Patent Value Assessment Framework

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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