IP Citation Network Mapping

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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Patent search tool using blockchain and AI to find more relevant prior art. The tool breaks down patent claims into limitations and compares them to patent text, link structures, and classifications to find the most relevant prior art. It uses a network of patents, citations, and classifications to identify prior art. The tool also has features like spam filtering and focused limitation searches.

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Object analysis device that can provide an evaluation including business relevance by clustering objects based on citation relationships, detecting objects associated with a common organization across clusters, and calculating feature amounts using the citation and cluster association data.

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Dynamic monitoring method for critical development paths of unconventional energy technologies using patent citation networks. It involves constructing a citation network from unconventional energy patent applications, calculating edge weights, finding key development paths using dynamic programming, and monitoring those paths over time. The method provides objective, efficient, and low-uncertainty monitoring of unconventional energy technology evolution compared to subjective expert surveys.

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Patent evaluation method based on citation time difference and community classification to objectively rank patent importance. It constructs a patent citation network, performs unsupervised community classification on all nodes, counts the time difference between citations and cited patents, and assigns different weights to citation relationships based on community classification and citation time difference. The PageRank algorithm is used iteratively on the weighted network to obtain patent ranking. The method considers both citation quantity and quality, and factors like patent age and community context, to provide more objective patent evaluations compared to traditional indicators.

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Automatic generation of patent citation landscapes for comprehensive analysis of patent family connections and citations to identify problematic patents and analyze portfolio value. The system identifies related patents in a family, and searches and aggregates backward citations for analysis.

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Efficiently analyzing patent citation networks to extract insights from patent citations. The method involves dividing the patent citation network into subgraphs, establishing coupled systems for each subgraph, alternately optimizing the systems, and evolving the network structure. It learns the patent citation network graph model through citation relationships to better extract patent information and explain citation dynamics.

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Intelligent processing of intellectual property information to proactively understand industry technology development and patent layout. The method involves building a patent information storehouse with classification number indices and vectors. For each classification number, keywords are extracted and stored. Crawled patents have their classification number vectors supplemented. To analyze a new technical scheme, its vector is calculated and compared with classification numbers. If similarity exceeds a threshold, it recommends that classification number for the scheme.

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A method for processing patent information to evaluate the value of patents over time by leveraging temporal graph neural networks. The method involves constructing a time-varying patent network with timing information using citation relationships between patents over time. It then extracts spatio-temporal features from the network to determine node importance and centrality. Based on this, it assesses the importance of patents at specific time points, expressing patent evolution trends in a comprehensive and detailed manner.

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Analyzing the network structure of backward citations to predict patent value. The method involves characterizing the network characteristics of prior art cited by a patent, such as constraint, cohesion, and efficiency, and analyzing the effect of these network features on patent value. By calculating network variables from the cited patents, it provides insight into the potential impact and predictability of future patents.

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Wireless worldwide patent search and analytics tool that can be used for the search of undiscovered and undiscovered patents. The tool analyzes the claims under consideration (query claim of query patent), the text of the art, the link structure of the citation network, and the patent classification.

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Method to predict future technological knowledge flows between fields using patent data and graph neural networks. The method involves extracting growth, diffusion, and absorption metrics for each tech field over time. These metrics are then passed through modules to generate vectors representing the tech field's diffusion and absorption capacity. These vectors are input into a tech flow tracking module to predict future capacity vectors. By matching the capacity vectors, probabilities of knowledge flow between tech fields are determined. This leverages patent citation networks and hierarchical classification to accurately predict future tech knowledge flows.

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A method and system for mining technical text based on clustering graphs to accurately, scientifically, and quickly identify the frontier areas of technology research. The method involves constructing a technical clustering map using document coupling analysis, identifying the research frontiers based on the map, collecting patents and citations on those frontier technologies, and then dynamically generating a full path model of the patent citation network to find the key development pathways. This allows monitoring and forecasting of real-time breakthrough technologies at convergence points.

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Visual analysis method and system for multi-layer citation network correlation of documents, allowing intuitive and effective exploration of the connections between papers and patents. The method involves associating documents from different systems like papers and patents using natural language processing. It extracts topics from documents using LDA, associates topics between systems using topic vector similarity, and visualizes the associations using tools like force-directed graphs. This enables mapping the research themes across document types and understanding the relationships between them.

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A method and system for evaluating patent quality using a combination of network analysis and standard scores. The method involves constructing a weighted network of IPC classification numbers from patent data, calculating network feature indices to quantify patent novelty and typicality, and using regression analysis to predict citation counts based on those indices. This allows quantitative assessment of patent quality beyond just standard scores, and provides a way to predict citation potential.

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Analyzing intellectual property data using a method that visually displays the relevance and connection of patented technologies over time. The method involves calculating similarities between technologies using cosine similarity based on their patent data. This creates a matrix diagram showing the relationships between all technologies applied for by an organization over a specified time period. It allows understanding of the organization's technological development and patent layout as a whole, and identifying hotspots and gaps in specific fields.

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A method and system for classifying patent quality based on network features. It involves quantifying the novelty and typicality of patent features using network analysis techniques. The method constructs a directed weighted network of patent classification numbers. Network features are calculated to measure the novelty and typicality of classification numbers. Patents are grouped based on these scores and the proportion of highly cited patents in each group is calculated. The method also uses polynomial regression to analyze the relationship between feature novelty/typicality and citations. This allows predicting future citations of new patent applications based on feature scores.

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Method and apparatus for analyzing technological paradigms in a specific domain by quantifying the influence of patents on each other through citation relationships. It calculates a persistence value for each patent that indicates how long its knowledge lasts. Patents with high persistence are technological breakthroughs. By identifying patents with persistence above a threshold, it finds past and future paradigms.

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Analyzing intellectual property (IP) data to generate frameworks that can be used to provide services related to IP assets. The frameworks are built using natural language processing and machine learning techniques to map IP assets to products/services, determine valuations based on product revenue, and provide metrics and reports for IP portfolios. It leverages public and private IP databases, crowdsourcing, and parsing techniques to accurately analyze IP-product/service relationships.

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Automatically finding relevant documents for a given set of documents by analyzing citation information. The method involves recursively processing citation information of the queried documents to find citing documents, then processing citation information of the citing documents to find further cited documents. Relevance scores are computed for the cited documents based on how they are cited by the citing documents. Relevant documents are determined based on direct citation relevance from the original query documents, and indirect citation relevance from the recursive search.

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