Blockchain Robocall Detection with AI and Swarm Intelligence
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Solution Overview
Problem
Current robocall-blocking systems are ineffective due to their centralized architectures, vulnerability to cyber-attacks, and inability to identify and block calls from spoofed numbers, leading to increased fraud and victimization of consumers.
Innovation Solution
A method and system integrating blockchain, swarm intelligence, machine learning, deep learning, artificial intelligence, and a telecommunication expert knowledge graph to determine and block robocalls at the device level, utilizing blockchain's immutable storage and advanced AI algorithms for real-time parsing and filtering, with a localized call-filtering feature based on federal and state laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized database architecture is used for call-blocking, then the system is easier to manage and update, but it becomes vulnerable to cyber-attacks and single point of failure
Solution Approach 1:
The patent segments the centralized call-blocking database into a distributed network of nodes using blockchain technology. Each node maintains a copy of the blockchain ledger containing blocked number information, eliminating the single point of failure while maintaining consistent blocking rules across the network. This segmentation provides both security through decentralization and operational consistency through the immutable nature of blockchain.
2Device complexity
If a static, centralized database of blacklisted numbers is used, then the system is simpler to implement, but it becomes ineffective against spoofed and recycled numbers
Solution Approach 1:
The patent transforms the static blacklisted numbers database into a dynamic system using blockchain technology. The blockchain ledger continuously updates with new blocked numbers, scam patterns, and spoofed number information from multiple sources including user reports and law enforcement databases. This dynamic update mechanism ensures the system remains effective against evolving robocall techniques while maintaining relative simplicity through automated consensus-based updates.
3Use of energy by moving object
If conventional call-blocking features are used, then the system requires minimal additional resources, but it cannot identify robocalls from unknown or spoofed numbers
Solution Approach 1:
The patent introduces a blockchain-based intermediary layer that sits between the conventional call-blocking features and the incoming calls. This intermediary validates caller information against the distributed blockchain ledger containing known scam patterns, spoofed numbers, and blocked contacts. The intermediary provides enhanced identification accuracy by cross-referencing multiple data sources while maintaining efficient resource usage through optimized blockchain query mechanisms.
Data Source
AI summary
An innovative method is implemented to determine a robocall and blocks the incoming communication deemed to be a robocall. The method leverages blockchain's shared storage, memory, and ability to transact all information across a network and independently verified and stored on the immutable blockchain. The method takes advantage high-speed cellular network to process each communication with high-speed. Further, the method integrates blockchain encryption, swarm intelligence (SI), artificial intelligence (AI) and machine learning (ML) algorithms, a telecommunication expert knowledge graph (TEKG), and real-time parsing of records to block robocalls and reduce connection delays. All modules can evolve and update themselves with each use of the present invention through various SI, AI, and ML technologies. Additionally, the method includes a localized call-filtering feature based on state and federal laws to ensure the blocking of calls that are prohibited by either federal or state governments thereby facilitating recovery of damages.