IP Infringement Detection for Market Surveillance

Intellectual property analysis method and system using big data to evaluate the innovation, potential value, and infringement of intellectual property. The method involves detecting plagiarism in knowledge results by analyzing keyword overlap with professional fields. It also assesses practicality, social benefit, and economic benefit of knowledge results. This provides a comprehensive evaluation of intellectual property quality and risk. The system has modules for infringement detection, potential value assessment, and intellectual property database.

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Intelligent method for detecting intellectual property infringement using computer devices. The method involves extracting key information like trademarks, names, and images from enterprise data. It then compares and analyzes the extracted data to determine if there is infringement. The analysis includes steps like comparing text meaning, extracting associated entities, verifying relationships, and finding events. The computer device generates infringement determination results based on these analyses.

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An online automated system for detecting intellectual property infringements in e-commerce products. The system uses machine learning techniques to analyze text, images, and videos from product listings to identify potential infringements. It compares features like text similarity, image matching, and video comparison against authorized brand and licensing libraries to detect potential infringements. The system aims to improve speed and accuracy of finding infringements compared to manual inspections, particularly for large volumes of products.

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Intellectual property (IP) early warning system to proactively monitor and detect potential IP infringements. The system collects IP information from various sources, analyzes it, and provides alerts when there are matches or similarities. It has modules to obtain IP data of competing products, compare features, calculate coincidence levels, and display the results. The system aims to provide a comprehensive and efficient way to track IP rights across organizations and jurisdictions, and quickly identify potential infringements.

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Efficiently and accurately detecting intellectual property infringement using a method and device that retrieves products based on keywords, identifies intellectual property marks, compares product descriptions to IP content, and determines infringement. The method involves receiving an IP infringement detection task with product keywords and scope, retrieving products and their content matching those keywords, identifying IP marks and products, comparing descriptions to IP content, and determining infringement based on the comparisons.

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Active early warning system for identifying parties involved in intellectual property infringement using AI. The system collects data from various sources like transactions, logistics, communications. It preprocesses the data to generate a normalized dataset. An AI model is trained using reinforcement learning to identify infringers based on feedback scores. The model is deployed in a distributed server architecture with clustering and distributed storage. The system can continuously learn and improve by feeding back scores from the model's predictions.

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Intellectual property infringement detection and reminder system that analyzes data to detect potential intellectual property infringement and alerts the rights holder. The system involves a data analysis module that receives uploaded intellectual property content to analyze for infringement using techniques like keyword extraction and feature comparison. This module can detect infringement of trademarks, patents, and copyrights. The analysis results are displayed in real-time on a feedback terminal. The system also has a reminding alarm terminal to alert the rights holder of potential infringement.

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Efficiently prompting and handling intellectual property infringement by using machine learning to match rights protection strategies to suspected infringers based on historical data. The method involves crawling web pages, filtering for infringement suspects, matching strategies using a model, and feeding back strategies to users. It also involves monitoring tasks and sending timely infringement alerts.

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Method and device for infringement monitoring and evidence collection to help protect intellectual property rights by effectively monitoring and preserving digital content for evidence of infringement. It involves solidifying and encrypting user data, timestamps, and unique feature codes for the data. This is then compared against similar content in monitored areas to identify potential infringements. Infringing content is extracted, solidified, and timestamped with the feature codes. These are then stored and submitted to judicial institutions for preservation to provide evidence of infringement.

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Intellectual property infringement analysis system and method for efficient rights protection using AI. The system involves an intellectual property data analysis module that stores trademark and copyright information. It compares infringing subject data with stored rights data using a comparator. The difference ratio is calculated and compared against a threshold to determine if infringement is likely. This automated analysis helps rights holders and protection agencies quickly assess infringement cases and prioritize actions.

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Monitoring app stores for intellectual property infringement by crawling app stores, collecting metadata about apps, and analyzing it to detect potential IP violations. The system can also allow users to define brand tracking and receive alerts when apps using their brands change metadata. This provides a tool to combat trademark infringement and dilution in the app ecosystem.

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