Patent Classification Innovations

A method for accurately and efficiently classifying patents using artificial intelligence and natural language processing. The method involves training AI models to classify patents based on business language usage instead of the traditional hierarchical codes. The AI models learn from subject matter experts to understand how granted patents are actually used in industry. This allows more accurate classification of patents beyond just what they are. The business language classification system, called BVC, has other useful applications like patent audits for M&A.

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Patent early warning analysis method using natural language models to assist enterprises in analyzing patent information and improve efficiency. The method involves determining core classification phrases for patents using synonym chains and keyword segmentation. It then compares core phrases between patents to find repeated words and generates warning levels based on the number of repeated words. This allows identifying potential infringement risks between patents by comparing their core classification phrases.

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A method and system for predicting disruptive patents using intelligent models. The method involves identifying potentially disruptive technology themes from patent data using machine learning algorithms, scoring the themes using a disruptive technology measurement model, and selecting the top 10% with highest scores for further analysis. It combines SVM-LDA, indicator system construction, and cosine similarity algorithms to improve accuracy in identifying disruptive technologies in complex, uncertain environments.

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Patent classification method that improves accuracy by leveraging the interconnectedness between patents, inventors, and patent owners. The method involves extracting feature vectors for patents, inventors, and patent owners using separate networks. These feature vectors are then fused into a single representation vector and fed into a classification network. The separate view networks allow capturing distinct aspects of patent data, like text content, inventor expertise, and company background. The fusion step combines this multi-view information into a compact representation. This improves classification accuracy compared to single-view methods.

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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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A method for efficiently classifying Chinese patent documents using a multi-feature fusion approach based on the TRIZ invention principle. The method involves dynamically representing Chinese patent texts using the ALBERT pre-trained language model. It then extracts local features using bidirectional convolutional neural networks and global contextual features using bi-directional GRUs with self-attention. The extracted features are fused to obtain a more comprehensive text representation for classification. This improves accuracy compared to traditional classification methods by capturing both local character and global contextual semantics of patent texts.

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A method for accurately and efficiently classifying scientific research projects into subject categories using machine learning. The method involves leveraging attention mechanisms to classify project content, and then using additional related information like project type and funding source to refine the classification. This two-stage classification process improves accuracy compared to just keyword matching. The method involves obtaining project details, passing the content through an attention-based network to get an initial classification, then feeding both the initial result and related info to a second network to refine the classification.

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Automated system for generating custom industry classifications for startups based on their descriptions. The system uses a hybrid deep learning-based hierarchical classifier to classify industries and products/services for startups using their descriptions. It leverages representation learning techniques to automatically convert industry and product descriptions into vector representations. Unsupervised matching is done between the vector representations to assign unmapped descriptions to known classifications. Supervised training is then done with startup descriptions to classify them into custom and standard industries.

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Method for accurately classifying the type of entities like companies and organizations even when they lack a unique identifier like a social security number. The method involves extracting features like keywords from the entity names and regular expressions from the coding categories to create a dataset for training a classification model. This allows classification of new entities without unique identifiers using the learned patterns from the known entities.

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Efficient patent classification method using semantic similarity to improve the speed and accuracy of patent classification compared to manual classification. The method involves extracting key features from patents and comparing them to pre-stored features at each classification level. If a feature matches, that level becomes the patent's classification. This leverages semantic similarity between features to classify patents without manual review.

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Multi-level patent text classification using graph neural networks to improve patent document categorization accuracy. The method involves a multi-stage classification process where the graph neural network extracts features from the patent text at each stage. In the first stage, it identifies broad classifications like technology areas. In subsequent stages, it further subdivides into more specific categories. This multi-level approach allows capturing both high-level and detailed classifications from the patent text.

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Semantic-based intellectual property text classification method using a trained appearance patent classification model. The method involves constructing a training set with patent names, drawings, and classifications. The model learns text, image, and fusion features. It adjusts parameters using loss functions based on the training data. This trained model is then used to classify new patent application texts using the learned features.

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Method for analyzing the subject content and popularity evolution of patented technologies by leveraging the International Patent Classification (IPC) system. The method involves using a topic modeling technique called Partial Latent Dirichlet Allocation (pLDA) to extract technical topics from patent documents. The pLDA model is modified to take into account the IPC classification level and abstract text of the patent. By setting the IPC level for topic mining, it allows fine-grained analysis of technical topics at different levels. In subject evolution analysis, word clouds are used to show the subject content under IPC classifications over time. The topic intensity and trend are calculated to determine the hotness trend of topics under IPC classifications.

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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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Method for analyzing technology trends using patent data and classification systems to identify detailed technologies and their convergence, and objectively quantify technology development trends. The method involves extracting patent classification systems, configuring a matrix, clustering to form detailed technology groups, identifying technologies within each cluster, and applying time series analysis to track development trends.

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Patent recommendation method for assisting designers in expanding their knowledge space to improve conceptual design quality and efficiency. The method involves partitioning a network of patents into multiple domains based on their technical and semantic features. It then recommends patents related to design concepts by finding patents with similar concept combinations and evaluating their relevance and centrality in the domain network. This reduces the burden on designers to manually search and sift through many related patents to find specific ones.

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Intelligent resource management system that uses AI to optimize procurement of machine resources like compute, storage, and network capacity. The system interprets resource requirements and uses machine learning to predict future needs and make forward purchases in resource markets. It aggregates data from internal and external sources to configure purchases and timing. The system acquires resources at the best value based on cost parameters.

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Analyzing businesses using patents and financial data to provide efficient and short-term business analysis when intangible assets like intellectual property are involved. The analysis involves extracting patent factors, weighting them, assigning scores to patent holders, extracting businesses based on financial data, weighting industries, clustering patents with industries, and outputting analysis results. This allows understanding patents and businesses as part of value chains.

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Automated patent document classification method that uses text processing techniques to improve efficiency and accuracy compared to manual classification. The method involves preprocessing patent documents by formatting and stop word removal. Then, initial classification by field is done based on patent classification numbers. Finally, core words are identified and classified using text algorithms and models based on their frequency in each document group.

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Keyword-based patent text classification to automate patent classification using machine learning instead of manual classification. The method involves training a patent classification model by extracting features from historical patents in a field, converting them into feature maps, and using those maps to train the model. When a new patent text is provided, it's analyzed using the trained model to classify it into the same field. This saves time compared to manual classification as the model can handle large numbers of patents more efficiently.

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