Patent Value Assessment Framework

Patent management system that provides quick claim scope determination and relevancy assessment for patent portfolios. The system uses automated tools to search, map, analyze, and chart patent claims to help quickly understand and compare patent scope. It enables quick claim relevance assessment by generating expanded sets of search results, mapping claims to concepts, highlighting terms, and displaying charts. This allows rapid and accurate assessment of patent relevance and distinction compared to prior art.

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Sorting search results to improve relevance and value while also pushing relevant results. The method involves rating search results based on their type and style, then reordering the results using the ratings. This allows pushing relevant results with higher scores higher in the sorted list. The ratings are determined using a mapping of scores based on vertical class labels and historical search data. This improves relevance and value of the sorted results.

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A patent value evaluation method that combines traditional methods and machine learning to provide more accurate and reliable evaluation results for patents. It captures patent information and potential citation relationships through network data retrieval, determines stage-based weights, uses association rule mining, self-learning, and feedback to classify and value patents based on correlation matrices. This provides more comprehensive, dynamic, and real-time patent value evaluation.

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Improving the accuracy of determining patent value and generating technical routes using a comprehensive evaluation method. The method involves assessing patent value based on factors beyond just the patent document itself. It considers the patent applicant's ability, inventor info, inherent attributes, and awards. Coefficients are used to weigh these factors. This provides a more accurate patent value compared to just the patent document. The technical route generation uses patent application dates to improve accuracy compared to just searching.

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Method for evaluating the value of a patent based on claim analysis. The method involves extracting components from the patent claims, finding documents with matching configurations, calculating frequencies of prior and subsequent documents, and scoring the patent based on weights for these frequencies. The weights can favor subsequent documents over prior ones. The scoring also considers vector distances between components, component frequencies, technical field concentration, applicant type, and trend.

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Patent value assessment method, system and device using a quantitative model to determine the economic value of a patent. The method involves collecting patent data, building a database, and establishing a patent value evaluation model using machine learning techniques. The model quantitatively calculates the market value, legal value, and technical value of a patent based on indicators like practicality, versatility, advancement, and awards.

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A comprehensive method for evaluating patent value that considers multiple factors like legal, technical, and market dimensions. The method involves a multi-step process to quantitatively evaluate indicators using a Delphi technique. It involves experts making accurate judgments on indicators, converting language levels to fuzzy numbers, averaging the fuzzy numbers to construct indexes, and calculating patent value based on the indexes. This provides a more objective and accurate patent value evaluation compared to subjective methods like AHP.

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Objective and rapid patent value evaluation system that can avoid subjective judgments by using a classification number-industry comparison table. The system receives patent files and queries the table using the patent classification number to find the industry category. It then retrieves the output value and total number of valid patents for that industry. By comparing the patent's output value to the industry average, it objectively evaluates the patent's value.

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A method and device to accurately evaluate patent value using objective parameters instead of manual determination. The method involves analyzing objective factors like collections, awards, and ages to evaluate patents. It uses weighted evaluation indices and initial values to determine scores. For collections, it calculates median scores of patents in a set. The scores are then used to find the patent set value. This reduces manual error and improves accuracy compared to subjective evaluation.

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Automated patent valuation method using existing financial and patent data to provide standardized and objective patent valuations. The method involves extracting patent and financial data from multiple sources, applying a uniform qualitative assessment standard to all patents, and using a relief-from-royalty valuation model. This automated approach aims to bring transparency and standardization to patent valuation by minimizing subjectivity compared to manual income-based valuations.

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Method to assess the quality of patents and provide a reference for evaluating a company's innovation capabilities based on the patents they've applied for. The method involves calculating a patent score using a knowledge dispersion metric derived from the patent's IPC classification numbers. A lower concentration of classification numbers indicates more diverse knowledge, which is associated with higher patent scores. By comparing the patent scores to an evaluation benchmark, the quality of the patents can be assessed. This provides a more effective reference for judging a company's innovation abilities compared to just counting patent applications.

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Method for processing enterprise patent data to evaluate intellectual property strength and innovation capabilities. The method involves acquiring patent data for an enterprise, standardizing the data to make it dimensionless, substituting into a pre-trained weight model, and calculating innovation scores based on the standardized patent data. The weight model distributes weights to patent types based on the standardized data. This allows objective and comprehensive evaluation of enterprise intellectual property using patent data.

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A biopharmaceutical patent value evaluation method, system, and computer system that provides a more accurate and industry-specific way to assess the value of biopharmaceutical patents. The method involves establishing a patent value evaluation index system tailored to the biopharmaceutical industry using factors like technical dimensions, legal dimensions, and economic dimensions. It then extracts patent data features using an autoencoder and applies spectral clustering to build a biopharmaceutical patent value evaluation model. This enables more accurate evaluation of biopharmaceutical patents compared to generic patent value methods.

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Dynamic assessment of patent value using deep learning to provide a more comprehensive and accurate assessment compared to static methods. The dynamic evaluation takes into account the time factor as patent value changes with technology development. The method involves: basic value assessment, dynamic value assessment, building a deep learning model, and continuous optimization. The model learns from patent evaluations and records to improve accuracy.

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Improved computer systems and methods for identifying, evaluating, and protecting inventions that arise from research and development projects. The system uses machine learning and artificial intelligence to score and track aspects of the invention disclosure and patent filing process. It identifies and tracks invention types, problems, solutions, embodiments, portfolios, challenges, concepts, and decisions. The system also scores the quality of patent prosecution and analyzes the likelihood of patent issuance.

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A patent value evaluation method that uses patent indexes and weighting techniques to accurately evaluate patent value. The method involves obtaining patent indexes from legal, technical, and economic dimensions, weighting them using supervised and unsupervised methods, and combining the weights to calculate a comprehensive value score. The weights are further weighted to converge the results. Finally, a value model is used to determine the commercial value. This comprehensive and converged weighting improves accuracy compared to subjective or random weighting methods.

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More accurate patent searching and analysis using AI, specifically natural language understanding (NLU) and machine learning (ML) techniques, to improve search results and avoid missing relevant patents. The method involves analyzing patent claims and relevant text from a reference document using AI systems to determine the meaning of claim elements and search for matches to rank patents based on how well their claims match the reference document.

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A decentralized platform for evaluating and managing patent risk using blockchain technology. The platform provides customized insurance policies to cover the risk of patents being invalidated. It uses data analysis to evaluate the likelihood of patents being challenged and adjusts policy premiums and limits accordingly. Users can monitor claims and collect payouts if their patents are invalidated. The platform also allows crowdfunding of patent applications and uses blockchain tokens for transactions and rewards.

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A patent value evaluation model that uses data standardization and analytic hierarchy process (AHP) to objectively evaluate patent value. The model involves converting index values to standardized scores, combining them using AHP, and aggregating the results to determine overall patent value. The standardization step accounts for differences in index units and scales them to a common range. This allows comparing and combining indexes with varying numerical values. The AHP technique involves pairwise comparisons of index importance to calculate weights. These weights are then used to combine the standardized index scores.

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Method for accurately and objectively evaluating the value of intellectual property assets like patents to improve management and governance of intellectual property portfolios. The method involves calculating the value of each dimension (scope, market, quality) of the patent data, then determining the first patent value type based on the overall value. Next, rating each dimension separately to get the second value type. Finally, comparing the first and second types to determine the final, more accurate patent value type. This multi-step evaluation provides a more objective and accurate determination of patent value compared to just relying on a single evaluator.

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