IP Assessment 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 patent technology disclosure quality evaluation system that combines semantic and trend analysis to quickly and accurately determine the quality status of patent technology disclosure documents. The system uses a semantic analysis module to initially screen out obviously poor quality disclosures. It then applies a trend analysis module to assess the quality of the remaining disclosures by comparing their content over time. The trend analysis involves calculating test statistics to determine if the disclosure content is becoming increasingly divergent from a benchmark set. If so, the disclosure quality is deteriorating. This allows timely tracking and improvement of disclosure quality.

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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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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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Enhancing patent searching and analysis using AI, specifically natural language understanding (NLU) and machine learning (ML) techniques, to improve accuracy and avoid missing relevant patents. This method involves analyzing patent claims and relevant text from reference documents using AI to understand the meaning of claim elements. The AI system then searches for matches and ranks patents based on how well their claims align with the reference document, ensuring more precise and comprehensive search results.

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A decentralized platform for evaluating and managing patent risk using blockchain technology. This platform offers customized insurance policies to cover the risk of patent invalidation. It employs data analysis to assess the likelihood of patents being challenged, adjusting policy premiums and limits based on this evaluation. Users can monitor claims and receive payouts if their patents are invalidated. Additionally, the platform supports crowdfunding for patent applications and utilizes blockchain tokens for transactions and rewards, enhancing transparency and security.

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Method for pre-evaluating patent quality using automated checks to provide quick and accurate assessment of patent quality before formal review. The method involves duplicate checking the text against databases to determine deduction points for repetition, and formal checking against preset question types to determine deduction points for formality. Combining these checks provides an overall quality assessment with deduction points and a risk level.

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Comparing legal clauses using deep learning to simplify and automate the analysis of legal documents. The comparison involves generating hidden Markov chains for the clauses, comparing corresponding nodes to identify differences, and flagging them if they exceed a threshold.

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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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Patent drafting quality check system using natural language algorithms to improve the quality of patent applications. The system reviews and evaluates patent drafts for issues like fluency, typos, and deduplication. It uses a language model trained on patents to analyze drafts and generate a report with suggestions for improvement. The system can also prioritize draft acquisition methods based on historical review scores.

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A comprehensive technology competency evaluation system for companies based on their patent portfolios. The system evaluates both quantitative (number of patents) and qualitative (quality of patents) aspects of a company's technology capabilities. It collects all patents owned by companies, evaluates them using an individual patent database, calculates scores for each company's patents, averages and ranks them, then combines quantitative and qualitative scores to provide a comprehensive evaluation. This allows comparisons of a company's technological capabilities against all companies and against similar companies in the same industry.

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Patent valuation and operation strategy using a multi-dimensional evaluation system that provides a more accurate and comprehensive valuation of patents compared to traditional methods. The multi-dimensional evaluation considers factors like laws, technology, market demand, etc. to provide a detailed analysis of patent value. This allows better decision making for patent management, transfer, and monetization.

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Big data-based patent analysis service that provides more reliable and accurate patent valuation and analysis results. The service uses big data processing techniques to analyze patent, company, technology, and researcher information. It generates reports like company analysis, interest company analysis, and interest technology analysis based on input company/technology data. The reports are then used along with patent data to evaluate patent value. The service leverages big data to extract insights from large volumes of patent and related data to improve the reliability and accuracy of patent analysis and valuation.

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A method to accurately evaluate the innovation capabilities of scientific and technological enterprises by leveraging their intellectual property (IP) portfolios. The method involves calculating an IP evaluation value based on the enterprise's IP data, including both licensed and unlicensed IP. This IP evaluation value is then used to determine the net evaluation value, which represents the enterprise's innovation capability. By focusing on IP, it provides a more objective and comprehensive evaluation compared to traditional financial metrics.

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Method for accurately evaluating the innovation stability of scientific and technological enterprises by leveraging their intellectual property portfolios. The method involves obtaining the intellectual property information associated with the enterprise, determining the total amount of innovation in each cycle based on the number and weight of IP types, and then evaluating the innovation stability based on the staged stability of each IP type. This allows evaluating innovation stability more accurately by directly assessing the enterprise's IP generation over time rather than relying solely on financial data.

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A system for evaluating the technical value of intellectual property rights that provides a more reliable and objective way to calculate the value of intangible assets like patents. The system uses data from intellectual property and financial statement databases to evaluate factors like technological contribution, business feasibility, and industry discount rates to determine the value of the intellectual property. It involves steps like estimating the industrial importance of the technology, evaluating the degree of technological contribution, calculating discount rates and annual free cash flow, and finally determining the technology value.

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An intellectual property (IP) evaluation system that provides a comprehensive IP value analysis beyond just financial performance. The system collects IP data, classifies it, analyzes and evaluates it, and scores IP value based on the analysis. It uses statistical classification algorithms to classify and store IP file data. Retrieval, comparison, analysis, evaluation, and litigation status are done using the classified data. The system then scores IP value based on the analysis.

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Analyzing intellectual property (IP) data related to products and services to provide IP-related services. The method involves using natural language processing (NLP) and machine learning (ML) techniques to analyze IP documents and product/service data. It extracts linguistic structures from IP assets and products/services. It maps IP assets and products/services to technology categories. By comparing linguistic structures and category mappings, it determines IP assets related to products/services. This enables services like valuing IP, identifying infringement, and finding products covered by IP.

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A patent evaluation method and system using natural language processing and complex network algorithms to objectively evaluate the value of patents. The method involves collecting patents, generating technology points and clusters based on patent text, and evaluating patents within each cluster. This allows assessing the depth and breadth of patented technology, predicting patent lifespan, and comparing patents to global industry tech.

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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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