Smart Contract Analysis using Artificial Intelligence

Securely storing and analyzing transaction data using a distributed register and machine learning for dispute resolution. The system allows secure, immutable storage of resource transactions using a distributed register. Disputes can be initiated by users, analyzed using machine learning, and resolutions determined based on historical data. The distributed register prevents unauthorized data manipulation. Users can access verified data via multiple channels.

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Automated system for reviewing contracts and other legal documents to identify potential issues and provide qualitative analysis of document terms. The system uses machine learning to analyze source documents and provide feedback on term favorability, monitor changes over time, and certify compliance. It takes a source document, classifies it based on document type and industry, and uses AI to analyze the terms. This allows rapid, substantive analysis of document provisions without extensive legal expertise. The system can also monitor documents over time to identify changes and generate alerts for significant ones.

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Determining trigger events for blockchain smart contracts using a search engine and knowledge database. The method involves obtaining smart contract data from the blockchain, processing it to determine the trigger event, generating a query based on the event, recursively searching the knowledge database with the query, and determining if the event has occurred based on the search results. This allows leveraging authoritative sources like search engines to determine blockchain smart contract triggers instead of relying solely on blockchain data.

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Detecting anomalies in blockchain transactions using artificial intelligence to identify fraud and other invalid transactions. The method involves extracting graph parameters from block transaction graphs, generating statistical approximations, comparing against thresholds, detecting irregular patterns, identifying anomalies, generating address graphs, and alerting when anomalies are found. A neural network trained to analyze graphs for irregular patterns based on statistical approximations detects anomalies in block transactions over time.

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Real-time identification of anomalies in blockchain using artificial intelligence to detect fraud and invalid transactions. The system extracts block parameters and generates statistical approximations. It compares these approximations to thresholds and detects irregular patterns indicating anomalies. The AI model identifies anomalies within the block and generates alerts when anomalies are found. This allows real-time detection and prevention of fraudulent transactions in blockchains.

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Evaluating transaction requests received by a computer system using a machine learning algorithm to improve accuracy in granting versus declining transactions. The computer system trains a machine learning model using historical transaction requests. When a new request comes in, the model scores it and compares to a threshold. If above, the request is granted, if below, declined. This improves accuracy compared to just using a fixed threshold as the model learns from prior requests. However, wrongly rejecting or granting transactions can degrade performance. To mitigate this, the model is retrained periodically using a subset of recent requests to adapt to changing conditions. This allows updating the model without having to retrain from scratch.

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System for providing real-time security actions for blockchain operations based on AI models trained on labeled blockchain data. The system receives blockchain data, identifies operations, generates feature inputs based on blockchain characteristics, processes through AI models trained on labeled data, determines security actions, and alerts users of potential risks before execution. The labeled data is from sources and previously processed blocks. This allows customized risk assessment and prevention for new operations.

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Automatic detection of security threats in blockchain smart contracts using machine learning and anomaly detection to identify abnormal transactions and alert blockchain users. The system monitors transactions on a blockchain, generates risk scores for each transaction based on expected behavior learned from smart contract analysis, and sends alerts if scores fall below thresholds. It uses techniques like signature matching, heuristics, and machine learning models to detect malicious transactions.

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Automatically evolving smart contract endorsement policies in blockchain to adapt and mitigate fraud. The method involves computing historical patterns of fraud attempts from transaction logs, predicting future fraud, correlating historical and predicted fraud, modifying endorsement policies based on correlations, and adding the modified policies to the smart contract. This allows the smart contract to dynamically evolve and adapt its endorsement rules over time based on detected fraud patterns.

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Decentralized ecosystem for contracting, execution, and policy enforcement in secure zero-trust computing capsules for multi-party environments. The ecosystem allows different entities from different trust domains to accelerate agreement on collaboration terms in many-to-many relationships between participants. It provides a way for participating entities to contract and execute agreements in a decentralized, confidential, and trustless manner. The contracts are executed within the ecosystem using smart contracts, where terms, resources, legislation, and privacy regulations are federated and orchestrated across trust domains. The ecosystem supports features like dispute resolution, resource federation, privacy compliance, and trust domain federation.

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Automating contract compliance verification for business-to-business (B2B) transactions using smart contracts. The system involves digitizing and enforcing contractual terms and obligations using self-identifying smart contracts. It receives inputs from a master services agreement (MSA) to generate smart contract information. It then interactsively adjusts and implements the smart contract to create a smart purchase agreement (SPA) in blockchain. The SPA is automatically validated against the MSA in real-time to detect conflicts. If a conflict occurs, a warning is provided. This allows automated, real-time contract compliance checking and generation without needing manual review.

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Automated contract analysis and generation system that leverages natural language processing (NLP) and AI to transform business files into question lists and contract templates, enabling efficient offline negotiation and generation with a single click. The system parses keywords from processed files, generates question lists, and templates based on them. After offline negotiation using the question lists, the filled-in lists are uploaded to generate the final contracts. This enables efficient negotiation and generation compared to manual parsing and filling. The contracts can then be signed, uploaded, and analyzed for future model training.

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Using AI to analyze contract terms and provide recommendations to help users decide if they should accept offers. The system converts images of contracts to text, identifies key terms, analyzes them using a trained model, and generates recommendations like accepting vs modifications based on term scores. This aims to provide quick and automated contract analysis for users who don't have time to manually research terms.

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A quantum-safe blockchain consensus protocol and smart contract execution method that enables fast, post-quantum secure computation without using quantum-sensitive cryptography. The protocol involves quantum-safe aBFT consensus for blockchain and MPC for smart contracts. It uses techniques like common randomness, secret sharing, and quantum-safe consensus to provide quantum resistance while maintaining efficiency. The method also allows delegated multi-provers to collaboratively compute zero-knowledge proofs without revealing details.

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Modeling and managing smart contracts using formal automata to verify compatibility and detect violations. The smart contract is represented as a computation model using interface automata. It captures requirements, resources, services, etc. Compatibility between the models is verified and input/output actions synchronized. The compatible smart contract is recorded immutably. This formal modeling approach enables automated verification of smart contract terms and detection of violations.

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Generating smart contract code from natural language documents using neural networks to match the intent of the text with the contract logic. The method involves extracting information from the document using NLP, selecting appropriate code templates based on the text sections, and filling in values like entity identifiers, conditions, and dates from the text. This allows generating smart contract code that aligns with the original document's meaning.

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Continuous vulnerability management system for blockchain smart contracts that uses sandboxing and AI to proactively detect and mitigate vulnerabilities in smart contracts. The system involves scanning smart contracts in a sandbox environment using static and dynamic analysis techniques. It also leverages AI to acquire new vulnerability information and threat intelligence. A risk scoring engine uses machine learning to analyze the sandbox results and assign risk scores to vulnerable contracts. A security control system then takes actions on contracts with high risk scores to mitigate the threats.

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Intelligent consensus models, smart contracts, and sidechain technologies for distributed ledgers like blockchains that enable advanced functionality and interoperability. The intelligent consensus models allow blockchain nodes to dynamically choose consensus protocols based on transaction types. Smart contracts with machine learning models are deployed to improve contract execution. Sidechains allow tokens from one blockchain to securely be used in another blockchain.

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Blockchain analytics system that allows easier interaction with blockchains and smart contracts by providing tools and services for organizing, analyzing, and visualizing blockchain data. It enables users to authenticate and integrate their off-chain identities with their blockchain identities, making it simpler to use blockchain applications without requiring deep knowledge of cryptography and blockchain technologies. The system also facilitates generating machine learning models for blockchain analytics, allows automated deployment of blockchain objects, and provides services like signing to abstract the blockchain interaction process.

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Predicting smart contract violations before they occur to prevent unnecessary mining and conserve computing resources. The method involves generating a state space tree for a smart contract's variables based on its control flow graphs. The tree shows possible states reachable from the current one. The tree is updated after each transaction. Analyzing the tree determines if it contains violation states. If so, an alert is sent to intervene before the violation occurs. This prediction helps avoid wasted mining effort and conserves resources.

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