and activity levels. Rather than uniformly storing all possible account pair relations, the system stores and maintains social relation data primarily for accounts that are actively transacting or have significant interactions, reducing storage requirements while maintaining service capability for active users.
Solution Approach 2:
The patent makes the transaction ledger serve multiple functions: it acts as both the original transaction record and the source for deriving social relation information. This multi-functionality eliminates the need for separate dedicated storage systems for social relations, reducing overall data storage requirements while enabling comprehensive social media services.
Data Source
AI summary
Provided is an apparatus for deriving a social relation between accounts based on a transaction ledger, which includes: a data storage unit storing account information, software for deriving an inter-account social relation, and inter-account social relation information; and a processor deriving the social relation between the accounts based on a transaction ledger generated by transactions among users with the accounts.