Intelligent Databases for IP Asset Management

A patent management system that helps users quickly find relevant patents, analyze patent portfolios and monitor competitors. The system uses AI techniques like claim mapping, concept organization, and similarity indexing to enable efficient patent searching, analysis, and comparison. It provides tools like portfolio analytics, reference management, prior art analytics, docketing management, claim mapping, claim analytics, portfolio analytics, external database analytics, annuity management, and strategic monitoring.

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System and method for concept-based management of categorizations or classifications to organize a commonplace, enhancing the navigability of very large information bases by providing in-depth sub-categorization of terminology bases, providing users with incentives to be creative, protecting crowd-sourced contributions, managing searches for what is known either within, or in some accessible location outside of it, and establishing communities associated especially with the concepts, or its narrow categories, and particularly in Intellectual Property. It provides a user with a searching tool for something known or unknown, capturing the concept of the unknown to be reused as if known. This invention extends to new forms of fuzzy clustering and hierarchical self-organizing maps.

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A method for constructing a patent knowledge database that efficiently analyzes massive patent resource data to extract effective patent information. The method involves analyzing patent specifications and drawings to obtain explicit information like patentee names and application dates, and implicit information like relationships between technical elements. The explicit and implicit information is fused to filter noise and construct the patent knowledge database.

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A system for efficiently and accurately screening and analyzing intellectual property data by automating the process using a database, query, screening, control, early warning, storage, and client modules. The system collects intellectual property data, allows targeted queries, performs automated screening using a control module, and provides early warning of issues like expiring rights or infringement risks. The system improves efficiency and accuracy compared to manual retrieval and analysis.

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Intellectual property operation management service platform using big data to centralize, integrate, and manage intellectual property information. The platform has an application service system and a data resource management system. The application service system provides intellectual property services like government services, operation management, retrieval, evaluation, display, user management, and background management. The data resource management system builds a patent big data repository with databases like domestic patent, foreign patent, national IP, enterprise, personnel, and industry. The data resource cataloguing module analyzes big data, determines coding systems, and plans metadata. The retrieval evaluation module provides patent retrieval and evaluation using models like cost, present value, and market comparison. The platform collects, processes, updates, and manages big IP data efficiently for stable and automated IP operation services.

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A patent management system that allows an inventor, business manager, or patent attorney to quickly and easily obtain up-to-date information relating to cited references or technology trends in the art to which a user's or competitor's patent portfolio relates. The system includes a database, which stores data representing each matter in which a patent application is filed, an external database, and an operation database.

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Visualized intellectual property operation management system for streamlining IP processing and collaboration between clients and service organizations. The system uses visualization to provide a personalized, synchronized, and customizable view of IP data and processes for all parties involved. It allows clients and organizations to access and interact with IP data and documents through a visual interface that presents the information tailored to their needs. The system uses a shared database to store IP data like patent drafts and submissions. This allows accurate tracking and recording of IP documents while preventing ambiguous responsibilities.

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A method to build an intellectual property data analysis system that allows efficient processing and analysis of unstructured intellectual property data. The method involves extracting keywords from both unstructured and structured intellectual property data to create a general-purpose intellectual property data system for patent keyword analysis. It converts unstructured data into structured format through pre-processing and analysis steps. This enables semantic interoperability between textual IP sources and allows building a knowledge base system for retrospective and forward-looking research of IP data, technical field analysis, and competitor analysis.

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Intellectual property management system using big data for efficient and automated intellectual property (IP) mining and maintenance. The system includes an IP mining layout unit and an IP maintenance transaction unit that form an IP management portal. The mining unit provides services like IP mining and layout. The system leverages big data techniques to automate IP mining by analyzing and classifying existing IP data, determining innovation points, and identifying technical solutions. This reduces time and manual effort compared to manual classification. The system also automates IP maintenance transactions by facilitating IP transfer and licensing.

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A patent management system that allows parties involved in the patenting process to make decisions at each stage of a patent's life. The system includes a database, a user database, a mapping database, and an analytics database.

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Intellectual property service platform database to improve organization and efficiency of managing intellectual property (IP) information. The database is connected to an IP information input port for direct entry of IP data. It has modules to automatically search for expired IP rights, process unpaid documents, and update IP records. This allows proactive maintenance of the database instead of just storing IP data. The database also provides recommendations for matching IP information based on search queries.

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Patent analytics using MapReduce clustering to efficiently analyze large patent databases and identify patent clusters related to specific products. The method involves querying public patent databases using patent cluster keywords and enterprise databases using product cluster keywords. The patents are clustered using MapReduce and then sorted by criteria like cluster size and product conflict. This allows for finding patents covering a product, missing patents, related patent clusters, and areas to pursue. The sorting helps prioritize and focus on relevant clusters.

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Scalable computing system for managing intellectual property rights and royalty payments using dynamic flow networks. The system receives royalty statements from income sources, matches assets from the statements to database assets, calculates royalties using flow networks representing asset relationships, and processes in parallel for multiple deals. The flow networks allow efficient royalties calculation by dynamically routing units through graphs with capacity and condition constraints.

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Electronically mining intellectual property by using data mining techniques to automatically discover potential IP from enterprise documents. The method involves harvesting company documents and industry trends, identifying relevant trends, and analyzing documents using text mining to find correlations that could lead to new IPs. It aims to quickly identify business-critical intellectual property by associating enterprise processes with emerging technology trends.

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Enhanced methods, techniques, and systems for searching patent-related literature like issued patents, patent applications, and abstracts. The methods involve using machine learning and natural language processing to improve search accuracy and efficiency for tasks like assessing patentability, valuing assets, and finding prior art. It involves training models to understand patent language, identify key concepts, and match them across documents. The models can learn from user feedback and expertise to personalize search results. It aims to overcome the challenges of searching growing patent corpuses with complex language using AI.

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A method for processing data on an intellectual property (IP) transaction platform to facilitate IP sharing and transactions. The method involves building a database with separate tables for IP projects, transactions, matching, information, and member details. The IP projects table includes scientific research, patents, etc. The transactions table covers items like lab capital, IP financing, etc. The supporting library has tables for things like industry directories and science catalogs. This structured database allows efficient processing and analysis of IP data for enabling IP sharing and transactions on the platform.

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A patent management system that provides tools for analyzing patents and patent portfolios to assist in decision making during the patent lifecycle. The tools include prosecution analytics, patent analysis, reference management, prior art analytics, docketing management, claim mapping, claim analytics, portfolio analytics, external database analytics, annuity management, and strategic monitoring. The system retrieves patent claims and compares published and issued versions to identify unique keywords. It then performs keyword analysis on prior art using the unique keywords to potentially determine novelty points.

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Coordinated processing and analysis of large amounts of data to reveal complex interrelationships in a coherent manner, especially in the context of intellectual property analysis and litigation. The system uses a relational database structure to leverage overlapping information between patents and claims. It allows users to analyze and compare patents and claims in a controlled environment with configurable segments linked to claims. The system also provides features like heat maps, concept grouping, and prior art mining. It promotes crowd-sourced analysis with incentives and billing.

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