IP Asset Valuation in Technology Licensing

Computerized system for evaluating patent claim inputs and authorized contributions to a project. The system tracks inputs, outputs, prompts, queries, responses, and collaborations. It analyzes and evaluates metrics like creativity, novelty, utility, reliability, success, value, rights, and IP. The system uses AI to persistently monitor, parse, and analyze inputs/outputs/prompts for determining metrics. It provides data, scores, graphs, and statistics for measurably improving SWOT and ROI.

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Traditional intellectual property transaction traceability has problems such as information asymmetry, storage methods relying on centralized databases, and easy tampering of information, etc. A blockchain-based security mechanism for is proposed. Firstly, through the analysis massive case commonality individuality data are studied, structure scope collection requirements established; secondly, constructed based smart contract PROV origin model, signature verification completed BLS threshold Dynamic DKG protocol, process integrates model constructs a chained structure. The multi-level strategy developed to achieve protection throughout entire life cycle transactions.

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A platform for monetizing content creation through securitization and trading of content catalogs. The platform allows content creators to sell portions or all of their content catalogs and revenue streams to investors. It automates valuation of back catalogs and future content using machine learning to provide fair pricing. The system also has a sunset fund where secondary trading fees are retained and paid out to investors when the content revenue stops.

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Similar patent document retrieval is an essential task that reduces the scope of claimants searches, and numerous studies have attempted to provide automated search services. Recently, Retrieval-Augmented Generation (RAG) based on generative language models has emerged as excellent method for accessing utilizing knowledge environments. RAG-based services offer enhanced ranking performance AI by providing similar queries. However, achieving optimal similarity-based in remains a challenging task, methods similarity do not adequately address characteristics documents. Unlike general retrieval, documents must take into account prior art relationships. To this issue, we propose PAI-NET, deep neural network computing similarities incorporating expert We demonstrate our proposed outperforms current state-of-the-art classification tasks through semantic distance evaluation USPD KPRIS datasets. PAI-NET presents candidates, demonstrating superior improvement 15% over methods.

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Analyzing intellectual property (IP) portfolios of entities to provide insights and visualizations. The method involves associating financial metrics with IP assets, identifying similar IP assets based on technology areas, determining average financial metrics for similar IP assets, and generating visual representations of those metrics. This allows efficient comparison of financial performance of IP assets and entities without manually reviewing large numbers of documents.

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Digital asset valuation system that more accurately assesses the value of digital assets by considering multiple factors beyond just price. The system acquires digital asset data, determines weights for factors like usage, adoption, technology, and market conditions, builds a multi-factor model using these weights, and uses it to calculate more realistic asset values. The system aims to provide a more comprehensive and less manipulable valuation compared to models solely based on price.

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Quantifying the value of a patent using a multiple regression model to build a reliable patent valuation model that reflects structural characteristics of specifications and accurately values patents. The regression model is built by processing patent information and performing multiple regression analyses with key valuation elements as dependent variables. The representative regression coefficients for each independent variable across the analyses are used to generate valuation models for each valuation index.

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Evaluating the value of intellectual property, such as patents, using an index based on relative document strength rather than absolute values. The method involves acquiring a group of documents related to a patent, evaluating each document's value based on parameters and patenting weights, and using those values as an index of the patent's relative strength within the document group.

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A decentralized blockchain-based platform to register, transfer, license, apply for, and value IP assets, such as patents, using smart contracts. The blockchain provides transparency and security while the smart contracts automate processes like patent transfers.

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Collateralizing intellectual property (IP) assets like patents and trademarks for loans and insurance. It involves using model-based analysis to accurately value and assess risk of IP collateral. This enables lenders, insurers, and rating agencies to more confidently collateralize IP loans and insure against IP default. The models analyze factors like patent strength, infringement potential, litigation history, and market demand to provide quantitative assessments of IP value and risk.

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Method for evaluating the transaction value of data assets to quantify and measure the value of intangible data assets for better pricing and monetization in data asset transactions. The method involves building a data asset transaction value evaluation model that assesses the cost of obtaining the data assets and their application value. This model allows more accurate estimation of the transaction value of data assets by quantifying their application worth in specific contexts.

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Semi-automated valuation of patents using machine learning that leverages a database of information layers like megatrends, market indicators, financing figures, etc to provide a more holistic and dynamic valuation of patents. The system builds a machine learning model comparing patent info to refine valuations with user input and forecast changes as relevant information layers change over time.

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A patent search and analysis tool that provides a quality score for evaluating patents. The tool uses AI, machine learning, and data mining to analyze multiple factors correlated with patent value. This includes the patent's citation network and the value assigned to it by the patent owner. By performing a random walk on this weighted network, the tool calculates a "Qscore" to rank the commercial potential of patents.

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A user-centric system to carry out the valuation and transaction of patents. The system uses a standardized set of parameters to provide an objective and representative valuation of patents. The parameters include applicant and inventor experience, technology applicability, risk factors, and a dynamic database that allows rapid updating of patent values.

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Analyzing intangible assets like intellectual property to provide meaningful information for organizations. The analysis involves qualitative and quantitative techniques using frameworks. Qualitative analysis examines factors like coverage, opportunity, and risk. Quantitative analysis determines monetary values. The frameworks have components like strength of claims, breadth, replacement cost, market value, and revenue. The analysis is customized for different types of intangibles like patents and trade secrets.

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A method and system for determining the value of an enterprise or intangible assets using risk rating. The method involves assessing the risk rating of the subject matter based on financial data. Multiple valuation formulas are then applied to the financial data to calculate multiple estimates. The estimates are sorted based on numerical value and selected proportions are determined using the risk rating. The selected estimates are then further screened based on market characteristics to determine the final value.

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Evaluation of the exclusive power of a patent by quantifying the extent to which the patent obstructs third-party businesses. The calculation apparatus extracts legal procedure data from patent histories to determine how obstructive the patent was to third parties and then calculates a post-obsolescence cost based on the filing date. The total post-obsolescence costs across all extracted patents provide an evaluation of patent power.

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A decentralized network using blockchain to provide a transparent and efficient platform for managing intellectual property rights. It leverages blockchain's features like smart contracts, secure decentralized storage, and transparent transactions to improve the patent ecosystem with features like simplified patent registration, transparent ownership, valuation tools, automated examination, and streamlined licensing and transfer processes.

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Decentralized method and system for independently valuing intellectual property rights using a blockchain network and AI. The system collects historical transaction data for similar IP assets, extracts parameters like remuneration, development speed, enforceability, etc, and uses AI to analyze and predict IP valuations. Smart contracts on the blockchain enable secure, decentralized IP transfers and royalty payments. The system aims to provide a more accurate, transparent, and efficient way to value and monetize intangible assets compared to traditional methods.

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Method for accurately determining the monetary value of a patent based on factors like forward and backward patent citations, legal status, patent family, and commercial data of the patent holder. This provides a comprehensive evaluation that considers technical, legal, and market aspects. The method involves obtaining patent information, commercial data of the patent holder, and technology market details. It then evaluates the patent's value based on factors like citations, status, and breadth. The evaluation can be done using a computer program that analyzes the data.

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