A platform for detecting, reporting, removing, analyzing, and consulting on content, brand, and NFT infringement on the web

Abstract

A system and method for detecting, reporting, removing, consulting, analyzing, and revealing counterfeit and fraudulent products and content on the web. The system comprises various modules that use computer vision, machine learning, natural language processing, neural networks, rule-based algorithms, DMCA notices, legal action, IP consulting, data analysis, visu¬ alization, performance analytics, and IP strategy to search, identify, compare, extract, report, take down, de-index, verify, monitor, track, and prevent counterfeiting and fraudulence in online mar¬ ketplaces, platforms, and applications. The system protects the rights and interests of brand owners, content owners, and NFT owners from illicit activities on the web. The system provides a comprehensive and effective solution for combating counterfeiting and fraudulence on the web.

Description

A platform for detecting, reporting, removing, analyzing, and consulting on content, brand, and NFT infringement on the webThe invention relates to a platform that helps content owners protect their creative assets from counterfeiting and infringement in the digital world. The platform targets fashion and lifestyle brands, digital content companies, and NFT creators who face the risk of losing revenue and reputation due to consumers buying fake goods and content online. The platform provides a solution to this problem by using machine learning models to automate the detection and enforcement of IP and NFT violations in online marketplaces. The platform also uses neural networks, Natural Language Processing and computer vision to assign rarity points to NFTs and to identify and remove unauthorized NFTs. The platform aims to reduce the sales of fake goods and content online and to increase consumer confidence and brand reputation. The platform also offers real-time market analysis for IP infringement to help content owners understand the size and scope of the problem and to take effective action against offenders. The platform enables content owners to focus on high-value strategic activities rather than engaging in manual detection of fraud and violations. The platform is a dedicated tool for IP and NFT protection in the digital world.The present invention relates to the field of online intellectual property (IP), brand, digital content, and NFTs protection and anti-counterfeiting. More specifically, the invention relates to a system and a method for detecting, reporting, removing, and analyzing counterfeit products and non-fungible tokens (NFTs) on various e-commerce marketplaces and platforms using computer vision, machine learning, neural networks, natural language processing, and bot-powered reporting and de-indexing.G06N-G06Q-G06F-G10LSystem and Method for Automated Brand ProtectionWO / 2014 / 092934Brand threat information is identified relating to potential threats to one or more brands of one or more organizations. A characteristic of one or more operating environments is identified and a relationship is determined between a particular one of the potential threats and the characteristic. The determined relationship is used to determine risk associated with a particular brand of an organization. The system and method for automated brand protection aims to provide a comprehensive and efficient way to protect one or more brands from various threats that may harm their reputation, value, and customer loyalty.Automated Social Media-Related Brand ProtectionUS / 2021 / 0194923A method for defending against malicious profiles on the web comprises the steps of: i) inspecting a profile to determine its relevance to a brand that it is desired to protect from malicious activity; ii) determining whether said profile is relevant to said brand; iii) if it is determined that said profile is relevant, analyzing it to determine whether it is legitimate or malicious; and iv) if it is determined that the profile is malicious, assembling proof of its malicious activity and submitting same together with a takedown request to the administrator of the website where the profile was located. The patent claims that this process can help protect the reputation and intellectual property rights of brands from unauthorized or fraudulent use by malicious profiles.Systems And Methods For Handling Fraudulant Uses of BrandsEP3186944The disclosed computer-implemented method for handling fraudulent uses of brands may include (1) enabling a subscriber of a brand-protection service to select an action to perform when a fraudulent use of a brand is detected in Internet traffic that is transmitted via any of a plurality of Internet-traffic chokepoints that are managed by the brand-protection service, (2) monitoring, at each of the plurality of Internet-traffic chokepoints, Internet traffic for fraudulent uses of brands, (3) detecting, while monitoring the Internet traffic, the fraudulent use of the brand, and (4) performing the action in response to detecting the fraudulent use of the brand. Various other methods, systems, and computer-readable media are also disclosed.System and Method For Brand Protection Based on Search ResultsUS / 2021 / 0279743A method and a system for reducing access to a web resource are provided. The method comprises receiving information indicative of a brand to be protected; identifying a set of most popular search queries associated with the brand; acquiring a set of search results for at least one of the set of most popular search queries; calculating a harmfulness coefficient for at least one website contained in the set of search results; identifying the at least one website having the harmfulness coefficient exceeding a threshold value and defining it as a fraudulent website; generating an investment damage score for the fraudulent website; and causing execution of a brand protection measure against the fraudulent website in accordance with the investment damage score. A method and a system for reducing access to a web resource are provided. The method comprises receiving information indicative of a brand to be protected; identifying a set of most popular search queries associated with the brand; acquiring a set of search results for at least one of the set of most popular search queries; calculating a harmfulness coefficient for at least one website contained in the set of search results; identifying the at least one website having the harmfulness coefficient exceeding a threshold value and defining it as a fraudulent website; generating an investment damage score for the fraudulent website; and causing execution of a brand protection measure against the fraudulent website in accordance with the investment damage score.Device, System, and Method of Protecting Brand Names and Domain NamesEP2984577A computerized method of protecting a brand name of a brand owner, includes: (a) crawling a global communication network to identify and collect data about web-sites that possibly abuse the brand name; (b) for each web-site that possibly abuses the brand name, analyzing whether or not the web-site abuses the brand name by analyzing at least one of: (i) content of the web-site; and (ii) data about an owner of the web-site. The method further includes: for each web-site that possibly abuses the brand name, (A) generating an investment score indicating an estimated level of investment that was invested in development of the web-site; and (B) generating a damage score indicating a level of damage that the web-site is estimated to produce to the brand name.An Intellectual Property Protection System And Method Based On a Block ChainCN / 109741217The invention discloses a blockchain-based intellectual property protection system, which comprises a distributed storage platform, an intellectual property platform, a blockchain platform and a client, and is characterized in that an author submits a work file to the intellectual property platform through the client; the distributed storage platform realizes distributed storage of the work filesfrom the intellectual property platform, distributes a hash value for each file as an intellectual property fingerprint according to the contents of the work files, and returns the intellectual property fingerprint; after the intellectual property platform obtains intellectual property fingerprints, on one hand, non-repeated work files form intellectual property data, and the intellectual propertydata are submitted to the block chain platform; on the other hand, intellectual property data responded by endorsement of the block chain platform is submitted to the block chain platform; and the block chain platform writes the endorsed intellectual property data into the block and links the endorsed intellectual property data to the block chain. The intellectual property protection of the work can be effectively realized.NFT Work Storage and Copyright Protection Method Based on Block Chain, IPFS and Digital Watermaking TechnologyCN115730279The invention discloses an NFT work storage and copyright protection method based on a block chain, an IPFS and a digital watermark technology, and the method specifically comprises the steps: a user submits NFT data through a client module, then a data processing module adds a digital watermark to the NFT data, the IPFS stores the NFT data after the digital watermark is added to obtain a file hash, and the file hash is stored in the client module; and finally, the Hash of the IPFS is stored on the block chain through the NFT smart contract to create the NFT. The IPFS is used as a main storage mode of the data, so that the problems generated by a traditional HTTP network protocol are solved, and the defects of long response time and high storage cost caused by overhigh storage pressure of a block chain platform due to direct storage of the NFT original data on the block chain are further avoided. The application of the intelligent contract technology and the digital watermarking technology ensures the non-modifiability and traceability of the Hash value corresponding to the NFT, and provides technical support for solving the copyright dispute problem of the NFT.Intellectual Property Intelligent Transaction Method and System With Information Security ProtectionCN115099976The invention provides an intellectual property intelligent transaction method and system with information security protection. In order to overcome the defects in the existing NFT application technology, the invention provides the intellectual property intelligent transaction method and system with information safety protection, which have the advantages of better experience effect, customer information safety, intelligent contract transaction, intellectual property product safety, product transaction traceability, transaction account safety and easy maintenance. The method comprises the following steps: checking products on which the intellectual property is put on a shelf, starting to display the intellectual property without problems, starting transaction by using an upgraded intelligent contract, collecting, sorting, screening, filtering, integrating, packaging and packaging transaction data, and enabling the whole transaction process to be completely open and transparent in an upper chain and traceable in the whole process. After the transaction is finished, the data is analyzed, summarized and concluded and cannot be tampered, the system regularly performs global infringement retrieval on the intellectual property products, early warning is performed at the first time when infringement is found, and evidences are collected to prepare right protection and counterfeiting prevention.Block Chain - Based Internet Essay Intellctual Property Protection MethodCN108881244The invention belongs to the field of intellectual property protection, and provides a block chain-based Internet essay intellectual property protection method for solving the technical problems thatin existing Internet essay intellectual property protection, whether or not the work is infringed is difficult to detect, and quantization on the plagiarism degree is in deficiency. The block chain-based Internet essay intellectual property protection method comprises the following steps of intellectual property rights stating in the first stage; reprinting authorization in the second stage; and rights protection in the third stage. According to the method, the granularity of Internet essay intellectual property protection can be set, an original author of an Internet essay can state the Internet essay intellectual property in a fine granularity mode, after it is discovered that the Internet essay is infringed, illegal reprinted content is subjected to Hash processing by a server and thencompared with a Hash value array of the original article to obtain a reprinting proportion value, and then the plagiarism degree is quantized; and accordingly, the original author can more conveniently protect the rights of the original author according to the infringing degree of an infringer, and the protection effort on the network essay intellectual property is increased.Harnessing AI and Data Mining for a rubust E- commerce Fraud Detection ModelIN / 2023 / 41042489The proposed invention presents a computer-implemented method for e-commerce fraud detection by leveraging data mining, machine learning, and artificial intelligence techniques. Transactional data from an e-commerce platform is analyzed in real-time, identifying patterns indicative of fraudulent activities. Machine learning algorithms classify transactions as legitimate or suspicious, continuously updating a fraud detection model. Real-time alerts are generated for potentially fraudulent transactions, enabling timely intervention. The model adapts to evolving fraud patterns through artificial intelligence algorithms, incorporating historical data for improved accuracy. Advanced anomaly detection techniques and integration with existing fraud prevention systems enhance overall fraud prevention capabilities. The invention offers statistical analysis and reports on detected fraudulent activities, facilitating further investigation and prevention measures.Systems and methods for machine learning-based digital content clustering, digital content threat detection, and digital content threat remediation in machine learning-based digital threat mitigation platformUS / 2022 / 0232029A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse / digital fraud and not digital abuse / digital fraud; and identifying members or content clusters having digital fraud / digital abuse based on querying the distinct clusters of content data.Systems And Methods For Generating a Probationary Automated-Decisioning Workflow In a Machine Learning-Task Orient Digital Threat or Digital Abuse Mitigation SystemUS / 2023 / 0316282A machine learning-based method for accelerating a generation of automated fraud or abuse detection workflows in a digital threat mitigation platform includes identifying a plurality of distinct digital event features indicative of digital fraud; automatically deriving a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and a digital event data corpus associated with a target subscriber; automatically constructing a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, and deploying the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment associated with the target subscriber.By investigating the patents we have included in this section, we found that they have yet to provide a comprehensive and fundamental method based on artificial intelligence to simultaneously detect counterfeit content, brands, NFT, and intellectual property. Patents

[0063] and

[0066] have provided methods for detecting fraud detection of digital content.The present invention relates to a system and a method for protecting intellectual property, brand, content, and non-fungible tokens (NFTs) from counterfeiting and fraudulence on the web. The system and the method use various technologies and techniques, such as computer vision, machine learning, neural networks, natural language processing, bot-powered reporting and de-indexing, legal action, IP consulting, data analysis, visualization, performance analytics, and IP strategy, to perform different functions, such as:Detecting counterfeit products and NFTs by comparing their images and data with genuine ones and identifying visual and semantic differences and indicators;Reporting and removing infringing products and NFTs by generating and submitting takedown requests and DMCA notices to various e-commerce marketplaces and platforms and de-indexing the websites that host them;Analyzing and revealing counterfeit and fraudulent products and NFTs and their sources by displaying performance metrics and consolidated infringement data by country, marketplace, asset, and seller;Consulting and strategizing on intellectual property, brand, content, and NFT protection by providing access to in-house IP attorneys who can review the infringing listings and provide advice on removal processes and strategies and by providing data-driven recommendations and suggestions on how to optimize and safeguard the legitimate products and NFTs on the web. The system and the method of the present invention provide several advantages, such as high accuracy, efficiency, speed, and convenience in detecting, reporting, removing, analyzing, consulting, and strategizing on counterfeiting and fraudulence of products and NFTs on the web and protecting the rights and interests of the product and NFT owners.The rapid growth of e-commerce and social media has increased the exposure and vulnerability of brands, content, and NFTs to counterfeiting, plagiarism, and fraud. These illicit activities not only harm the reputation and revenue of the owners, but also pose a threat to the safety and satisfaction of the consumers. The existing methods of IP and NFT protection are insufficient and ineffective, as they rely on manual or semi-automated processes that are slow, costly, and prone to errors. Moreover, the existing methods do not account for the dynamic and diverse nature of the online environment, where new forms of infringement and abuse emerge constantly. Therefore, there is a need for a novel and comprehensive solution that can automatically detect, report, and remove counterfeits, fakes, and unauthorized uses of brands, content, and NFTs across various online platforms and marketplaces.Solution of problemThe invention provides a system and a method for detecting counterfeit products on various e-commerce marketplaces and platforms with high accuracy and efficiency, using computer vision-based image recognition and machine learning-based semantic analysis, and self-improving the accuracy based on patterns found among confirmed infringements.The system and the method use acomputer vision moduleto perform image recognition on the product images uploaded by the sellers or the buyers on various e-commerce marketplaces and platforms, and to compare them with the authentic product images obtained from the official sources, such as the brand websites, catalogs, etc. The computer vision module can use image processing and computer vision techniques to analyze the features and attributes of the product images, such as color, shape, texture, logo, label, etc., and to calculate the similarity score of each product image with the authentic product image.The system and the method also use asemantic analysis moduleto perform semantic analysis on the product descriptions and reviews provided by the sellers or the buyers on various e-commerce marketplaces and platforms, and to compare them with the authentic product descriptions and reviews obtained from the official sources, such as the brand websites, catalogs, etc. The semantic analysis module can use natural language processing and machine learning techniques to understand the meaning and sentiment of the product descriptions and reviews, such as keywords, phrases, opinions, ratings, etc., and to calculate the similarity score of each product description and review with the authentic product description and review.The system and the method further use acounterfeit detection moduleto detect counterfeit products on various e-commerce marketplaces and platforms, based on the similarity scores obtained from the computer vision module and the semantic analysis module. The counterfeit detection module can use machine learning and artificial intelligence techniques to classify each product as authentic or counterfeit, based on a predefined threshold or a dynamic threshold that adapts to the data distribution and the user feedback. The counterfeit detection module can also use data mining and statistical techniques to identify the patterns and trends among the confirmed counterfeit products, such as the seller’s identity, location, rating, etc., and to use them to improve the accuracy and efficiency of the counterfeit detection.By integrating these modules, the system and the method not only detect counterfeit products on various e-commerce marketplaces and platforms with high accuracy and efficiency, using computer vision-based image recognition and machine learning-based semantic analysis, but also self-improve the accuracy based on patterns found among confirmed infringements, and provide valuable information and insights to the users and the entities.The presence of this prone to chemical changes.The invention provides a system and a method for reporting counterfeit products on various e-commerce marketplaces and platforms with speed and convenience, using bot-powered rules and self-learning algorithms and constructing infringement libraries to protect brands from fakes effectively.The system and the method use abot moduleto enable the users to report counterfeit products on various e-commerce marketplaces and platforms with speed and convenience. This module can use natural language processing and speech recognition techniques to understand the user’s input and output, and to provide guidance and assistance to the user on how to report a counterfeit product. The module can also use graphical user interface elements to facilitate the reporting process, such as providing buttons, menus, forms, etc.The system and the method also use arule moduleto apply bot-powered rules to the reported counterfeit products, based on the information provided by the users and the entities. This module can use logic and reasoning techniques to evaluate the reported counterfeit products according to predefined or dynamic rules, such as the product name, description, image, price, seller, rating, etc., and to determine the validity and severity of the report.The system and the method further use analgorithm moduleto apply self-learning algorithms to the reported counterfeit products, based on the data collected and analyzed from various sources. This module can use machine learning and artificial intelligence techniques to learn from the feedback and outcomes of the previous reports, and to improve the accuracy and efficiency of the counterfeit detection and reporting.The system and the method additionally use alibrary moduleto construct infringement libraries to store and manage the reported counterfeit products, based on the results of the rule module and the algorithm module. This module can use data mining and statistical techniques to organize and categorize the reported counterfeit products according to various criteria, such as the product type, brand, marketplace, platform, etc., and to provide insights and recommendations to the users and the entities based on the library data.By integrating these modules, the system and the method not only enable the users to report counterfeit products on various e-commerce marketplaces and platforms with speed and convenience, using bot-powered rules and self-learning algorithms, but also construct infringement libraries to protect brands from fakes effectively, and provide valuable information and insights to the users and the entities.The invention provides a system and a method for removing and cleaning counterfeit products from global marketplaces and platforms, using seller intelligence, test purchases, and legal actions, and addressing the source of the problem by identifying high-risk sellers and matching data across platforms to discover the real source of threat and obtain a holistic view of counterfeiting activities and key insights to make informed decisions.The system and the method use aseller intelligence moduleto monitor and analyze the activities and behaviors of the sellers on various marketplaces and platforms, and to identify high-risk sellers who are likely to sell counterfeit products. This module can use web scraping and data mining techniques to collect and compare the seller data from different sources, such as seller profiles, ratings, reviews, feedbacks, etc., and can use machine learning and artificial intelligence techniques to classify the sellers into different risk levels, based on various criteria, such as seller reputation, product quality, price, location, etc.The system and the method also use atest purchase moduleto perform test purchases on the products sold by the high-risk sellers, and to verify the authenticity of the products. This module can use e-commerce and payment technologies to order and pay for the products from the high-risk sellers, and to receive and inspect the products. The module can also use computer vision and image recognition techniques to compare the product images and features with the authentic product images and features obtained from the official sources, such as the brand websites, catalogs, etc., and to determine the authenticity of the products.The system and the method further use alegal action moduleto take legal actions against the sellers who sell counterfeit products, and to remove and clean the counterfeit products from the marketplaces and platforms. This module can use legal and regulatory technologies to file and submit complaints and reports to the marketplaces and platforms, and to the relevant authorities, such as the brand owners, the law enforcement agencies, the courts, etc., and to request and enforce the removal and cleaning of the counterfeit products. The module can also use communication and collaboration technologies to coordinate and cooperate with the marketplaces and platforms, and the relevant authorities, and to provide evidence and documentation to support the legal actions.The system and the method additionally use asource identification moduleto address the source of the problem by matching data across platforms to discover the real source of threat and obtain a holistic view of counterfeiting activities and key insights to make informed decisions. This module can use data mining and statistical techniques to match and link the data of the sellers and the products across different marketplaces and platforms, and to discover the connections and relationships among the sellers and the products, such as the seller’s identity, location, network, etc., and the product’s origin, distribution, supply chain, etc. The module can also use data visualization and analytics techniques to provide a holistic view of the counterfeiting activities and key insights to the users and the entities, based on the data matching and linking, and to enable the users and the entities to access and interact with the data and the insights, such as filtering, sorting, searching, etc.By integrating these modules, the system and the method not only remove and clean counterfeit products from global marketplaces and platforms, using seller intelligence, test purchases, and legal actions, but also address the source of the problem by identifying high-risk sellers and matching data across platforms to discover the real source of threat and obtain a holistic view of counterfeiting activities and key insights to make informed decisions, and provide valuable information and insights to the users and the entities.The invention provides a system and a method for analyzing and consulting on brand protection, using a performance dashboard and in-house IP attorneys, and revealing e-commerce trends and counterfeit landscape with clarity and comprehensiveness.The system and the method use aperformance dashboard moduleto provide a user-friendly and interactive interface for the users and the entities to access and monitor the data and the insights related to the brand protection, such as the number of counterfeit products detected and removed, the number of legal actions taken and resolved, the number of test purchases performed and verified, the number of high-risk sellers identified and tracked, etc. The performance dashboard module can use data visualization and analytics techniques to display the data and the insights in various formats, such as charts, graphs, tables, maps, etc., and to enable the users and the entities to interact with the data and the insights, such as filtering, sorting, searching, etc.The system and the method also use aconsulting moduleto provide professional and personalized advice and guidance on brand protection, using in-house IP attorneys who have expertise and experience in the field of intellectual property and e-commerce. The consulting module can use communication and collaboration technologies to connect the users and the entities with the in-house IP attorneys, and to enable them to communicate and cooperate with each other, such as through voice calls, video calls, text messages, emails, etc. The consulting module can also use legal and regulatory technologies to provide the users and the entities with the relevant information and documentation related to the brand protection, such as the laws, regulations, policies, procedures, etc.The system and the method further use ananalysis moduleto reveal e-commerce trends and counterfeit landscape with clarity and comprehensiveness, using the data collected and analyzed from various sources, such as the marketplaces, platforms, websites, feedbacks, reviews, etc. The analysis module can use data mining and statistical techniques to identify the patterns, trends, and correlations among the data, and to provide insights and recommendations to the users and the entities based on the analysis results. The analysis module can also use natural language generation and speech synthesis techniques to provide verbal insights and recommendations to the users and the entities, based on the content and analysis of the data.By integrating these modules, the system and the method not only analyze and consult on brand protection, using a performance dashboard and in-house IP attorneys, but also reveal e-commerce trends and counterfeit landscape with clarity and comprehensiveness, and provide valuable information and insights to the users and the entities.Advantage effects of inventionThe invention achieves the following technical effects and advantages over the prior art:It improves the quality and reliability of online brand, content, and NFT protection, by using AI and bot technologies that are more accurate, faster, and cheaper than the existing methods.It enhances the scalability and robustness of online brand, content, and NFT protection, by covering a wide range of online platforms and marketplaces, and adapting to the changing and evolving forms of infringement and abuse.It fosters the creativity and innovation of online brand, content, and NFT protection, by offering new and unique features that differentiate and elevate the IPs and NFTs from the counterfeits and fakes.It increases the reputation and revenue of the owners, by protecting their IPs and NFTs from unauthorized and fraudulent uses, and ensuring a safe and satisfying online experience for their consumers.: shows a system for detecting counterfeit products: shows a system for reporting counterfeit products: shows a system for removing and cleaning counterfeit products from global marketplaces: shows a system for analyzing and consulting on brand protection: shows a system for monitoring and tracking copyright-infringing content on the web: shows a system for taking down and de-indexing infringing content on the web: shows a system for analyzing, visualizing, and strategizing on content infringement: shows a system for monitoring and discovering fraudulent NFT cases on the web: shows a system for taking down and sending legal notice letters to fraudulent NFT cases on the web: shows a system for analyzing and revealing e-commerce trends and counterfeit landscape: The flow chart shows a system that uses computer vision and machine learning to detect counterfeit products based on images and data from online listings. The computer vision module compares the image of the product with a reference image of the genuine product and identifies visual similarities and differences, while the machine learning module analyzes data related to the product using a semantic model and identifies indicators of counterfeiting such as price, quality, untrustworthy seller, etc. The system also uses a database of confirmed counterfeit products to provide feedback and improve the accuracy of the computer vision and machine learning modules, and displays the results and the likelihood of the product being counterfeit on a user interface.: The flow chart depicts a system that allows users to report counterfeit products from various online marketplaces using user-defined rules. The system also uses a self-learning module to generate new rules from historical patterns and user feedback, and a library module to store and compare images and texts that are commonly used by counterfeiters. The results of the system are displayed on a user interface that enables users to report the counterfeit products to the relevant marketplaces.: The flow chart describes a system that aims to remove and clean counterfeit products from various online marketplaces using data analysis, test purchases, and legal actions. The system collects data related to the sellers of a specific product from multiple marketplaces and uses a seller intelligence module to identify high-risk sellers and the source of counterfeiting activities. The system then verifies the authenticity of the products using a test purchase module and initiates legal proceedings against the counterfeiters using a legal action module. The results and recommendations of the system are displayed on a user interface for the user.: The flow chart describes a system that helps users protect their brand from counterfeit products by providing data analysis and legal consultation. The system collects data from various online marketplaces and displays performance metrics and infringement data by country, marketplace, asset, and seller. The system also provides access to in-house IP attorneys who can review the infringing listings and provide consulting on removal processes and strategies. The user can interact with the system and the IP attorneys through a user interface.: The flow chart shows a system that monitors and tracks web content that infringes on the rights of the original content owners. The system uses a database module to store and update information on potential sources of illicit content, a neural network module to perform web searches using keywords, phrases, and queries related to the content of interest, a natural language processing module to analyze audio content and extract relevant information and metadata, and a computer vision module to recognize visual content and identify similarities and differences with the content of interest. The system displays the results, including the level of infringement and the source of the content, on a user interface.: The flow chart shows a system that identifies and removes infringing content from the web using DMCA notices and de-indexing. The system receives data from various search engines and platforms about the infringing content and its sources, and uses a bot module to generate and submit DMCA notices to them. The system also uses a de-indexing module to monitor and verify the removal of the infringing content and perform real-time de-indexing of the websites that host it. The system displays the status and progress of the takedown and de-indexing process on a user interface.: The flow chart shows a system that analyzes, visualizes, and strategizes on content infringement using real-time data from various sources. The system uses four modules: data analysis, visualization, performance analytics, and IP strategy, to perform statistical and trend analysis, display graphical and interactive results, estimate loss and impact, and provide data-driven recommendations for protecting and optimizing legitimate content. The system also has a user interface that allows the user to interact with the results and the modules.: The flow chart shows a system that monitors and discovers fraudulent NFT cases on the web using real-time data from various sources. The system uses four modules: data analysis, visualization, performance analytics, and IP strategy, to perform statistical and trend analysis, display graphical and interactive results, estimate loss and impact, and provide data-driven recommendations for protecting and optimizing legitimate content. The system also has a user interface that allows the user to interact with the results and the modules.: The flow chart shows a system that identifies and removes fraudulent NFT cases from the web using takedown requests and legal notice letters. The system receives data from various NFT marketplaces about the infringed NFTs and their sources, and uses a takedown module to generate and submit takedown requests to them. The system also uses a legal notice module to send legal notice letters to the scammers and infringers and suggest and leverage local teams for further investigations and actions globally. The system displays the status and progress of the takedown and legal notice process on a user interface.: The flow chart shows a system that analyzes and reveals e-commerce trends and the counterfeit landscape using data from various marketplaces. The system uses a performance dashboard module to receive data related to counterfeit products and their sources and display performance metrics and consolidated infringement data by country, marketplace, asset, and seller. The system also has a user interface that displays the results of the performance dashboard module and allows the user to interact with them.ExamplesImagine you want to buy an original painting online, but you are not sure if it is real or fake. How can you tell the difference? You can use a system that helps you check the painting before you buy it. This system has four parts:The first part is called a computer vision module. This part can look at the picture of the painting from the online listing and compare it with the picture of a real painting by the same artist. It can find out if they look the same or different. For example, it can check the style, brushstrokes, colors, signature, etc. of the painting.The second part is called a machine learning module. This part can look at other information about the painting from the online listing and analyze it using a smart model. It can find out if there are any signs that the painting is fake. For example, it can check the price, provenance, seller, reviews, etc. of the painting.The third part is called a database. This part can store information about paintings that are confirmed to be fake. It can also give feedback to the first and second parts to help them improve their accuracy. For example, it can tell them what features to look for or ignore when checking the painting.The fourth part is called a user interface. This part can show you the results of the first and second parts and tell you how likely the painting is to be fake. For example, it can show you a score or a label that says "real" or "fake" for the painting.Imagine you want to report fake products online, but you don't have time to check every listing on every marketplace. How can you do that? You can use a system that helps you find and report fake products automatically. This system has four parts:The first part is called a bot module. This part can follow the rules that you give it to identify and report fake products from many listings on many marketplaces. For example, you can tell it to report any product that has a price lower than a certain amount, or that has a bad rating, or that has a suspicious description, etc.The second part is called a self-learning module. This part can learn from the past and from your feedback to create and suggest new rules for finding and reporting fake products. For example, it can learn that some products are more likely to be fake than others, or that some sellers are more trustworthy than others, or that some words or phrases are often used by counterfeiters, etc.The third part is called a library module. This part can store and update pictures and texts that are commonly used by counterfeiters and compare them with the pictures and texts of the products from the listings. For example, it can store and update the logos, labels, packaging, etc. of the real products and check if they match with the ones in the listings.The fourth part is called a user interface. This part can show you the results of the first, second, and third parts and let you report the fake products to the marketplaces where they are listed. For example, it can show you a list of products that are likely to be fake, along with the reasons why, and let you click a button to report them.This system can help you report counterfeit products and protect other buyers from being scammed.Imagine you want to stop fake products from being sold online, but you don't know who is making and selling them or where they are located. How can you do that? You can use a system that helps you find and stop the counterfeiters from different marketplaces around the world. This system has four parts:The first part is called a seller intelligence module. This part can get information about the sellers of products from different marketplaces and analyze it using a smart model to find out which sellers are more likely to sell fake products and match information across platforms to find out where the fake products are coming from. For example, it can get information like the seller's name, address, phone number, email, rating, feedback, etc. and compare it with other sellers on other marketplaces to see if they are the same or different.The second part is called a test purchase module. This part can choose and buy products from the sellers that are more likely to sell fake products and check if they are real or fake using digital methods. For example, it can use a barcode scanner, a hologram detector, a watermark reader, etc. to verify the authenticity of the products.The third part is called a legal action module. This part can send legal notice letters to the counterfeiters and suggest and use local teams to do more investigations and actions globally. For example, it can send letters that warn the counterfeiters to stop selling fake products or face legal consequences, and it can use teams of lawyers, investigators, or police officers in different countries to find and stop the counterfeiters.The fourth part is called a user interface. This part can show you the results of the first, second, and third parts and give you important insights and recommendations. For example, it can show you a list of counterfeiters that are identified and stopped, along with the details of their activities, and it can give you suggestions on how to prevent counterfeiting in the future.This system can help you remove and clean counterfeit products from global marketplaces and protect the rights of the original creators and the buyers.Imagine you want to protect your brand from being copied or stolen online, but you don't know how to measure and improve your brand protection efforts. How can you do that? You can use a system that helps you analyze and consult on brand protection. This system has three parts:The first part is called a performance dashboard module. This part can get information about fake products and their sources from different marketplaces and show you how well you are doing in protecting your brand. For example, it can show you how many fake products are found and removed, how much money you are losing or saving, how many countries, marketplaces, assets, and sellers are involved, etc.The second part is called an IP consulting module. This part can give you access to experts who can help you with your brand protection. These experts are called IP attorneys. They are lawyers who specialize in intellectual property, which is the legal term for things like your brand name, logo, design, etc. They can look at the fake products and give you advice on how to remove them and prevent them from coming back. For example, they can tell you what steps to take, what documents to prepare, what laws to follow, etc.The third part is called a user interface. This part can show you the results of the first and second parts and let you talk to the IP attorneys. For example, it can show you a dashboard with graphs, charts, and tables that summarize your brand protection performance, and it can let you chat with the IP attorneys through text or voice.This system can help you analyze and consult on brand protection and protect your brand from being copied or stolen online.Imagine you have created some content, such as a song, a video, a book, or a picture, and you want to protect it from being copied or stolen on the web. How can you do that? You can use a system that helps you monitor and track the content that is similar or identical to yours on the web. This system has five parts:The first part is called a database module. This part can store and update a large list of websites, platforms, and applications that are possible sources of illegal content. For example, it can store and update the names, addresses, and types of websites like YouTube, Facebook, Instagram, etc. that might have content that is similar or identical to yours.The second part is called a neural network module. This part can search the web using words, phrases, and questions related to your content. It can do this very fast and very well. For example, it can search the web using the title, lyrics, or genre of your song, or the name, plot, or characters of your book, or the theme, style, or colors of your picture, etc.The third part is called a natural language processing module. This part can listen to the audio content of the web pages and platforms and get important information and details from it. For example, it can listen to the songs, videos, podcasts, etc. on the web and get information like the name, artist, album, language, etc. of the audio content.The fourth part is called a computer vision module. This part can look at the visual content of the web pages and platforms and find out if they are similar or different from your content. For example, it can look at the pictures, videos, books, etc. on the web and find out if they have the same or different style, shape, size, logo, signature, etc. as your content.The fifth part is called a user interface. This part can show you the results of the second, third, and fourth parts and tell you how similar or identical the content on the web is to yours and where it comes from. For example, it can show you a score or a label that says "similar" or "identical" for the content on the web, and it can show you the name, address, and type of the website, platform, or application where the content is found.This system can help you monitor and track the content that is similar or identical to yours on the web and protect your rights as the original creator.Imagine you have some content on the web, such as a song, a video, a book, or a picture, and someone else copies or steals it and puts it on their own website without your permission. How can you make them stop and remove your content from their website? You can use a system that helps you take down and de-index the content that is copied or stolen from you on the web. This system has three parts:The first part is called a bot module. This part can get information about the content that is copied or stolen from you and its sources from different search engines and platforms. It can also create and send DMCA notices to them. A DMCA notice is a legal document that tells the search engines and platforms that they are showing or linking to content that belongs to you and that they have to remove it or face legal consequences12. For example, it can get information like the title, author, URL, etc. of the content that is copied or stolen from you and send DMCA notices to Google, YouTube, Facebook, etc. to ask them to take down the content or the links to the content.The second part is called a de-indexing module. This part can check and make sure that the content that is copied or stolen from you is removed from the search engines and platforms. It can also de-index the websites that host the content that is copied or stolen from you. De-indexing means removing the websites from the search results, so that people cannot find them or access them easily3. For example, it can check if Google, YouTube, Facebook, etc. have taken down the content or the links to the content that is copied or stolen from you and de-index the websites that still have the content on them.The third part is called a user interface. This part can show you the results of the first and second parts and tell you how the takedown and de-indexing process is going. For example, it can show you a list of the content that is copied or stolen from you and its sources, along with the status and progress of the DMCA notices, the removals, and the de-indexing.This system can help you take down and de-index the content that is copied or stolen from you on the web and protect your rights as the original creator.Imagine you have some NFTs, which are unique digital items that belong to you and are stored on a blockchain, which is a secure online ledger. You want to know if anyone is copying or stealing your NFTs and selling them on the web. How can you do that? You can use a system that helps you monitor and discover fraudulent NFT cases on the web. This system has five parts:The first part is called a database module. This part can store and update a large list of NFT marketplaces, which are websites where people can buy and sell NFTs. It can also check if these marketplaces are possible sources of illegal NFTs, which are NFTs that are copied or stolen from someone else.The second part is called a neural network module. This part can search the web using words, phrases, and questions related to your NFTs. It can do this very fast and very well. For example, it can search the web using the name, description, or category of your NFTs, or the name of the artist who created them, etc.The third part is called a natural language processing module. This part can listen to the audio content of the web pages and platforms and get important information and details from it. For example, it can listen to the songs, videos, podcasts, etc. on the web and get information like the name, artist, album, language, etc. of the audio content.The fourth part is called a computer vision module. This part can look at the visual content of the web pages and platforms and find out if they are similar or different from your NFTs. For example, it can look at the pictures, videos, books, etc. on the web and find out if they have the same or different style, shape, size, logo, signature, etc. as your NFTs.The fifth part is called a user interface. This part can show you the results of the second, third, and fourth parts and tell you how similar or identical the NFTs on the web are to yours and where they come from. For example, it can show you a score or a label that says "similar" or "identical" for the NFTs on the web, and it can show you the name, address, and type of the website, platform, or application where the NFTs are found.This system can help you monitor and discover fraudulent NFT cases on the web and protect your NFTs from being copied or stolen.Imagine you have some NFTs, which are unique digital items that belong to you and are stored on a blockchain, which is a secure online ledger. You want to make the people who copy or steal your NFTs and sell them on the web stop and remove your NFTs from their websites. How can you do that? You can use a system that helps you take down and send legal notice letters to the people who copy or steal your NFTs on the web. This system has three parts:The first part is called a takedown module. This part can get information about the NFTs that are copied or stolen from you and their sources from different NFT marketplaces, which are websites where people can buy and sell NFTs. It can also create and send takedown requests to them. A takedown request is a document that tells the NFT marketplaces that they are showing or selling NFTs that belong to you and that they have to remove them or face legal consequences12. For example, it can get information like the name, description, or URL of the NFTs that are copied or stolen from you and their sources, and it can send takedown requests to OpenSea, Rarible, SuperRare, etc. to ask them to take down the NFTs or the links to the NFTs.The second part is called a legal notice module. This part can send legal notice letters to the people who copy or steal your NFTs and sell them on the web. A legal notice letter is a document that warns the people who copy or steal your NFTs to stop doing that or face legal consequences3. It can also suggest and use local teams to do more investigations and actions globally. For example, it can send legal notice letters to the email addresses or physical addresses of the people who copy or steal your NFTs, and it can suggest and use teams of lawyers, investigators, or police officers in different countries to find and stop the people who copy or steal your NFTs.The third part is called a user interface. This part can show you the results of the first and second parts and tell you how the takedown and legal notice process is going. For example, it can show you a list of the NFTs that are copied or stolen from you and their sources, along with the status and progress of the takedown requests, the legal notice letters, and the investigations and actions.This system can help you take down and send legal notice letters to the people who copy or steal your NFTs on the web and protect your NFTs from being copied or stolen.Imagine you want to know more about the e-commerce trends and the counterfeit landscape, which are the patterns and the problems of online shopping and fake products. How can you do that? You can use a system that helps you analyze and reveal the e-commerce trends and the counterfeit landscape. This system has two parts:The first part is called a performance dashboard module. This part can get information about the fake products and their sources from different e-commerce marketplaces, which are websites where people can buy and sell products. It can also show you how well or how bad the situation is in different countries, marketplaces, products, and sellers. For example, it can get information like the number, location, type, and frequency of the fake products and their sources, and it can show you things like the growth rate, the distribution, the correlation, etc. of the information.The second part is called a user interface. This part can show you the results of the first part in various ways that are easy to understand and interact with. For example, it can show you graphs, maps, tables, and dashboards that summarize and illustrate the information, and it can let you zoom in, filter, sort, and export the information.This invention is a system for detecting, reporting, removing, cleaning, analyzing, consulting, and strategizing on counterfeit products, NFTs, and contents on the web. This invention has a high industrial applicability, because it can be used in many industries that are affected by counterfeiting and piracy, such as:The creative industry, which includes artists, musicians, writers, filmmakers, designers, etc. who create original content and want to protect their intellectual property rights and revenues from online infringement.The manufacturing industry, which includes producers, distributors, and retailers of physical products and goods who want to prevent counterfeit products from entering the market and damaging their brand reputation and customer trust.The e-commerce industry, which includes online platforms and marketplaces that facilitate the buying and selling of products and services and want to ensure the quality and authenticity of their offerings and avoid legal liabilities and customer complaints.The legal industry, which includes lawyers, attorneys, and law firms that specialize in intellectual property and anti-counterfeiting and want to provide effective and efficient solutions and services to their clients and partners.This invention can also benefit other industries that are related to or depend on the above-mentioned industries, such as the entertainment industry, the fashion industry, the pharmaceutical industry, the education industry, the gaming industry, etc.This invention can also create new opportunities and markets for the development and innovation of related products and services, such as:Anti-counterfeiting software and hardware, which can help detect, report, remove, clean, analyze, consult, and strategize on counterfeit products and content on the web using advanced technologies such as artificial intelligence, machine learning, computer vision, natural language processing, blockchain, etc.Anti-counterfeiting education and training, which can help raise awareness and knowledge about the risks and impacts of counterfeiting and piracy and the best practices and strategies to prevent and combat them among various stakeholders such as creators, consumers, sellers, platforms, authorities, etc.Anti-counterfeiting certification and verification, which can help establish and maintain standards and criteria for the quality and authenticity of products and content on the web and provide reliable and trustworthy proofs and guarantees to the stakeholders.

Claims

A system for detecting counterfeit products, comprising:a computer vision module configured to receive an image of a product from a listing and compare it with a reference image of a genuine product to identify visual similarities and differences;a machine learning module configured to receive data related to the product from the listing and analyze it using a semantic model to identify indicators of counterfeiting such as price, quality, untrustworthy seller, etc.a database configured to store information about confirmed counterfeit products and provide feedback to the computer vision module and the machine learning module to improve their accuracy;a user interface configured to display the results of the computer vision module and the machine learning module and indicate the likelihood of the product being counterfeit.A system for reporting counterfeit products, comprising:According to claim 2 a bot module configured to receive user-defined rules for identifying and reporting counterfeit products from a plurality of listings on a plurality of marketplaces;According to claim 3 a self-learning module configured to generate and suggest new rules based on historical patterns of confirmed counterfeit products and user feedback;According to claim 4 a library module configured to store and update images and texts that are commonly used by counterfeiters and compare them with the images and texts of the products from the listings;According to claim 5 a user interface configured to display the results of the bot module, the self-learning module, and the library module and allow the user to report the counterfeit products to the respective marketplaces.A system for removing and cleaning counterfeit products from global marketplaces, comprising:According to claim 7 a seller intelligence module configured to receive data related to sellers of products from a plurality of marketplaces and analyze it using a risk model to identify high-risk sellers and match data across platforms to discover the source of counterfeiting activities;According to claim 8 a test purchase module configured to select and purchase products from high-risk sellers and verify their authenticity using digital methods;According to claim 9 a legal action module configured to send legal notice letters to counterfeiters and suggest and leverage local teams to conduct further investigations and actions globally;According to claim 10 a user interface configured to display the results of the seller intelligence module, the test purchase module, and the legal action module and provide key insights and recommendations to the user.A system for analyzing and consulting on brand protection, comprising:According to claim 12 a performance dashboard module configured to receive data related to counterfeit products and their sources from a plurality of marketplaces and display performance metrics and consolidated infringement data by country, marketplace, asset, and seller;According to claim 13 an IP consulting module configured to provide access to in-house IP attorneys who can review the infringing listings and provide consulting on removal processes and strategies;According to claim 14 a user interface configured to display the results of the performance dashboard module and the IP consulting module and allow the user to interact with the in-house IP attorneys.A system for monitoring and tracking copyright-infringing content on the web, comprising:According to claim 16 a database module configured to store and update a large database of websites, platforms, and applications that are potential sources of illicit content;According to claim 17 a neural network module configured to perform efficient and powerful searches on the web using keywords, phrases, and queries related to the content of interest;According to claim 18 a natural language processing module configured to perform speech recognition on the audio content of the web pages and platforms and extract relevant information and metadata;According to claim 19 a computer vision module configured to perform image recognition on the visual content of the web pages and platforms and identify similarities and differences with the content of interest;According to claim 20 a user interface configured to display the results of the neural network module, the natural language processing module, and the computer vision module and indicate the level of infringement and the source of the content.A system for taking down and de-indexing infringing content on the web, comprising:According to claim 22 a bot module configured to receive data related to infringing content and its sources from a plurality of search engines and platforms and generate and submit DMCA notices to them;According to claim 23 a de-indexing module configured to monitor and verify the removal of the infringing content from the search engines and platforms and perform real-time de-indexing of the websites that host the infringing content;According to claim 24 a user interface configured to display the results of the bot module and the de-indexing module and indicate the status and progress of the takedown and de-indexing process.A system for analyzing, visualizing, and strategizing on content infringement, comprising:According to claim 26 a data analysis module configured to receive real-time data related to infringing content and its sources from a plurality of search engines and platforms and perform statistical and trend analysis on the data;According to claim 27 a visualization module configured to display the results of the data analysis module in various graphical and interactive formats such as charts, maps, tables, and dashboards;According to claim 28 a performance analytics module configured to estimate the magnitude of loss from content infringement and the impact of anti-piracy efforts on the legitimate content and provide key performance indicators and metrics;According to claim 29 an IP strategy module configured to provide data-driven recommendations and suggestions on how to protect and optimize the legitimate content in search engines and platforms;According to claim 30 a user interface configured to display the results of the visualization module, the performance analytics module, and the IP strategy module and allow the user to interact with them.A system for monitoring and discovering fraudulent NFT cases on the web, comprising:According to claim 32 a database module configured to store and update a large database of NFT marketplaces that are potential sources of illicit NFTs;According to claim 33 a neural network module configured to perform efficient and powerful searches on the web using keywords, phrases, and queries related to the NFTs of interest;According to claim 34 a natural language processing module configured to perform speech recognition on the audio content of the web pages and platforms and extract relevant information and metadata;According to claim 35 a computer vision module configured to perform image recognition on the visual content of the web pages and platforms and identify similarities and differences with the NFTs of interest;According to claim 36 a user interface configured to display the results of the neural network module, the natural language processing module, and the computer vision module and indicate the level of fraudulence and the source of the NFTs.A system for taking down and sending legal notice letters to fraudulent NFT cases on the web, comprising:According to claim 38 a takedown module configured to receive data related to infringed NFTs and their sources from a plurality of NFT marketplaces and generate and submit takedown requests to them;According to claim 39 a legal notice module configured to send legal notice letters to scammers and infringers and suggest and leverage local teams to conduct further investigations and actions globally;According to claim 40 a user interface configured to display the results of the takedown module and the legal notice module and indicate the status and progress of the takedown and legal notice process.A system for analyzing and revealing e-commerce trends and counterfeit landscape, comprising:According to claim 42 a performance dashboard module configured to receive data related to counterfeit products and their sources from a plurality of e-commerce marketplaces and display performance metrics and consolidated infringement data by country, marketplace, asset, and seller;According to claim 43 a user interface configured to display the results of the performance dashboard module and allow the user to interact with them.

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