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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Streamlining the patent application drafting process using artificial intelligence (AI) to analyze user input, prior art, and patentability guidelines to automatically generate patent applications. The AI learns the behavior of patentable inventions by analyzing existing patents and guidelines. It then uses that knowledge to draft patent applications based on user input and prior art.

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Patent evaluation system that provides more precise and realistic evaluation of patents by applying qualitative evaluation criteria to complement the quantitative evaluation methods used in conventional systems. The system subdivides patent evaluation items, adds precision evaluation items, and assigns differential weights to give qualitative evaluation for each item. It also sets different weights based on factors like cited subject type, family country set, annual fee payment, and product application. This allows more realistic and precise evaluation compared to uniform quantitative methods.

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Patent evaluation method and system that goes beyond traditional patentability, technicality, and marketability analysis to also consider media exposure. The system obtains patent information, extracts keywords, finds similar patents, calculates indices, applies weights, and generates a numerical value representing patent evaluation. The media exposure factor is incorporated by leveraging a media server to gather information on patent-related news and publications.

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Patent novelty analysis system to determine if a patent application is novel. The system compares the technical features of a new patent application with existing patents using a step-by-step analysis process. It involves extracting clauses from patent documents, analyzing their syntactic dependencies, constructing concept maps, comparing concept maps, and calculating overall similarity. This method provides a reliable way to compare and analyze patents for novelty without manual search and analysis.

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Automatic scoring of intellectual property rights like patents using a method, system and equipment that enables objective and systematic assessment of patent quality and value. The scoring is based on parameters like legal status, citations, age, classification, claims, words, etc. A patent is scored relative to a patent group. This provides insights into patent characteristics and inferences about future value. The scoring can be customized by modifying parameter weights. The system allows users to select score categories or create their own.

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A patent evaluation method using structural equation modeling to accurately assess the value of patents. The method involves building evaluation models for factors like rightness, technicality, and usability using a structural equation approach. Experts rate the importance of factors, and reliability and validity are verified. Impact indexes are determined for each evaluation factor. Patents are evaluated using the models and factors. The approach allows more reliable patent evaluation by considering interdependencies between factors.

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An apparatus and method for evaluating patent power using patent history data. The invention extracts legal procedure data from patent histories and calculates a post-obsolescence cost based on the procedures performed by third parties like requests for inspection or invalidation trials. The extracted procedures are matched to costs in a table. Post-obsolescence costs are calculated based on the filing date, procedure dates, and obsolescence functions for the technical field. Totaling the costs provides an evaluation of the patent's power.

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System for performing patentability searches and analysis using machine learning and pre-filing analysis to improve patent quality and success rates for inventors. The system allows users to submit concept descriptions, claim terms, or draft patent applications for analysis. It conducts searches of patent and non-patent literature databases, identifies relevant prior art, and provides results to the user. The system also identifies the likely patent examiner and art unit for the application and considers frequently cited references.

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A system for quickly assessing the creativity of patents using standard benchmarks. The system takes in patent documents and extracts technical details. It converts these into a format matching a creative level benchmark. It then compares the converted patent against the benchmark to determine the creativity level. This provides a rapid and reliable way to assess patent creativity without manual analysis or long delays.

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A method for assessing the value of patents that is more objective and quantitative than traditional methods. The method involves a comprehensive evaluation of patent legal status, inventor qualifications, patent area, specification content, and other factors. It uses a disaggregated, three-dimensional evaluation approach that considers multiple aspects of the patent rather than just financial or market value. The evaluation involves steps like speculating patent reliability, assessing inventor maturity, grading patent content, and considering patent area. This binds the evaluation to the actual situation of the patent and makes it more detailed and operable.

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A method and device for evaluating patent rights, patent applications, and inventors in a quantitative and flexible way. The method involves calculating evaluation values for technical merit, legal strength, and overall value of patents and patent applications. These evaluation values are derived using technical and legal indicators like citations, complexity, and clarity. The device acquires patent document data, calculates the evaluation values, and uses them to rank and rate patents, patent applications, and inventors. It provides a quantitative and flexible way to evaluate patent rights beyond just financial methods.

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A system for objectively evaluating the technical value of patents using machine learning and database searches. The system receives user-specified inventive solutions and expands them using machine learning to generate extended solutions. It then searches a database of patents to find matching extended solutions and weights them based on relevance. This weighted list is used to evaluate the technical value of the original invention compared to the core patent. The system can also extract relevant technologies from input patents and search for matching expanded solutions to evaluate relative value. The goal is to provide more objective patent value assessments by expanding and comparing inventions instead of relying solely on subjective evaluation.

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A method for objectively evaluating the technical value of a patent. It uses machine learning to compare the patent's solution with similar patents and extracts the relationship between the patent and key patents. It calculates the range of products the patent's technique can apply to. By analyzing the technical maturity and expansion potential, it evaluates the patent's technical value relative to key patents.

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Automated patentability determination system for inventive ideas that provides a structured and quantifiable process for assessing patentability. The system receives inventive ideas from inventors and breaks them down into granular functionalities. It then compares the functionalities to prior art and scores them for importance and match. The scores are combined into an overall patentability index. This provides a consistent and quantifiable assessment of patentability that can be used to decide whether to pursue patent protection.

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