Patent Value Assessment Framework

Patent mapping, ranking, and rating system that allows quick analysis and visualization of patent portfolios to determine relevance and value. The system categorizes patents by concepts like scope and technology categories. It maps concepts to claims, ranks concepts, and generates charts showing relationships between ranked concepts and claims. This allows users to quickly see which concepts provide good coverage and value in a portfolio.

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A graph-based system for executing and analyzing smart contracts that overcomes limitations of traditional smart contracts like lack of reusability and difficulty in determining outcomes. The system uses a symbolic AI model that represents smart contracts as directed graphs with categorized vertices. This allows extracting features, scores, and conditional statements to simulate contract evolution and outcome prediction. It also enables analyzing and comparing contracts across domains by finding intermediary entities and quantifying relationships. The graph-based representation and analysis provide more efficient, accurate, and systematic handling of smart contracts with complex conditions, obligations, and information asymmetry.

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Tracking the provenance of inputs and outputs of machine learning models using blockchain to provide accurate valuation and ownership attribution. The method involves storing the inputs, data sources, prompts, and responses of a model on the blockchain as it is developed. This creates an auditable trail linking the inputs to the outputs. It also allows tracking of model use and generating estimates of model value based on frequency of use.

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Protecting the security of data used in machine learning while accurately determining the value of contributed data. The method involves training multiple machine learning models using subsets of provided datasets. The models are run on input data and the results compared to ground truth. A model is chosen based on accuracy and the value of the corresponding dataset is determined. This value is encapsulated in a smart contract along with the dataset and recorded on a blockchain. It provides a way to track and reward the value of contributed data while protecting its confidentiality.

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Patent management system with tools to help analyze, organize, and search patent portfolios. The system allows quick and relevant patent analysis using features like claim mapping, concept organization, similarity indexing, and expanded search results. It provides tools to efficiently manage and analyze patent portfolios by leveraging automated techniques like mining, mapping, and indexing to extract insights from patent claims and texts. The system also enables expanded search results, concept organization, and claim mapping to help with quick and relevant patent analysis.

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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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AI-driven asset analysis and personalized value assessment system that overcomes limitations of traditional asset search and valuation tools. The system uses AI techniques like computer vision, natural language processing, and specialized neural networks to analyze multi-modal asset data like images, videos, text, and audio. This provides a deeper understanding of asset features and more accurate value predictions tailored to individual user profiles. The system also incorporates environmental, contextual, and personalization factors to optimize asset discovery, selection, and identification.

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An intellectual property landscaping platform that uses machine learning to analyze and visualize patent portfolios. The platform clusters related patents based on technical aspects using user-seeded searches. It generates refined clusters of IP assets using seed searches in varying areas of interest like target fields, products, or competitors. The platform also calculates metrics like coverage, opportunity, and exposure for each cluster. The clusters are visually represented in an interactive map to provide an efficient and accurate way to analyze IP landscapes with large portfolios.

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Artificial intelligence (AI) system for analyzing patent documents to provide insights into technology development trends, competitive intelligence, and patent analysis. The system uses AI techniques like semantic analysis and relationship analysis to automatically identify the most critical patents and documents in a collection, determine what they reveal, and provide summaries and visualizations. It consolidates metadata like citations, litigation, and expiration dates to provide statistics like citation indices and influence factors. The AI also generates alerts, flags, and indicators based on patent quality, relevance, and expiration. The system aims to automate tasks like competitive analysis, prior art search, patentability assessment, and freedom to operate analysis using AI instead of manual review.

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A platform for analyzing intellectual property portfolios of entities by identifying similarities between portfolios and clustering IP assets based on technical aspects. The platform allows users to seed searches based on technical fields, competitor portfolios, etc. to find IP assets similar to their own. It then clusters the assets at varying levels of granularity and generates visual representations of the clusters. This helps users efficiently analyze and compare portfolios, identify gaps and saturation, and assess exposure.

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Automated analysis of patent claims to help evaluate relative breadth, identify corresponding products, and find related patents. The system analyzes claims using techniques like stemming and normalization to extract features like unique word counts. It generates claim profiles based on these features and compares them to determine relative breadth. It also searches for products and related patents based on identified elements in the claims.

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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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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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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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Early evaluation method for assessing the potential value of patents from scientific research institutions and public welfare organizations. The method involves using a deep learning model to analyze patent texts and indices to predict the patent value. It addresses limitations of previous methods by using a more complex model to extract semantic features from patent texts, modeling patent indices, and combining citation counts and transfer status to provide a more comprehensive evaluation of patent value.

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A patent value evaluation method and system based on big data analysis that aims to efficiently evaluate patent value using public information rather than detailed analysis of patents. The method involves querying publicly available patent data, splitting it into indicators, and evaluating each indicator using patent value algorithms. This allows faster and less resource-intensive patent valuation compared to detailed analysis of patent technical content.

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A method to objectively assess patents using AI. The method involves obtaining information about the target patent and the corporate that owns it, generating input signals from the obtained data, feeding these signals into pre-trained neural networks to classify the patent and corporate, comparing the network outputs to stored comparison values to assess the patent and corporate, and providing the assessment results.

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Semi-automated determination of patent valuation using machine learning and dynamic representations. The method involves generating a database containing layers of information like megatrends, indicators, ontology, codes, devices, etc. from various sources. This dynamic representation of patents is used as input for a machine learning model to predict patent valuation. The model optimizes using historical and present structured/unstructured data and expert knowledge.

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A mathematical model for evaluating the value of patents using a multi-perspective approach that considers factors like market demand, technological innovation, strategic importance, and economic potential. The model involves calculating scores for each perspective based on specific criteria, then combining them to arrive at a overall patent value. The scores are derived from factors like market demand, degree of improvement, technological monopoly, strategic importance, and economic potential. The model aims to provide a more comprehensive and accurate evaluation of patent value compared to traditional methods.

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Objective and rational calculation of technology value for each technology field using an adjustment index that reflects the inefficiency of each patent classification code. The method involves calculating patent strength based on factors like R&D expenses, prior/succeeding patents, family patents, and claims. It then calculates technology contribution by substituting patent strength and industrial importance. This allows more accurate evaluation of technology value compared to subjective scoring of individual technology strengths. The method uses patent data, financial statements, and an adjustment index based on patent classification inefficiency.

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A method for evaluating the value of patents using big data analysis rather than expert scoring. The method involves constructing a patent value evaluation model by selecting parameters that affect patent value, choosing patent data samples based on evaluation criteria, and establishing a formula reflecting the relationship between the selected parameters and patent value. This allows objective and accurate patent value evaluation models to be generated in a targeted way, improving the accuracy of patent value evaluation compared to expert scoring.

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Patent value evaluation method and system that provides a more objective and reliable patent valuation process compared to existing methods. The method involves a step-by-step approach using secondary indexes and the analytic hierarchy process (AHP) to assign weights to the indexes. This reduces subjectivity and improves reliability compared to single weighting methods. The steps include: 1. Identify secondary indexes related to legal, economic, and technological aspects of patents. 2. Assign weights to the secondary indexes using AHP, where weights are calculated independently for each level (legal, economic, technological) and combined to get final weights. 3. Calculate patent value using the weighted secondary indexes. 4. Evaluate patents using the weighted secondary indexes and calculated values. The evaluation system uses the method described above to evaluate patent value.

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Patent evaluation method that leverages natural language processing and complex network algorithms to objectively evaluate the depth and breadth of patented technologies and predict the expected life of patents. The method involves collecting patent documents, generating technical points, clustering patents into global industries, and comparing patents with technologies in the global industry to evaluate their value.

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A method to accurately evaluate the value of patents using deep learning techniques that combine patent text, attributes, and citation relationships. The method involves training a neural network model using patent data to learn features that correlate with patent value. The model takes as input patent text, attributes like inventor and assignee, and citation information. It outputs a predicted patent value. The trained model can then be used to evaluate the value of new patents based on their text, attributes, and citation data.

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Online patent value evaluation method and system using machine learning models to accurately assess the value of patents. The method involves constructing a patent value evaluation index system with network platform characteristics, and proposing a patent value prediction model based on gray correlation analysis-random forest regression (GCA-RFR). This allows uploading patent data to the system, preprocessing it, and running the GCA-RFR model to generate a patent value report. The system provides a library of value evaluation models and allows users to upload their own models for flexible expansion.

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Method for evaluating the value and price of patents that involves a step-by-step process to accurately assess patent worth and price. It uses a combination of patent analysis techniques and machine learning to account for factors like patent quality, infringement potential, technical background, and scope. The method involves obtaining patent information, preprocessing it, analyzing in-depth, determining grade, evaluating value, evaluating price, and calibrating a patent transaction model using real data.

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A method and system for evaluating patent value based on technical analysis of the patented technology and the industry it's in. The evaluation involves obtaining patent information, analyzing the patented technology and industry tech, calculating technical and application value weights, and then combining them to determine the patent's value in the industry.

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Method for evaluating patent value using big data and deep learning to accurately determine the technical worth of patents. The method involves a paradigm database, a comparison database, an operating terminal, and a computing server. The server evaluates patents by classifying the technology, comparing the text against prior art, determining patent age, and assessing series patents. It extracts text segments using paradigms, compares logical relationships, and assigns values. This multidimensional evaluation provides a more comprehensive and objective assessment compared to manual methods.

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A system for mapping, ranking, and visualizing patents and patent portfolios to analyze patent value, coverage, and related concepts. It uses data structures and analytics to organize patents by concepts like scope, technology category, and ranking. This allows interactive charts to be generated showing relationships between patent claims, concepts, rankings, and metadata like filing date and owner. The system also enables features like automated prior art citation management within portfolios.

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An intelligent method to evaluate the value of patent transactions using machine learning. The method involves quantifying factors that affect patent value like problem solved, technical means, industry trends, supporting tech, etc. These factors are vectorized and used to train a patent value prediction model. The model can then be used to estimate the value of a given patent based on its factor vector. This provides a more objective and reliable alternative to expert evaluation. The model can be trained using a database of patent value pairs and refined through industry-specific sub-databases.

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A method and system for evaluating the value of patented technology that goes beyond just economic indicators to assess quality, technology, and risk. The method involves evaluating factors like patent strength, technology maturity, market potential, legal stability, cooperation feasibility, and intellectual property protection. The system uses a client and server to input patent details, evaluate the factors, and output an overall value score.

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A patent analysis system that calculates scores to determine the quality and value of patents in a way that is both automated and consistent across a large patent portfolio. The system calculates scores for individual claim elements and overall patents and then compares those scores against peer patents to provide rankings.

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Calculating a corporate value index based on patent scores that considers industry-specific patent value trends. The index calculation involves first calculating a patent score for each patent. Then, some patents are extracted based on a predetermined order using the patent scores. Finally, the patent scores of the extracted patents are totaled for each company to get an index value indicating the corporate value of each company. The extraction process is weighted based on the patent score trend in each industry to account for differences in patent value between industries.

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A technology value evaluation system using patent parameters to objectively and reasonably calculate the degree of technical contribution and evaluate the technology value. The system calculates patent strength based on factors like number of preceding and succeeding patents, family patents, claims, targets, and application fields. It then uses this patent strength along with industry and financial data to calculate the technology contribution, business value, and overall technology value.

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A standardized, objective method to evaluate the value of a patent. The method involves extracting evaluation items from patent details, calculating scores for each item using predefined coefficients, inputting the items and scores into a customizable evaluation model, and analyzing the patent value. The coefficients can be modified to tailor the evaluation for different needs.

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Efficiently evaluating the value of patents by leveraging the analytic hierarchy process (AHP) and a library of patent element features. The method involves comparing features of a patent against preset features to build matrices. Consistency checks are done. Weights are determined based on matrix results. These weights are applied to calculate a target value score for the patent using feature scores. The library provides a structured approach to evaluating patents that improves efficiency over manual analysis.

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