Patentability Evaluation in IP Automation

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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A decentralized blockchain system for granting smart contracts that provides an alternative to centralized patent examination systems. The system uses a distributed network of validators to review and grant smart contracts granting patent-like rights. Validators are incentivized to participate by receiving tokens when they successfully validate a contract.

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Improved computer system and method 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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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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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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A system to objectively evaluate and categorize innovation content to minimize subjectivity and risk in innovation analysis. The system has two subsystems: one to assess the scope of innovation based on geographic novelty levels, and another to categorize based on factors like innovation degree, activity nature, engagement, and barriers.

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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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Automatically classifying patents via machine learning to save time and resources. The method involves learning from a patent database to establish a basic classification model. When a user searches for patents, it uses their classification input to predict their personalized classification standard. This combines with the basic model to classify the remaining unclassified patents. It leverages transfer learning to customize the basic model using the user's classification pattern.

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Data-driven method to efficiently screen a large number of patent documents for relevancy to a target subject matter using a triage approach. The method involves mapping key concepts from claims of the identified patent pool to quickly screen and rule out irrelevant patents. The concepts marked as "definitely not in target subject matter" are used to eliminate patents without manually reviewing each one.

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Automatically generating patent applications with improved patentability using AI and prior art. It takes invention text, determines its patent classification, finds similar patents, extracts details not in the original text, and adds those to the invention description. This expands the invention scope and increases patentability.

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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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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 patent map display device and method that dynamically generates a visual map representing patents and their attributes to help users navigate and understand patent landscapes. The system compares user-inputted instruction information with patent data to evaluate patents. It then generates a map with characters representing evaluated patents and displays it. The characters can move based on analysis results, have different appearances based on attribute totals, and be grouped based on classifications. The map allows easy visualization of patent landscapes and attributes.

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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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A system and method for objectively assessing the risk of patent infringement in technological achievement transformation or patent transactions. The system uses a set of analysis and evaluation indicators and weights to determine the infringement risk. It helps parties in patent transactions or tech transfer evaluate potential infringement issues and mitigate risks.

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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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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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Patent evaluation system that determines patentability of an invention idea. The system uses an automated process to compare an invention sentence with existing patent sentences using an edit distance measure. The comparison looks for similar sentences in a patent sentence database. The determination of patentability is based on multiplying the reciprocal of the edit distance with the length index of the invention sentence and a content rate of the keyword.

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Identifying a value gap in a patent portfolio strategy of an organization to measure the misalignment between the organization's patent strategy and the universal market focus and quantify the business impact. The method involves comparing the organization's internal ranking of focus areas with a universal ranking based on factors like market size, potential, and trends. The difference between the rankings represents the value gap score, indicating the penalty for misaligned strategy.

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Streamlining the patent application drafting process by using artificial intelligence (AI) to automatically generate patent applications. The system receives information about an invention and prior art, analyzes it, determines patentability, and generates the application sections. The AI learns user drafting style over time for personalized output, and can also be trained on drafting guidelines.

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