330 results about "Intellectual property" patented technology

A novel avatar-based conversational AI platform that integrates large language models (LLMs) with realistic avatar visualization is disclosed. The platform employs advanced AI technologies to create lifelike avatars capable of personalized and emotionally responsive communication with users. avatar creation tools enable users to generate unique avatar configurations, while animation machine learning subsystems automatically generate the avatar's speech, movements, and emotional responses in real-time. personality machine learning subsystems direct the avatar's voice, personality, and emotions, enhancing the realism of the interaction. The platform supports integration with third-party applications and provides comprehensive deployment and asset management tools. A two-way messaging architecture enables real-time interactions between avatars and users. Authorization and licensing controls protect intellectual property rights, while the avatar's personality dynamically evolves based on user interactions. By integrating LLMs, real-time animation, and emotional intelligence, the platform offers a groundbreaking solution for creating immersive and personalized conversational experiences across various applications.

The invention relates to the technical field of deep learning, and discloses an intelligent consultation response system and method based on deep learning, and the system comprises a data acquisition module, a feature extraction module, a feature fusion module, a relevance calculation module, an infringement probability generation module, a clause screening module, and a reply generation module. The intellectual property consulting module is used for obtaining intellectual property consulting questions and legal texts of a user, generating a key clause set of the legal texts according to a multi-scale text convolution kernel and a multi-head attention mechanism, calculating semantic relevance between the intellectual property consulting questions and key clauses, constructing a knowledge graph of the key clauses, and displaying the knowledge graph. Performing multi-hop reasoning on the knowledge graph based on a graph neural network to obtain an infringement probability score, performing clause screening on the key clause set based on the infringement probability score and semantic relevance to obtain screened clauses, and generating a structured standard reply of the question according to the legal potency level of the screened clauses, and the response efficiency of the intellectual property consultation problem is improved.

The present invention introduces a dual-chain property management system that integrates blockchain-based tokens with off-chain government and institutional property records to provide secure, verifiable, and legally recognized asset transactions. Unlike existing blockchain-based property solutions, this system prevents unauthorized tokenization, enforces compliance with jurisdictional property laws, and synchronizes title records dynamically. A concurrency management module eliminates double-minting risks, while a discrepancy resolution engine continuously detects and corrects mismatches between blockchain and legal registries. Smart contracts automate ownership transfers, lease agreements, lien tracking, and compliance enforcement, ensuring fraud-resistant transactions in real estate, vehicles, intellectual property, luxury goods, and financial instruments. This innovation enables legally binding digital property transfers with real-time regulatory oversight, bridging the gap between decentralized asset tokenization and traditional legal frameworks. By automating verification, synchronization, and compliance, the system creates a scalable, transparent, and jurisdictionally compliant property management ecosystem.

A blockchain-based intellectual property (“IP”) asset fractional payment system and method that facilitates sharing a revenue stream generated from an intellectual property asset among individuals having a fractional ownership of the revenue stream generated. A fractional ownership platform is employed where each fraction purchased may subsequently be divided into fractions of fractions, and those fractions can be sold or transferred without limitation. Both the IP asset created, and each fractional ownership share of a revenue stream generated therefrom may digitally represented using an NFT such that a smart contract on a blockchain may be employed to enable and facilitate fractional ownership tracking among the IP asset's owner and any number of third parties holding fractional ownership of the IP asset's associated revenue stream and payment distribution to the IP asset owner and all fractional owners.

The invention provides an intelligence information integration method and device based on a multi-agent task chain strategy large model, and the method comprises the steps: collecting a plurality of pieces of open-source dynamic science and technology intelligence information of a target information source, and employing a literature analysis agent, an abstract induction agent, a content generation agent, a sequencing agent and a report generation agent which are constructed through a large language model; key information extraction, semantic analysis and induction, content evaluation and updating, information sequencing and version change recording and report generation are performed respectively, and automatic aggregation and sequencing of intelligence information are realized. According to the method, the defects of generalization, objectivity, cross-document information processing, flexibility, robustness, illusion rate, extrapolation ability, small sample learning ability and the like in intelligence information integration in the prior art are effectively overcome, the efficiency and effect of intelligence work are improved, and the intellectual property is improved. The method is of great significance in enhancing the service quality benefit of dynamic intelligence information and supporting strategic decision and policy making.

The invention discloses a big data hypergraph network modeling method and device, computer equipment and a product, aims to solve the problem that efficiency and accuracy are difficult to consider when large-scale data of intellectual property is processed in the prior art, and aims at the characteristics of high increment, high timeliness and multiple modes of the intellectual property data. The method comprises the following steps: analyzing and concluding passed intellectual property files, classifying according to corresponding professions, and establishing a corresponding intellectual property text library; constructing a text corpus based on intellectual property by using a BERT algorithm model, performing capsule graph neural network processing on the image information of the corresponding category, obtaining a correlation between image objects and text attributes thereof, and constructing an image database; capturing complex correlation characteristics in the text by utilizing the characteristics of the graph convolutional neural network; and constructing a hypergraph neural network model. The invention provides a big data hypergraph network modeling method capable of processing high-increment, high-timeliness and multi-modal data.

The invention relates to the technical field of artificial intelligence, in particular to a federated learning model property protection method based on quantum coding and lattice passwords. The method comprises the steps that firstly, an initial global model is distributed, and quantum watermark generation and embedding are carried out on the basis of the initial global model in combination with lattice password quantum state coding and an information theory; then, generating corresponding quantum fingerprints based on the initial global model embedded with the watermark, aggregating the quantum fingerprints by adopting a multi-key homomorphic encryption method to obtain global aggregated fingerprints, and recording corresponding identity information in a DAG account book; and finally, receiving verification information, and performing model ownership affiliation verification based on the verification information and the identity information in combination with a zero-knowledge proof protocol based on a lattice difficulty problem. The watermark is encrypted by an LWE instance, and any calculation behavior trying to remove the watermark is equivalent to solving a difficult LWE problem, which is not feasible in calculation. Excellent performance is achieved by adopting an advanced lattice cryptographic algorithm.

A system and method for collaborative creation and management of intellectual property (IP) projects is disclosed. The system includes a server configured to dynamically assign roles and permissions to participants based on predefined criteria. A graphical user interface (GUI) facilitates real-time collaboration, editing, and version control, while a semantic analysis engine ensures compliance with intellectual property standards and assesses content novelty. Predictive analytics generate metrics, such as patentability scores and inventorship overlap, to guide project development. Contributions are electronically tracked and stored in a secure, non-transitory computer-readable medium alongside generated metrics. The system supports role-specific recommendations, automated notifications, and integration with external data sources for enhanced functionality. Participants can visualize contributions, resolve conflicts, and simulate commercialization outcomes, enabling efficient project management and innovation. This invention improves the collaborative development process, ensuring compliance, enhancing productivity, and maximizing the value of intellectual property assets.

The invention discloses a concept erasing method and system based on a T2I autoregressive visual generative model, and belongs to the technical field of intellectual property, personal privacy protection and machine learning. The method comprises the following steps: (1) positioning a causal key layer of a target concept through causal inference, including noise-free operation, noise addition operation and recovery operation, recovering an intermediate activation value of a specific layer under a noise addition condition in the recovery operation as an intermediate activation value under a noise-free condition, and determining the causal key layer according to CLIP score change of a generated image and the target concept; and (2) modifying parameters of the causal key layer based on knowledge editing to erase the generation capability of the target concept while minimizing the influence on other concepts. According to the method, the target concept representation in the model parameters can be accurately positioned, the performance influence on other functions of the model is minimized while the target concept generation capability is completely eliminated, and the requirements of intellectual property and personal privacy protection are met.

The invention belongs to the technical field of project management, and particularly relates to a scientific research project full-process management platform. Comprising a scientific research project management cockpit module, a scientific research project library management module, a scientific research project execution management module, a scientific research project document management module, a scientific research project budget management module, an intellectual property library management module, a scientific and technological achievement library management module, a scientific research project report management module and a scientific research project billboard module. The beneficial effects of the invention are that the system achieves the higher refined management requirements of scientific and technological innovation through an informatization means, and solves the problems of project dispersion, repeated research and development, and project and talent cultivation. And the obvious defects of thin base, late starting and insufficient professional degree in scientific and technological innovation management of production enterprises and research and development institutions for energy power generation are overcome.

The invention discloses a Bayesian-based two-stage deep regression model copyright protection method, which comprises the steps of 1, constructing and training a dropout layer-based watermark model, 2, acquiring a public model and a suspicious model, 3, selecting a key sample, 4, constructing a Bayesian network-based likelihood estimation model, 5, selecting a Bayesian network-based likelihood estimation model, 6, selecting a Bayesian network-based likelihood estimation model, and 7, selecting a Bayesian network-based likelihood estimation model. 5, calculating the infringement probability of the suspicious model by using a likelihood estimation model based on the Bayesian network; and 6, calculating the KL divergence between the suspicious model and the output of the watermark model so as to judge whether the suspicious model infringers or not. When copyright of the regression model is stolen, irrelevant models can be preliminarily excluded in the first stage, and whether the suspicious model is the pirated model or not is further judged in the second stage, so that the pirated model can be quickly identified, and intellectual property of the pirated model is protected from being infringed.

The invention relates to a science and technology project evaluation method and system, and relates to the technical field of multi-index decision and intelligent modeling. Comprising the steps of collecting multi-dimensional data of finance, intellectual property, teams, historical projects and the like of science and technology projects; constructing an evaluation index system; constructing a cognitive map by taking the indexes as nodes and combining statistical correlation and expert rules; a graph attention network (GAT) is utilized to train the graph, and structural causal weights among indexes are extracted; fusing the structure weight and the original fuzzy relation matrix to form a structure enhanced fuzzy relation matrix; and in combination with a weight vector determined by a fuzzy analytic hierarchy process (FAHP), performing fuzzy weighted comprehensive evaluation, and outputting a project grade and an interpretable result. According to the method, the accuracy, self-adaptability and interpretability of science and technology project evaluation are effectively improved, and the method is suitable for complex project evaluation tasks in a multi-source heterogeneous data environment.

The invention provides an intellectual property and academic assistant system, an implementation method and device thereof, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps that question information is obtained, the question information is input into an intention classification model for intention classification, an intention classification result indicating question categories of the question information is obtained, and the question categories of the question information comprise an intellectual property question and answer category, a patent retrieval category and an academic question and answer category; if the question category of the question information is a patent retrieval category, inputting the question information into a patent retrieval model to generate a patent retrieval result; if the question category of the question information is an intellectual property question and answer category, inputting the question information into an intellectual property large model to generate an intellectual property answer result; and if the question category of the question information is an academic question and answer category, inputting the question information into an academic question and answer model to generate an academic question and answer result. The problem of poor efficiency of an intellectual property and academic assistant system in related technologies is solved.

The invention relates to the technical field of data processing, and discloses an intellectual property information analysis and matching system based on big data, comprising: an acquisition unit acquiring user demands and a historical demand set, performing semantic analysis on the user demands to extract demand keywords, and determining importance; sequencing the demand keywords according to the importance to form a current keyword group; the judgment unit is used for comparing the keyword group with a historical demand set and judging whether a historical matching catalog is taken or not; when the historical matching catalogue is judged to be used, determining a current matching catalogue according to the historical matching catalogue and the current keyword group; when it is judged that the historical matching catalogue is not used, determining a current matching catalogue according to the current keyword group; the processing unit performs clustering analysis on the matching catalog to determine a matching display result; and the storage unit stores the current keyword group and the matched display result. According to the invention, the matching efficiency and accuracy are improved, and the problems of low efficiency, low accuracy and the like in traditional intellectual property matching are solved.

The invention discloses a model anti-attack robustness evaluation method and system in a black box scene, and belongs to the field of anti-attack and artificial intelligence intellectual property protection analysis. Comprising the steps of obtaining an access permission of a target model in a black box scene, and defining a substitution model having the same function as the target model; obtaining a label-free original image sample consistent with target model data distribution, and taking the label-free original image sample as an initialized confrontation sample; optimizing the adversarial sample by using the substitution model; and inputting the original image sample and the final adversarial sample after iteration into the target model, and calculating a robustness evaluation score and a security level of the target model. According to the method for generating the adversarial sample, the intermediate feature of the substitution model is used as an optimization target, the adversarial sample is optimized to generate a more robust adversarial feature so as to enhance the adversarial mobility of the adversarial sample, and the method does not need to obtain a model label of the sample. Quantitative evaluation of the model anti-attack robustness is realized on the basis of taking the anti-attack feature robustness as a model security evaluation index.

The present disclosure is related to systems and methods for application of machine learning techniques to functional safety analyses of IP cores used in chip designs, to facilitate efficiency, accuracy and completeness of the chip designs. In embodiments of the present disclosure, a functional safety system receives input data for a chip design, and profiles the input data to identify a plurality of IP cores present in the design. The functional safety system determines a match of at least one identified IP core with an IP core present in a functional safety related data structure. The functional safety system then conducts one or more safety analyses on the identified IP core and generates a safety report accordingly.

The invention discloses a model adversarial mobility risk assessment method and system based on adversarial weight adjustment, and belongs to the field of adversarial attack and artificial intelligence intellectual property protection analysis. Comprising the following steps: generating an adversarial sample with high mobility through iterative optimization of a substitution model and the adversarial sample; and evaluating the adversarial mobility risk of the target model based on the generated adversarial sample by calculating a safety index. The invention aims to evaluate the security of the deep learning model for defending against the attack of the confrontation sample in the black box attack scene. According to the method, the flat local maximum value and the model smoothness of the adversarial sample are optimized by adaptively adjusting the parameters of the substitution model, so that the mobility of the adversarial sample is enhanced, and the substitution model does not need additional training data. The invention further provides a migration countermeasure attack safety index, and quantitative measurement is provided for model safety. According to the method, the vulnerability of the model to migration countermeasure attacks can be comprehensively analyzed, and researchers and engineers can be helped to design more effective defense strategies.

The invention provides an industry classification label construction method and a related product, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining scientific and technological innovation subject data, intellectual property data and existing industry classification data, carrying out the data processing, and obtaining relation data between the scientific and technological innovation subject data and the intellectual property data; extracting and fusing keyword data from the intellectual property data, and extracting and synchronously storing relational data between the keyword data and the intellectual property data; constructing a knowledge graph, and performing entity classification and theme feature extraction based on the knowledge graph to obtain a final-stage industry classification label; and associating the last-stage industry classification label with a preset industry classification label to form an industry classification label system. According to the technical scheme, the knowledge graph is established, the last-stage industry classification label is extracted by utilizing the scientific and technological innovation subject data, the intellectual property data and the relational data between the data, and the industry classification label system is established in combination with the existing industry classification data.

The invention relates to the technical field of block chains, in particular to an intellectual property transaction traceability information storage method and system based on a block chain, and the method comprises the following steps: S10, collecting information recorded in an intellectual property full life cycle, and integrating the information into intellectual property full life cycle traceability information through a PROV model; s20, packaging the traceability information into an intelligent contract; and S30, verifying the packaged traceability information based on the threshold signature added with the timestamp. According to the method, the PROV model and the threshold signature are deeply fused, so that the integrity and verifiability of each operation step are ensured, sensitive operation must be authorized by a legal program, and the risk of centralized storage of key data is avoided through distributed key management. The technology coupling not only strengthens the non-repudiation of the traceability data, but also realizes hierarchical authorization control through a dynamic threshold strategy, and obtains balance between safety and efficiency.

A brand protection tool for managing risk to a brand is disclosed. The brand protection tool may provide a survey to a user. The survey may include a plurality of questions related to intellectual property. Based at least in part on the survey responses, the brand protection tool may determine a risk score, a protection score, and a net protection score. The brand protection tool may automatically generate a report including the net protection score and a visualization including a representation of the risk score and protection score. The brand protection tool may provide the report to a user or downstream system.

The present invention discloses a method for software bidding settlement review, including a function review module, a function point code decomposition module, a compliance module, a security module, an economic review module, and an open source code management module. The method for software bidding settlement review of the present invention compliantly references open source code, utilizes standard development function points, saves the development cycle and development costs, greatly saves social resources, makes the cost formula of code development, and at the same time avoids security and intellectual property infringement disputes brought by open source code.

The invention discloses a project and intellectual property achievement association evaluation method and system, and relates to the technical field of evaluation methods, and the method comprises the following steps: S1, data acquisition: obtaining project data, intellectual property achievement data and product data; s2, association model construction: constructing a project-intellectual property achievement-product association model based on the project data, the intellectual property achievement data and the product data; s3, matching degree calculation: calculating a multi-dimensional matching degree between the project and the intellectual property achievement based on the association model; s4, performing dynamic correction; and S5, value evaluation. According to the method, through quantitative analysis of a claim modification track in the review opinion reply process, especially the core index of the calculation technology abandon-retention ratio, patents which are subjected to large limitation shrinkage and of which the core protection range is seriously shrunk can be effectively discriminated, and association degree overestimation caused by neglecting the review game process is avoided.

This invention introduces the Dynamic Equity Distribution Method, a groundbreaking system designed to enable fair, transparent, and adaptable equity allocation in startups and collaborative ventures. By integrating blockchain technology, tokenization, and dynamic adjustment mechanisms, this method overcomes the limitations of static equity models, ensuring that ownership shares continuously align with each member's real-time contributions. Key features include: Comprehensive Contribution Valuation: A structured approach to evaluate both financial and non-financial contributions, such as intellectual property, network resources, physical assets, skills, and time, using Fair Market Value (FMV) assessments. Dynamic Adjustment Mechanism: Automated equity realignments powered by smart contracts, conducting periodic reviews to reflect ongoing contributions and performance milestones. Tokenization and Blockchain Integration: Equity shares represented as digital tokens stored on a blockchain, ensuring transparency, security, and ease of ownership transfer. Structured Exit Protocols: Predefined procedures for member exits, including buy-back options, share dilution mechanisms, reserve funds to manage ownership transitions seamlessly.

To support intellectual property business of users.SOLUTION: An input unit (103) of a business support system (1) accepts input of at least a part of a document for inference, which is a document for a patent application or application for utility model registration, to a large-scale language model including a first model in which first training data including a first input part related to a part of a document for training at least and a first output part related to whether or not the part thereof at least should be claimed up has been re-learnt, and another model used supplementarily for inference related to whether or not the part thereof at least should be claimed up. An output acquisition unit (104) acquires an output related to whether or not at least a part of the document for inference should be claimed up from the large-scale language model. A business support unit (105) supports intellectual property business by determining, based on the output, a text subject to claim-up among texts included in the document for inference, and recommending the text subject to the claim-up to a user.SELECTED DRAWING: Figure 7

The invention belongs to the technical field of intellectual property, and discloses a patent commercialization value evaluation system and method, and the system comprises a patent information obtaining unit which is used for obtaining target patent information from a patent database; the patent technical analysis unit is used for extracting keyword features in the patent text and analyzing technical features of the patent text according to a similarity comparison result; the market potential prediction unit is used for analyzing the technical field in the patent text and predicting a market response demand conforming to the technical field by using a time sequence analysis technology; and the value comprehensive evaluation unit is used for establishing a comprehensive evaluation model by taking the technical characteristics of the patent text and the market response demand as evaluation indexes, and evaluating the value degree of the target patent by utilizing the comprehensive evaluation model. Through combination of technical characteristics and market response demands, the technical innovation and commercialization potential of patents are comprehensively measured.

The invention discloses a global user and manufacturer oriented end-to-end online customization manufacturing platform system and a method thereof, which realize the whole process from creative input to finished product delivery based on ai-driven design generation and global manufacturing resource intelligent scheduling. The platform comprises an AI design generation module, a CAD modeling and optimization module, a manufacturing rule verification module, a material intelligent recommendation module, a global factory matching and pricing module, a tax and logistics estimation module, a one-key ordering and delivery module, an intellectual property protection module and a user and factory authentication mechanism module. And a multi-part structure decomposition and remote part division type cost optimization scheduling module. A user can generate a product drawing through language or image input, a platform automatically outputs a CAD model and performs structure optimization and manufacturability verification, and materials are recommended in combination with a purpose scene. The system intelligently matches global manufacturers based on factors such as features, manufacturing difficulty, regions, tax and productivity, estimates the tax and logistics cost, and provides a one-key ordering path.

The invention discloses a project file building method and system, a chip verification system and a storage medium. The project file is used for providing a simulation environment of a to-be-tested chip, and the project file building method comprises the following steps: creating a project directory; storing project files of previous projects according to the project directory to obtain a sample library, wherein the sample library comprises a plurality of intellectual property modules and a plurality of verification intellectual property modules; obtaining an intellectual property module required for constructing the iteration project and a verification intellectual property module for verifying the function point from a sample library according to the function point of the iteration project; and filling the project directory according to the obtained intellectual property module and the verification intellectual property module to obtain a project file of the iterative project. The project file of the iterative project is automatically established based on the project file of the previous project, so that the establishment efficiency of the project file of the iterative project is improved, and the research and development period of the iterative project is further shortened.

The invention belongs to the technical field of data storage management, and discloses an intellectual property data-based storage management system, which comprises the following steps of: firstly, acquiring comprehensive data, preprocessing, and constructing a comprehensive feature set; secondly, according to the comprehensive feature set, repeated data are detected, and repeated data items are identified; then, the influence of the repeated data items is evaluated and processed; and finally, in the data item processing process, data among multiple nodes are kept consistent, and abnormal states are monitored in real time, so that quick response and problem solving are facilitated, the intellectual property data management efficiency and quality are improved, the robustness and reliability of the system are enhanced, and more reliable and convenient services are provided for users.

The invention provides an intellectual property service intelligent recommendation method and system and electronic equipment, and the method comprises the steps: carrying out the feature extraction of an invention technology material submitted by a user, obtaining a technical feature vector, carrying out the data preprocessing, constructing a technical feature matrix based on the technical feature vector, and constructing a human body immune model. Inputting the technical characteristic matrix as an antigen into a human immune model, generating an initial technical antibody, obtaining an initial technical antibody candidate set based on the initial technical antibody, performing clone mutation operation on the initial technical antibody candidate set based on two-channel evaluation, obtaining a mutation technical antibody candidate set, and obtaining a mutation technical antibody candidate set; carrying out three-property optimization screening on the variation technology antibody candidate set to obtain an immune technology antibody set; carrying out simulation examination verification on the immune technology antibody set based on GAN to obtain a technology optimization strategy; optimizing the immune technology antibody set based on the technology optimization strategy to generate a corresponding intellectual property service strategy; and a more refined service recommendation strategy is provided for intellectual property service recommendation.