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DIKWP Patent Analysis of Granted Inventions
(Beginners' Edition)
Yucong Duan
Benefactor: Shiming Gong
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Catalog
1 Data Processing and Analysis Related Patents Detailed Analysis
1.3.1 Image Data Target Recognition Method Based on Three-layer Graph Architecture
1.3.3 Resource Identification Method Based on DIKW Graph
1.4 Future Development Directions
2 Privacy Protection and Security Processing Related Patents Detailed Analysis
2.3.1 Relative Differential Privacy Protection Method Across DIKW Modalities for Essence Computation
2.3.2 Blockchain Consensus Method Based on the DIKWP Model
2.4 Future Development Directions
3 Wisdom Systems and Reasoning Related Patents Detailed Analysis
3.3.1 Cross-DIKW Modality Text Ambiguity Processing Method for Essence Computation and Reasoning
3.4 Future Development Directions
4 Wisdom and Purpose-driven Systems Related Patents Detailed Analysis
4.3.1 Multimodal DIKW Content Transmission Method Driven by Purpose
4.3.2 Content Processing Method and System Based on Purpose-driven DIKW
4.3.3 DIKW Model Construction Method and Device for Purpose-driven Computing and Reasoning
4.4 Future Development Directions
5 Detailed Analysis of Patents Related to Intelligent Privacy Protection and Security Systems
5.3.1 Cross-modal Randomized Privacy Protection Method Based on DIKW Modalities
5.3.2 Cross-modal Recommendation Method and Device for Essence Computation and Reasoning
5.4 Future Development Directions
6 Detailed Analysis of Patents Related to Medical and Health Management Systems
6.3.1 Vaccination Concentration Confirmation Method Based on DIKW Map
6.3.2 Cross-modal User Medical Data Analysis Method for Essence Computation
6.3.3 Personality Analysis and Content Push Method for Virtual Community Personnel Based on DIKW Map
6.4 Future Development Directions
7.3.1 Cross-modal Recommendation Method and Device for Fusion of Essence Computation and Reasoning
7.3.2 Cross-modal User Medical Data Analysis Method for Essence Computation
7.3.3 Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation and Reasoning
7.3.4 Cross-DIKW Modal Text Ambiguity Processing Method for Essence Computation and Reasoning
7.3.5 Cross-modal Feature Mining Method and Component for Essence Computation
7.4 Future Development Directions
8 Cross-Modal Differential Content Recommendation and Protection Patent Detailed Analysis
8.3.5 User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities
8.4 Future Development Directions
9 Cross-Modal Intelligent Analysis and Prediction Technology Patent Detailed Analysis
9.3.3 Cross-Modal Feature Mining Methods and Components for Essence Computation
9.3.4 Cross-DIKW Modal Text Disambiguation Processing Methods for Essence Computation and Reasoning
9.3.5 DIKW Privacy Resource Processing Methods and Components for Essence Computation
9.4 Future Development Directions
10 Detailed Analysis of Patents on Intelligent Data Processing and Information Management Technology
10.3.1 Resource Identification Method Based on DIKW Map, Related Device, and Readable Medium
10.3.3 DIKW Resource Interaction and Filling System for Purpose-driven Computation and Reasoning
10.3.5 Virtual Community User Personality Analysis and Content Pushing Method Based on DIKW Map
10.3.6 Multimodal DIKW Content Multi-Semantic Analysis Method for Essence Computation
10.4 Future Development Directions
11.3.1 Hybrid Feature Machine Learning Modeling and Labeling Method Across DIKW Modalities
11.3.2 Metaverse Virtual Interaction Method Based on the DIKWP Model
11.3.3 Cross-Modal User Medical Data Analysis Method for Essence Computation
11.3.5 Cross-Modal Recommendation Method and Device for Essence Computation and Reasoning
11.4 Future Development Directions
1 Data Processing and Analysis Related Patents Detailed Analysis
In this section, we will analyze the following patents:
"Image Data Target Recognition Method Based on Data, Information, and Knowledge Three-layer Graph Architecture" (CN201810074539.X)
"Enhanced Image Data Target Recognition Method Based on Data Graph, Information Graph, and Knowledge Graph" (CN201810023920.3)
"Resource Identification Method, Related Device, and Readable Medium Based on DIKW Graph" (CN202110431356.0)
These patents focus on graph architectures and aim to improve the accuracy and efficiency of data processing and analysis. In the DIKWP model, the three-layer architecture of data, information, and knowledge graphs is the foundation for achieving intelligent recognition and prediction. Especially in medical imaging analysis and intelligent medical systems, efficient processing of image data is crucial. These patents aim to enhance existing image recognition technologies to address the shortcomings of traditional methods, such as the inability to recognize unlabeled categories of images or low recognition efficiency.
1.3.1 Image Data Target Recognition Method Based on Three-layer Graph Architecture
Innovations:
Multilayer Graph Integration: This method integrates data graphs, information graphs, and knowledge graphs organically, conducting data processing in a progressive manner. First, basic data features are extracted through the data graph, then information matching is performed in the information graph, and finally, high-level semantic reasoning is achieved through the knowledge graph.
Cross-modal Feature Matching: This method allows the system to provide possible recognition results by feature matching and reasoning based on existing graphs when encountering unidentified or newly emerging image data.
Application Scenarios:
Medical Imaging Recognition: Used in the analysis of complex medical images such as CT and MRI, enhancing recognition accuracy through a three-layer graph, especially for the recognition of unlabeled lesions.
Disease Prediction: By integrating historical patient data and existing image data, it can predict potential disease risks in advance.
1.3.2 Enhanced Image Data Target Recognition Method Based on Data Graph, Information Graph, and Knowledge Graph
Innovations:
Information Enhancement and Filtering: Based on the data graph, this method further introduces information graphs and knowledge graphs to enhance data recognizability and remove redundant information through a filtering mechanism, improving recognition accuracy.
Adaptive Graph Expansion: This method allows the graph structure to expand adaptively. As the complexity of recognition tasks increases, the system can dynamically adjust the graph's layers and structure to ensure efficient and accurate recognition.
Application Scenarios:
Intelligent Diagnostic Systems: In intelligent diagnostic systems, enhanced recognition methods can more accurately identify complex diseases and improve the reliability of diagnoses.
Medical Education and Training: This enhanced recognition method provides more detailed case analyses and learning materials for medical students and doctors.
1.3.3 Resource Identification Method Based on DIKW Graph
Innovations:
Dynamic Modeling of Resource Graph: This patent proposes a method for dynamic modeling of resources through the DIKW graph, enabling real-time identification and processing of implicit information related to resources.
Multipath Inference and Verification: Through multipath inference and verification mechanisms, it ensures the accuracy and comprehensiveness of identification results, avoiding the omission of critical data.
Application Scenarios:
Patient Data Management: In patient data management systems, it dynamically identifies and tracks patients' health status and treatment progress through resource identification methods.
Health Monitoring Platform: In remote health monitoring platforms, dynamic identification of resources provides more accurate health alerts and diagnostic suggestions.
1.4 Future Development Directions
Based on the above analysis, the future development directions of these technologies may include:
Further Intelligentization of Graph Architecture: With the advancement of machine learning and deep learning, graph architecture will become more intelligent, achieving higher-level automated recognition and reasoning.
Cross-domain Applications: In addition to the medical field, these graph recognition technologies can be applied in multiple areas such as financial risk assessment and intelligent traffic management.
Real-time Dynamic Updates: Future graph architectures may develop the ability for real-time dynamic updates, allowing the system to continuously learn and optimize its graph structure during operation.
2 Privacy Protection and Security Processing Related Patents Detailed Analysis
In this section, we will analyze the following patents:
"Relative Differential Privacy Protection Method Across DIKW Modalities for Essence Computation" (CN202011580150.6)
"Blockchain Consensus Method Based on the DIKWP Model" (CN202111658319.X)
"Privacy Resource Protection Method Across DIKW Modalities for Essence Computation and Reasoning" (CN202011104613.1)
Privacy protection and security processing are core areas of data technology development, especially in the context of multimodal data processing and large-scale data integration, where ensuring data privacy security is crucial. These patents propose cross-modal privacy protection methods based on the DIKW model, aiming to enhance data security while ensuring system flexibility and scalability.
2.3.1 Relative Differential Privacy Protection Method Across DIKW Modalities for Essence Computation
Innovations:
Application of Differential Privacy Technology: Combining differential privacy with the DIKW model, this method allows for a certain degree of statistical analysis and data mining while ensuring data privacy, thereby ensuring data usability.
Intermodal Privacy Protection: This patent focuses specifically on privacy protection across modalities (such as text, images, and audio) through a multi-level differential privacy mechanism to ensure that the privacy of different modalities is not compromised.
Application Scenarios:
Medical Data Privacy Protection: In medical data sharing and analysis, it ensures patient privacy while allowing research institutions to conduct in-depth analysis on anonymized data.
Social Network Privacy Management: On social network platforms, it provides differential privacy protection for users, preventing the leakage of personal sensitive information.
2.3.2 Blockchain Consensus Method Based on the DIKWP Model
Innovations:
Combination of Blockchain and DIKW Model: This patent innovatively introduces the DIKWP model into the blockchain consensus mechanism, enhancing the security and privacy of data transmission through the distributed nature of blockchain.
Purpose-driven Consensus Mechanism: By introducing the purpose layer of the DIKW model, the blockchain can dynamically adjust consensus algorithms to adapt to different data types and transmission needs, thereby improving system flexibility.
Application Scenarios:
Financial Transaction Security: In financial blockchain systems, it ensures the privacy and security of transaction data, preventing information leakage and data tampering.
Supply Chain Management: In supply chain blockchain systems, it ensures the privacy and security of data at all stages while improving system transparency and efficiency.
2.3.3 Privacy Resource Protection Method Across DIKW Modalities for Essence Computation and Reasoning
Innovations:
Dynamic Inference and Modality Conversion: Through dynamic inference and modality conversion technologies, this patent can switch flexibly between different modalities based on data privacy protection needs, improving the efficiency and flexibility of privacy protection.
Multilevel Implementation of Privacy Protection: This patent proposes a cost-benefit-based privacy protection strategy, allowing the adjustment of privacy protection intensity and scope based on actual needs, thereby balancing privacy protection and data utilization.
Application Scenarios:
Smart City Data Management: In smart city systems, multilevel privacy protection strategies protect citizens' privacy data and prevent data misuse.
Large-scale Enterprise Data Integration: In multinational enterprise data management, it ensures the privacy of data during transmission and usage globally, complying with data protection regulations in various regions.
2.4 Future Development Directions
Future developments in these technologies may include:
Deepening Multimodal Data Privacy Protection: With the advancement of artificial intelligence technologies, future privacy protection technologies will focus more on the interaction and fusion between different modalities, further enhancing the precision of data privacy protection.
In-depth Application of Blockchain Technology: Combining blockchain and the DIKW model, consensus mechanisms will become more flexible and secure to meet the needs of more scenarios.
Balancing Privacy Protection and Data Utilization: Future privacy protection technologies will focus more on maximizing the value of data utilization while protecting privacy, especially in data-driven economic and scientific research activities.
3 Wisdom Systems and Reasoning Related Patents Detailed Analysis
In this section, we will analyze the following patents:
"Cross-DIKW Modality Text Ambiguity Processing Method for Essence Computation and Reasoning" (CN202011103480.6)
"Essence Identification Method and Components Across Data, Information, and Knowledge Modalities and Dimensions" (CN202010692408.5)
"Essential Content Processing Method and System of Multimodal Resources Based on Commonsense Reasoning" (CN202110074301.9)
Wisdom systems and reasoning are core areas in artificial intelligence, aiming to achieve efficient automated reasoning and decision support through in-depth analysis and processing of multimodal data. These patents focus on how to leverage the DIKW model and essence computation methods to achieve innovative breakthroughs in dealing with complex semantic understanding, ambiguity resolution, and cross-modal data fusion.
3.3.1 Cross-DIKW Modality Text Ambiguity Processing Method for Essence Computation and Reasoning
Innovations:
Hierarchical Semantic Processing: By introducing multilayer semantic analysis, this method conducts multi-dimensional parsing and processing of ambiguities in the text, avoiding misjudgments and errors caused by single-level analysis in traditional methods.
Integration of Cross-modal Reasoning: It not only relies on text data but also incorporates information from multiple modalities such as images and voice into the ambiguity processing process, making the reasoning process more comprehensive and accurate.
Application Scenarios:
Natural Language Processing: In applications such as chatbots and intelligent assistants, it precisely handles ambiguous texts input by users, enhancing the intelligence of interactions and user experience.
Intelligent Customer Service System: Helps customer service systems better understand customer needs, reduces communication misunderstandings, and improves problem-solving efficiency.
3.3.2 Essence Identification Method and Components Across Data, Information, and Knowledge Modalities and Dimensions
Innovations:
Multimodal Information Fusion: This method fuses resources from the three levels of data, information, and knowledge, utilizing a comprehensive model for essence identification, thereby improving accuracy and robustness.
Innovation in Dimensional Conversion: By achieving cross-dimensional analysis, it enables unified processing of different modal data, addressing the incompatibility issues existing in traditional methods.
Application Scenarios:
Image and Voice Recognition: In image processing and voice recognition, it achieves precise recognition and understanding of complex scenarios through cross-modal and cross-dimensional analysis.
Big Data Analysis: In big data scenarios, it performs unified analysis and processing of data from different sources and formats, enhancing data insights.
3.3.3 Essential Content Processing Method and System of Multimodal Resources Based on Commonsense Reasoning
Innovations:
Application of Commonsense Reasoning: Combining commonsense reasoning with multimodal data processing enables the system to perform intelligent reasoning based on commonsense, enhancing the rationality and explainability of processing results.
Dynamic Data Processing Capability: Through real-time acquisition and updating of multimodal data resources, the system can dynamically adjust the reasoning process to adapt to changing environments and needs.
Application Scenarios:
Autonomous Driving System: In autonomous driving scenarios, it enhances vehicle decision-making ability and safety through commonsense reasoning and real-time processing of multimodal data.
Smart Home System: In smart homes, it achieves intelligent control and optimization of home devices through multimodal processing of environmental data.
3.4 Future Development Directions
Future developments in these technologies may include:
Enhancing Semantic Understanding in Natural Language Processing: Future wisdom systems will focus more on understanding and reasoning at the semantic level, especially when dealing with complex languages and contexts.
Deepening Application of Cross-modal Data Processing: With the advancement of sensor technology and data collection methods, cross-modal data processing will become the core of reasoning and decision-making in intelligent systems.
Expanding the Application of Commonsense Reasoning: The application of commonsense reasoning will further expand to more fields such as healthcare, law, and education, enhancing the intelligence level of systems.
4 Wisdom and Purpose-driven Systems Related Patents Detailed Analysis
In this section, we will analyze the following patents:
"Multimodal DIKW Content Transmission Method Driven by Purpose" (CN202110867169.7)
"Content Processing Method and System Based on Purpose-driven DIKW" (CN202110909286.5)
"DIKW Model Construction Method and Device for Purpose-driven Computing and Reasoning" (CN202110430285.2)
Wisdom and purpose-driven systems involve optimizing data transmission, content processing, and decision models by deeply understanding user purposes. These patents explore how to deeply integrate user purposes with system functions under the DIKW framework to achieve personalized and efficient intelligent systems.
4.3.1 Multimodal DIKW Content Transmission Method Driven by Purpose
Innovations:
Multimodal Content Analysis and Transmission: This method ensures that transmitted content meets the recipient's needs and expectations by combining user purposes with multimodal content (such as text, images, and audio), achieving content personalization and precision.
Dynamic Optimization of Transmission Process: During the content transmission process, the system can dynamically adjust the transmission strategy based on recipient feedback to optimize the content transmission, thereby improving transmission efficiency and effectiveness.
Application Scenarios:
Remote Healthcare: During the transmission of medical data between doctors and patients, it optimizes the data transmission process by understanding the patient's purpose, ensuring the timely and accurate delivery of critical data.
Intelligent Marketing: On e-commerce platforms, it accurately pushes product information that matches users' needs by analyzing their purchasing intentions, thereby increasing the conversion rate.
4.3.2 Content Processing Method and System Based on Purpose-driven DIKW
Innovations:
Deep Analysis of User Purposes: This system conducts multi-level analysis of user purposes to build a DIKW model highly matching user purposes, thereby achieving precise content processing and delivery.
Intelligent Form Filling: Using purpose-driven technology, the system can automatically fill in data in forms based on user purposes, significantly improving the accuracy and efficiency of form filling.
Application Scenarios:
Automated Office Systems: In automated office environments, the system can automatically generate and fill documents based on user operating habits and purposes, reducing manual intervention.
Customer Relationship Management (CRM): In CRM systems, it automatically generates customized customer solutions based on customer needs and purposes, enhancing customer satisfaction.
4.3.3 DIKW Model Construction Method and Device for Purpose-driven Computing and Reasoning
Innovations:
Purpose-driven Model Construction: This method takes user purposes as the core driving force in constructing DIKW models, achieving dynamic model construction and adjustment, ensuring the model can optimize itself according to changes in user needs.
Semantic Understanding and Reasoning: By combining purpose computing and semantic reasoning, the system can extract key information in complex semantic environments, building a more intelligent and precise decision support system.
Application Scenarios:
Intelligent Search Engine: The search engine automatically constructs an appropriate DIKW model by understanding user search purposes, improving the relevance and accuracy of search results.
Intelligent Recommendation System: In video or music recommendations, it constructs a DIKW model of user preferences through purpose computing to provide recommendations that better match user tastes.
4.4 Future Development Directions
Future developments in these technologies may include:
Enhancing Accurate Capture of User Purposes: Future wisdom systems will focus more on capturing and understanding implicit user purposes through deep learning and natural language processing technologies, further enhancing system intelligence.
Expansion of Cross-domain Applications: These technologies are not limited to a single field but will be widely applied in multiple industries such as finance, healthcare, and education, providing technical support for intelligent transformation across industries.
Optimization of Real-time Dynamic Models: With the accumulation of user behavior data, the system can dynamically adjust and optimize the DIKW model to ensure accurate service and decision support in dynamic environments.
5 Detailed Analysis of Patents Related to Intelligent Privacy Protection and Security Systems
This section will analyze the following patents related to intelligent privacy protection and security:
"Cross-modal Randomized Privacy Protection Method Based on DIKW Modalities" (CN202110043010.3)
"Cross-modal Recommendation Method and Device for Essence Computation and Reasoning" (CN202010856960.3)
"Cross-DIKW Modal Typified Privacy Information Resource Differential Protection Method and System" (CN202110075080.7)
With the deepening of digital transformation, issues of personal privacy and data security have received increasing attention. These patents aim to optimize data processing and transmission while protecting personal privacy. They not only ensure data security but also improve the efficiency and effectiveness of data processing through advanced algorithms.
5.3.1 Cross-modal Randomized Privacy Protection Method Based on DIKW Modalities
Innovation Points:
Cross-modal Privacy Protection: This method effectively protects private information by randomizing data resources of multiple modalities, preventing malicious attackers from obtaining complete data from a single modality.
Combination of Randomization Processing and Essence Computation: By combining randomization processing with essence computation, the privacy protection effect of the data is significantly enhanced, reducing the risk of privacy leakage.
Application Scenarios:
Medical Data Privacy Protection: Ensures the security of private information and prevents the leakage of sensitive data when handling cross-modal data of patients (e.g., electronic medical records, imaging data).
Financial Transaction Privacy Protection: In the financial sector, multi-modal randomized processing of transaction data prevents transaction records from being maliciously tracked or analyzed.
5.3.2 Cross-modal Recommendation Method and Device for Essence Computation and Reasoning
Innovation Points:
Dynamic Recommendation Mechanism: This patent proposes a dynamic recommendation mechanism based on cross-modal analysis, generating personalized recommendations by analyzing users' historical behaviors and current needs.
Combination of Privacy and Reasoning Computation: This method fully considers user privacy protection needs during reasoning computation and limits the exposure scope of sensitive data through a privacy computation framework.
Application Scenarios:
Personalized Recommendations on E-commerce Platforms: Through cross-modal analysis of user behavior and preferences, e-commerce platforms can adjust recommended content in real-time, enhancing user experience while protecting user privacy.
Online Education Platforms: On education platforms, personalized learning resources are recommended based on students' progress and behavioral data while ensuring the security of their personal data.
5.3.3 Cross-DIKW Modal Typified Privacy Information Resource Differential Protection Method and System
Innovation Points:
Application of Differential Privacy Mechanism: This system uses a differential privacy mechanism to protect typified privacy information resources under DIKW modalities, ensuring that user privacy information is not leaked even during data sharing and analysis.
Multi-level Protection of Typified Resources: By categorizing typified resources, this method provides customized security protection at different levels of privacy protection, enhancing the flexibility and applicability of the system.
Application Scenarios:
Social Network Privacy Protection: Through differential protection of user privacy information in social networks, preventing the misuse or leakage of user data during social interactions.
Cloud Computing Services: In cloud computing environments, providing customized differential privacy protection services for different types of user data to ensure the security and reliability of cloud services.
5.4 Future Development Directions
Future development directions for intelligent privacy protection and security system technologies may include:
Continuous Optimization of Privacy Protection Algorithms: With the development of data analysis technology, privacy protection algorithms will continue to be optimized to meet the needs of efficient data processing while providing a higher level of privacy protection.
In-depth Research on Multi-modal Privacy Protection: Future privacy protection technologies will pay more attention to the integration and protection of cross-modal data, developing more intelligent and flexible privacy protection mechanisms.
Personalized Privacy Protection Solutions: Providing highly personalized privacy protection solutions based on user privacy needs and behavior characteristics to adapt to data processing and protection requirements in different scenarios.
6 Detailed Analysis of Patents Related to Medical and Health Management Systems
This section will analyze the following patents related to medical and health management:
"Vaccination Concentration Confirmation Method Based on DIKW Map" (CN202110830241.9)
"Cross-modal User Medical Data Analysis Method for Essence Computation" (CN202011199039.2)
"Personality Analysis and Content Push Method for Virtual Community Personnel Based on DIKW Map" (CN202110788578.8)
The field of medical and health management has developed rapidly in recent years, especially with the continuous progress of big data and artificial intelligence technologies. How to better manage and analyze patient data to provide personalized medical services has become an important research direction. These patents focus on optimizing the processing and analysis of medical data through the DIKW model to improve the quality and efficiency of medical services.
6.3.1 Vaccination Concentration Confirmation Method Based on DIKW Map
Innovation Points:
Personalized Vaccination: By establishing a DIKW map of the target user, analyzing, and calculating the suitable vaccination concentration for that user, providing a personalized vaccination plan.
Comprehensive Data Analysis: This method comprehensively considers the user's physical condition and related medical data, using information maps and knowledge maps for in-depth analysis to ensure the scientific and safety of the vaccination plan.
Application Scenarios:
Precision Medicine: Providing the most suitable vaccination concentration for different individuals during vaccination, reducing adverse reactions, and improving the effectiveness of vaccination.
Public Health Management: Using this technology to achieve personalized vaccination strategies for large-scale populations in vaccine promotion and vaccination plans, improving vaccination coverage.
6.3.2 Cross-modal User Medical Data Analysis Method for Essence Computation
Innovation Points:
Cross-modal Data Fusion: This method integrates medical information from different data sources (such as images, genetic data, electronic medical records) for cross-modal deep analysis, providing a more comprehensive health assessment for patients.
Dynamic Health Management: The system can update patients' health data in real-time, continuously optimizing medical plans through the dynamic evolution of the DIKW map.
Application Scenarios:
Chronic Disease Management: Using this technology to achieve continuous tracking and analysis of long-term health data for chronic disease patients, providing personalized treatment advice.
Preventive Medicine: Through comprehensive analysis of cross-modal data, potential health risks are identified early to assist doctors in early intervention.
6.3.3 Personality Analysis and Content Push Method for Virtual Community Personnel Based on DIKW Map
Innovation Points:
Personalized Health Service Push: This method uses the DIKW map model to analyze the behavior and personality traits of users in virtual communities and recommends personalized health services or product suggestions.
Social Health Management: By analyzing the behavior patterns of users in virtual communities, understanding their health needs, and optimizing community health management strategies.
Application Scenarios:
Health Social Platforms: On health social platforms, personalized recommendation functions help users better manage their health and enhance their health awareness.
Online Medical Services: Online health service providers can use this technology to recommend suitable health services and products to users, improving the effectiveness of health management.
6.4 Future Development Directions
The future development directions for technologies related to medical and health management systems may include the following trends:
Further Development of Precision Medicine: As the demand for personalized medicine grows, more technologies based on the DIKW model will be applied to the formulation of personalized medical plans to improve treatment outcomes.
Integration of Big Data and Artificial Intelligence: In the future, the processing and analysis of medical data will rely more on big data and artificial intelligence technologies, further improving data analysis accuracy and efficiency through more advanced models and algorithms.
Privacy Protection and Data Security: With the deepening of medical data analysis, how to protect patient privacy while ensuring data analysis effectiveness will become an important research direction.
7 Detailed Analysis of Patents Related to Cross-modal Methods and Systems for Essence Computation and Reasoning
This section will analyze the following patents related to "Cross-modal Methods and Systems for Essence Computation and Reasoning":
"Cross-modal Recommendation Method and Device for Fusion of Essence Computation and Reasoning" (CN202010856960.3)
"Cross-modal User Medical Data Analysis Method for Essence Computation" (CN202011199039.2)
"Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation and Reasoning" (CN202011104613.1)
"Cross-DIKW Modal Text Ambiguity Processing Method for Essence Computation and Reasoning" (CN202011103480.6)
"Cross-modal Feature Mining Method and Component for Essence Computation" (CN202011387490.7)
Cross-modal computation and reasoning technologies aim to provide more comprehensive solutions by combining resources from different modalities (such as Data, Information, Knowledge, etc.) for integrated analysis to better understand complex problems. These technologies have shown great potential in various fields such as medical health, privacy protection, intelligent recommendation, and more.
7.3.1 Cross-modal Recommendation Method and Device for Fusion of Essence Computation and Reasoning
Innovation Points:
Resource Fusion and Recommendation: By using a pre-established resource information library, user resource information is matched with data maps, information maps, and knowledge maps. The recommended content is dynamically adjusted based on the differential resources to ensure it meets user needs.
Dynamic Adjustment and Optimization: Dynamic optimization is performed based on the resource fusion cost library and access cost value to select the optimal recommendation resources, improving the accuracy of the recommendation system and user satisfaction.
Application Scenarios:
Personalized Recommendation Systems: In e-commerce, social networks, online education, and other fields, this technology can achieve personalized content recommendations to enhance user experience.
Intelligent Information Retrieval: In big data environments, helping users quickly find the required information and providing high-quality content recommendations.
7.3.2 Cross-modal User Medical Data Analysis Method for Essence Computation
Innovation Points:
Cross-modal Data Fusion: Integrates different types of patient medical data (such as images, genes, electronic medical records) for comprehensive health status analysis, providing personalized medical services.
Privacy Protection: During analysis, privacy protection methods for cross-modal resources are used to ensure patient privacy is not compromised while providing accurate health assessments.
Application Scenarios:
Chronic Disease Management: Analyzes long-term health data to help doctors provide personalized treatment advice to patients.
Health Prevention: Through comprehensive analysis, potential health risks are detected early for preventive intervention.
7.3.3 Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation and Reasoning
Innovation Points:
Differential Privacy Protection: By analyzing the modal transformation cost of the target resource, the optimal path is selected for modal transformation, increasing the complexity of privacy resources and ensuring effective privacy protection.
Intelligent Reasoning: In the resource protection process, intelligent reasoning technology is used to judge and optimize resource protection strategies, reducing the possibility of privacy leakage.
Application Scenarios:
Data Sharing Platforms: Ensuring user privacy is not compromised on platforms that require data sharing while maintaining data utility.
Enterprise Information Management: Ensuring the security of sensitive data within enterprises and reducing the risk of data leakage.
7.3.4 Cross-DIKW Modal Text Ambiguity Processing Method for Essence Computation and Reasoning
Innovation Points:
Disambiguation: By analyzing data resources and information resources in the text and generating condition-limited texts combined with supplementary resources, accurately identify and disambiguate the text.
Automated Reasoning: Uses automated reasoning models to analyze and judge text meanings, ensuring the accuracy of text understanding.
Application Scenarios:
Natural Language Processing: Improves the system's ability to understand user input and reduces communication misunderstandings in chatbots, intelligent customer service, etc.
Legal Document Analysis: Helps legal professionals quickly identify and eliminate ambiguities in complex legal documents.
7.3.5 Cross-modal Feature Mining Method and Component for Essence Computation
Innovation Points:
Cross-modal Feature Mining: By correlating and fusing different types of resources, high-reliability and larger quantities of features are extracted to support subsequent data processing and analysis.
Efficient Data Processing: Cross-modal feature mining technology is used to improve the efficiency and accuracy of data mining, reducing noise data interference.
Application Scenarios:
Big Data Analysis: Quickly extract valuable features from massive data, improving the precision of data analysis.
Intelligent Diagnosis: In the medical field, feature mining technology helps doctors extract key information from patients' multi-modal data for accurate diagnosis.
7.4 Future Development Directions
In the future, cross-modal technologies for essence computation and reasoning will develop in the following directions:
Deeper Modal Fusion: Explore more complex modal fusion technologies to enhance the system's ability to analyze and understand complex problems.
Real-time Intelligent Reasoning: Develop systems capable of real-time intelligent reasoning and dynamic adjustment to adapt to a wider range of application scenarios.
Efficient Privacy Protection Mechanisms: Continue to strengthen privacy protection mechanisms while ensuring data utilization, balancing the relationship between data analysis and privacy protection.
8 Cross-Modal Differential Content Recommendation and Protection Patent Detailed Analysis
This section analyzes the following patents related to "Cross-Modal Differential Content Recommendation and Protection":
"Differential Content Recommendation Method for Essence Computation Across Data, Information, and Knowledge Modalities" (CN202010693137.5)
"Task Processing Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions" (CN202011198393.3)
"Differential Protection Method and System for Typified Privacy Information Resources Across DIKW Modalities" (CN202110075080.7)
"Warning Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions" (CN202010692385.8)
"User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities" (CN202011377647.8)
With the increase in data types and sources, cross-modal content recommendation and privacy protection have become an increasingly important research area. These technologies can integrate content from different modalities (Data, Information, Knowledge, etc.) to provide more precise recommendations, while enhancing privacy protection during content transmission and usage.
8.3.1 Differential Content Recommendation Method for Essence Computation Across Data, Information, and Knowledge Modalities
Innovations:
Differential Content Recommendation: By analyzing users' personal information data tables, constructing query functions, and matching existing content in the database, personalized recommendations are provided to users. The accuracy of recommended content is improved through differential analysis methods.
Privacy Protection: This method ensures that users' private information is not disclosed during content recommendation, protecting sensitive user data through differential privacy techniques.
Application Scenarios:
E-commerce Platforms: Based on users' historical purchasing behavior and preferences, personalized products are recommended to enhance user satisfaction.
Content Aggregation Platforms: For news, videos, and other content recommendations, personalized content is provided based on user interests.
8.3.2 Task Processing Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions
Innovations:
Multi-Modal Integration for Task Processing: By comprehensive planning across multiple dimensions and modalities, task implementation resources are processed to ensure more accurate task execution.
Dynamic Adjustment: Task execution processes are adjusted in real time based on the dynamic changes in task planning resources, enhancing flexibility in task processing.
Application Scenarios:
Project Management: In complex projects, data from different departments and fields are integrated to optimize resource allocation and task execution.
Smart Manufacturing: In the manufacturing industry, multimodal data is used to finely manage production tasks, improving production efficiency.
8.3.3 Differential Protection Method and System for Typified Privacy Information Resources Across DIKW Modalities
Innovations:
Path Optimization: By calculating the support of each candidate resource path, the optimal path is selected for privacy information resource protection, enhancing resource security.
Dynamic Adjustment: The system can adjust privacy protection strategies in real time based on user behavior and environmental changes, ensuring the effectiveness of protection measures.
Application Scenarios:
Financial Services: In financial transactions, user privacy information is protected while providing an efficient service experience.
Smart City Management: In urban management, through real-time monitoring and data analysis, citizens' privacy is protected, and public service efficiency is improved.
8.3.4 Warning Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions
Innovations:
Multi-Modal Fusion Warning: By integrating various types of data resources, potential risks are comprehensively analyzed and predicted, issuing warnings in advance.
Intelligent Decision Support: The warning system can not only detect risks but also automatically generate response strategies based on warning results, supporting quick decision-making by decision-makers.
Application Scenarios:
Disaster Warning: In natural disaster warnings, multimodal data such as meteorological and seismic data are integrated to provide accurate risk predictions.
Cybersecurity: In cybersecurity management, multiple threat sources are monitored to warn of potential security vulnerabilities in advance.
8.3.5 User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities
Innovations:
Personalized Privacy Protection: According to specific user data resources, privacy data is identified and categorized to provide targeted privacy protection measures.
Differentiated Protection Strategies: Different protection strategies are adopted for different types of privacy data to minimize the risk of privacy leakage.
Application Scenarios:
Health Management: In personal health data management, differentiated privacy protection measures are provided based on different data types to ensure the security of users' health information.
Smart Home: In smart home systems, customized privacy protection solutions are provided based on the different privacy needs of family members.
8.4 Future Development Directions
In the future, cross-modal differential content recommendation and protection technologies will develop in the following directions:
More Intelligent Differential Recommendation Algorithms: Optimize recommendation algorithms through machine learning and deep learning technologies to improve the accuracy of recommended content.
Real-Time Privacy Protection Mechanisms: Develop more flexible real-time privacy protection technologies to adapt to constantly changing user behavior and environments.
Multi-Domain Application Expansion: Promote the application of these technologies in more fields, such as education, healthcare, and public safety, to further realize their potential.
9 Cross-Modal Intelligent Analysis and Prediction Technology Patent Detailed Analysis
This section analyzes the following patents related to "Cross-Modal Intelligent Analysis and Prediction Technology":
"Warning Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions" (CN202010692385.8)
"Relative Differential Privacy Protection Method for Cross-DIKW Modalities for Essence Computation" (CN202011580150.6)
"Cross-Modal Feature Mining Methods and Components for Essence Computation" (CN202011387490.7)
"Cross-DIKW Modal Text Disambiguation Processing Methods for Essence Computation and Reasoning" (CN202011103480.6)
"DIKW Privacy Resource Processing Methods and Components for Essence Computation" (CN202110043702.8)
With the development of big data, artificial intelligence, and the Internet of Things, the analysis and prediction of cross-modal data have become core requirements in various fields. By integrating data from different modalities (e.g., images, text, speech), the accuracy and generalization ability of predictive models can be improved, providing more comprehensive risk assessment and decision support.
9.3.1 Warning Methods and Components Across Data, Information, and Knowledge Modalities and Dimensions
Innovations:
Multi-Modal Data Fusion: By combining multiple data modalities, more accurate warning information is generated, suitable for risk management in complex environments.
Intelligent Prediction Model: Through the analysis of historical data, the system can detect potential risks in advance and automatically trigger warning mechanisms.
Application Scenarios:
Financial Risk Management: In financial markets, multimodal data analysis (such as market data, news, social media, etc.) is used to warn of market volatility risks in advance.
Medical Health Warning: By integrating multimodal data from patients (such as physical examination data, electronic medical records, genetic data), potential health risks are predicted and warned.
9.3.2 Relative Differential Privacy Protection Method for Cross-DIKW Modalities for Essence Computation
Innovations:
Relative Differential Privacy Strategy: A relative differential privacy protection strategy is adopted between different modal data to prevent data leakage.
Dynamic Privacy Regulation: The system can adjust privacy protection strength in real time according to the sensitivity of the data to achieve optimal protection.
Application Scenarios:
User Behavior Analysis: In user behavior data analysis, privacy protection technology ensures the security of user data while improving the accuracy of analysis results.
Personalized Recommendation: In recommendation systems, recommendation strategies are dynamically adjusted according to users' privacy needs, providing personalized services while protecting user privacy.
9.3.3 Cross-Modal Feature Mining Methods and Components for Essence Computation
Innovations:
Cross-Modal Feature Mining: By correlating analyses of cross-modal data, features hidden behind the data are mined to enhance the predictive power of models.
Automated Feature Extraction: Intelligent algorithms are used to automatically extract important features from multimodal data, reducing human intervention.
Application Scenarios:
Image Recognition: In image recognition, multimodal data such as text and voice are combined to improve recognition accuracy.
Intelligent Surveillance: By integrating multimodal data such as video and audio, a more intelligent surveillance system is realized, enhancing the ability to detect abnormal events.
9.3.4 Cross-DIKW Modal Text Disambiguation Processing Methods for Essence Computation and Reasoning
Innovations:
Disambiguation Algorithm: By combining different modalities such as Data, Information, and Knowledge, ambiguities in text are accurately identified and resolved.
Multi-Modal Information Fusion: Multimodal information is used to enhance the context understanding ability of text processing, reducing misjudgments caused by ambiguity.
Application Scenarios:
Natural Language Processing: In natural language processing tasks, this technology is applied to improve machines' understanding of complex semantics, reducing errors caused by ambiguity.
Intelligent Question Answering Systems: By fusing multimodal information, intelligent question answering systems are enhanced in understanding users' questions, providing more accurate answers.
9.3.5 DIKW Privacy Resource Processing Methods and Components for Essence Computation
Innovations:
Dynamic Processing of Privacy Resources: According to the characteristics of different modal resources, privacy protection strategies are dynamically adjusted to ensure the security of various privacy data.
Modal Transformation and Protection: The system can transform privacy resources between different modalities and provide effective protection to adapt to different usage scenarios.
Application Scenarios:
Medical Data Management: In medical data management, this technology is applied to classify and protect patients' multimodal privacy data.
Smart Home Privacy Protection: In smart home scenarios, ensure the security of users' privacy data during transmission and storage between different modalities.
9.4 Future Development Directions
In the future, cross-modal intelligent analysis and prediction technology will develop in the following directions:
Deep Learning-Driven Multi-Modal Fusion: Through more advanced deep learning algorithms, achieve deeper fusion and analysis of multimodal data.
Real-Time Intelligent Prediction: Develop systems that can predict and make decisions in real-time scenarios, adapting to rapidly changing environments.
Cross-Domain Application Expansion: Apply these technologies to more fields, such as intelligent transportation, smart cities, and environmental monitoring, to promote wide application of the technology.
10 Detailed Analysis of Patents on Intelligent Data Processing and Information Management Technology
This section will analyze the following patents related to "Intelligent Data Processing and Information Management Technology":
"Resource Identification Method Based on DIKW Map, Related Device, and Readable Medium" (CN202110431356.0)
"Essential Content Processing Method and System for Multimodal Resources Based on Common Sense Reasoning" (CN202110074301.9)
"DIKW Resource Interaction and Filling System for Purpose-driven Computation and Reasoning" (CN202111004843.5)
"User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities" (CN202011377647.8)
"Virtual Community User Personality Analysis and Content Pushing Method Based on DIKW Map" (CN202110788578.8)
"Multimodal DIKW Content Multi-Semantic Analysis Method for Essence Computation" (CN202011099503.0)
In the era of big data, the intelligentization of data processing and information management has become a crucial research direction. These technologies can significantly improve the accuracy and efficiency of information acquisition through intelligent processing of massive data while ensuring effective protection of user privacy. As an advanced framework for data processing and information management, the DIKW map demonstrates its powerful application potential in multiple fields.
10.3.1 Resource Identification Method Based on DIKW Map, Related Device, and Readable Medium
Innovation Points:
Resource Map Deduction: Conducts deep deduction of resources through the DIKW map to explore hidden relationships among resources, enhancing the comprehensiveness and accuracy of identification.
Cross-Modal Resource Integration: Integrates data, information, and knowledge resources from different modalities, achieving intelligent resource identification across fields.
Application Scenarios:
Information Retrieval: Quickly locates target information in complex information retrieval tasks through resource map deduction, improving retrieval efficiency.
Intelligent Q&A Systems: Provides more comprehensive background information for intelligent Q&A systems to ensure the accuracy and depth of answers.
10.3.2 Essential Content Processing Method and System for Multimodal Resources Based on Common Sense Reasoning
Innovation Points:
Common Sense Reasoning Mechanism: Combines intelligent processing methods of common sense reasoning to deeply process the content of multimodal resources, enhancing the system's understanding capability.
Multimodal Fusion: Processes resources across modalities, enabling the integration of data from different sources to generate high-quality content analysis results.
Application Scenarios:
Medical Data Analysis: In medical data processing, applies common sense reasoning and multimodal fusion techniques to improve diagnostic accuracy and the scientific basis for treatment planning.
Content Management Systems: Implements intelligent classification and recommendation of complex content through a common sense reasoning mechanism in content management systems.
10.3.3 DIKW Resource Interaction and Filling System for Purpose-driven Computation and Reasoning
Innovation Points:
Purpose-driven Intelligent Filling: Analyzes user purposes to intelligently fill and perfect data resources, enhancing data integrity and relevance.
Dynamic Content Adjustment: Dynamically adjusts content filling strategies based on user needs and purposes to ensure the flexibility and intelligence of system responses.
Application Scenarios:
Intelligent Form Filling: Automatically fills complex forms based on user purposes in intelligent form filling systems, improving filling efficiency and accuracy.
Personalized Content Recommendation: Achieves more precise recommendations through purpose analysis in personalized content recommendation, improving user satisfaction.
10.3.4 User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities
Innovation Points:
Differentiated Privacy Protection Strategy: Adopts differentiated privacy protection strategies based on different modalities of user data to ensure data security.
Dynamic Privacy Adjustment: The system can dynamically adjust protection measures according to user privacy needs, providing flexible privacy management solutions.
Application Scenarios:
Social Platform Privacy Protection: Protects user personal information from misuse through differentiated privacy protection measures on social platforms.
Financial Data Management: Ensures the security of user financial information by adopting dynamic privacy adjustment techniques in financial data management.
10.3.5 Virtual Community User Personality Analysis and Content Pushing Method Based on DIKW Map
Innovation Points:
User Profile Construction: Models user behaviors and preferences through the DIKW map to form comprehensive user profiles.
Personalized Content Pushing: Dynamically adjusts content pushing strategies based on user profiles to provide precise personalized services.
Application Scenarios:
E-commerce Platform Recommendation Systems: Achieves personalized product recommendations through user profile analysis on e-commerce platforms, increasing conversion rates.
Virtual Community Management: Improves community user engagement and activity by analyzing user behavior and pushing personalized content in virtual communities.
10.3.6 Multimodal DIKW Content Multi-Semantic Analysis Method for Essence Computation
Innovation Points:
Multi-Semantic Analysis: Conducts multi-semantic analysis on multimodal data to identify complex semantic structures hidden in the data, enhancing the system's understanding capability.
Cross-Modal Association: Combines multimodal information to achieve associative analysis at the semantic level, generating more precise content understanding results.
Application Scenarios:
Natural Language Processing: Improves the accuracy of text understanding through multi-semantic analysis in natural language processing tasks.
Intelligent Customer Service Systems: Provides more precise customer service by applying multimodal semantic analysis technology in intelligent customer service systems.
10.4 Future Development Directions
In the future, intelligent data processing and information management technology will develop in the following directions:
Autonomous Learning and Evolution: Develop intelligent systems with autonomous learning capabilities that can dynamically adjust data processing and information management strategies based on different scenarios.
Cross-Domain Data Fusion: Achieve multimodal data fusion across domains to enhance the synergy of data processing in different fields.
Privacy Protection and Compliance: Ensure data security while complying with privacy protection laws and regulations in various countries and regions to ensure the compliance of data usage.
11 Detailed Analysis of Patents on Intelligent Privacy Protection and Information Security Technology
This section will continue to analyze the following patents related to "Intelligent Privacy Protection and Information Security Technology":
"Hybrid Feature Machine Learning Modeling and Labeling Method Across DIKW Modalities" (CN202111674614.4)
"Metaverse Virtual Interaction Method Based on the DIKWP Model" (CN202111675871.X)
"Cross-Modal User Medical Data Analysis Method for Essence Computation" (CN202011199039.2)
"Privacy Resource Protection Method Across DIKW Modalities for Essence Computation and Reasoning" (CN202011104613.1)
"Cross-Modal Recommendation Method and Device for Essence Computation and Reasoning" (CN202010856960.3)
"Fairness-Oriented Affective Content DIKW Mapping and Transmission Method" (CN202111532716.2)
As the digitalization process accelerates, the importance of information security and privacy protection has become increasingly prominent. These patents focus on how to achieve privacy protection between different data modalities, how to enhance the effectiveness of privacy protection through intelligent technology, and how to achieve effective data utilization while protecting privacy.
11.3.1 Hybrid Feature Machine Learning Modeling and Labeling Method Across DIKW Modalities
Innovation Points:
Hybrid Feature Modeling: Combines data, information, and knowledge resources in DIKW modalities to conduct hybrid feature modeling through machine learning, enhancing the model's generalization ability and accuracy.
Intelligent Labeling Mechanism: Automates the labeling and classification of features under different modalities, reducing human intervention and improving efficiency.
Application Scenarios:
Medical Image Analysis: Improves the accuracy of lesion recognition in medical image data processing by using hybrid feature modeling.
Intelligent Recommendation Systems: Enhances the personalization capabilities of recommendation systems by intelligently labeling different user behavior features.
11.3.2 Metaverse Virtual Interaction Method Based on the DIKWP Model
Innovation Points:
DIKWP Model Mapping: Maps the DIKWP model into the metaverse environment to achieve information synchronization and interaction between the virtual and real worlds.
Privacy Protection and Openness Control: Achieves privacy protection in virtual interactions while controlling openness through the DIKWP model for personalized privacy control.
Application Scenarios:
Virtual Meetings and Social Interaction: Ensures efficient interaction experience while protecting user privacy on virtual meeting and social platforms.
Immersive Learning Environment: Provides personalized learning experiences through metaverse technology in the education field while ensuring data privacy.
11.3.3 Cross-Modal User Medical Data Analysis Method for Essence Computation
Innovation Points:
Cross-Modal Data Integration: Integrates multimodal medical data to provide comprehensive health status analysis and improve diagnostic accuracy.
Privacy Protection Mechanism: Protects medical data privacy through the DIKW map to ensure the security of patient information.
Application Scenarios:
Telemedicine: Provides accurate medical diagnoses and recommendations while protecting patient data privacy in telemedicine services.
Personalized Health Management: Offers personalized health management plans through cross-modal data analysis.
11.3.4 Privacy Resource Protection Method Across DIKW Modalities for Essence Computation and Reasoning
Innovation Points:
Privacy Protection for Essence Computation: Uses essence computation methods to protect privacy resources for data of different modalities, enhancing data security.
Dynamic Privacy Protection Mechanism: Dynamically adjusts privacy protection strategies based on the visitor's identity and data type, achieving more flexible privacy management.
Application Scenarios:
Cloud Computing Platform: Provides efficient data privacy protection services in cloud computing environments to meet different user needs.
Cross-Border Data Sharing: Ensures data compliance with local privacy laws and regulations through essence computation in cross-border data sharing.
11.3.5 Cross-Modal Recommendation Method and Device for Essence Computation and Reasoning
Innovation Points:
Multimodal Resource Fusion: Achieves precise recommendations by processing multimodal resources, improving user experience.
Intelligent Reasoning Mechanism: Combines essence computation and reasoning technology to enhance recommendation relevance and user satisfaction.
Application Scenarios:
E-commerce Platform: Provides personalized recommendations through multimodal resource analysis on e-commerce platforms to increase user purchase rates.
Online Content Recommendation: Offers highly relevant content recommendations through intelligent reasoning in online content recommendation systems.
11.4 Future Development Directions
In the future, intelligent privacy protection and information security technologies will continue to develop in the following directions:
Personalized Privacy Management: Develop more flexible privacy protection strategies to meet the personalized needs of different users.
Cross-modal Data Security: Ensure the security of data throughout the process of multimodal data fusion and processing.
Transparent and Compliant Privacy Protection: Promote the transparency and compliance of data privacy protection worldwide to meet regulatory requirements in various regions.
Detailed Analysis Table of All 90 Patents:
Patent Number | Patent Name | Innovation Points | Application Scenarios | Future Development Directions |
1 | Hybrid Feature Machine Learning Modeling and Tagging Method Across DIKW Modalities | Hybrid feature modeling, intelligent tagging mechanism | Medical image analysis, intelligent recommendation systems | Efficient feature fusion, improve AI model accuracy |
2 | Metaverse Virtual Interaction Method Based on the DIKWP Model | DIKWP model mapping, privacy protection, and openness control | Virtual conferences and social interactions, immersive learning environments | More realistic and secure metaverse interaction experience |
3 | Cross-modal User Medical Data Analysis Method for Essence Computation | Cross-modal data integration, privacy protection mechanism | Remote healthcare, personalized health management | Improve the accuracy and privacy of remote healthcare |
4 | Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation and Reasoning | Privacy protection for essence computation, dynamic privacy protection mechanism | Cloud computing platforms, cross-border data sharing | Dynamic privacy management, improve cross-border data security |
5 | Cross-modal Recommendation Method and Device for Essence Computation and Reasoning | Multimodal resource fusion, intelligent reasoning mechanism | E-commerce platforms, online content recommendation | Personalized recommendation, improve user satisfaction |
6 | Fairness-Oriented Emotional Content DIKW Mapping and Transmission Method | Emotional content mapping, fairness-oriented privacy protection | Intelligent customer service systems, social platforms | Efficient emotional communication, reduce misunderstandings |
7 | Virtual Community Personality Analysis and Content Push Method Based on DIKW Graph | User behavior modeling, personalized content push | E-commerce platforms, social media | Precise content recommendation, improve user retention rate |
8 | DIKW Model Construction Method and Device for Purpose Computation and Reasoning | Purpose reasoning model, cross-modal data integration | Intelligent assistants, automated reasoning systems | Deep reasoning and automated decision-making |
9 | Cross-DIKW Modal Text Ambiguity Handling Method for Essence Computation | Multimodal text analysis, ambiguity processing | Natural language processing, AI dialogue systems | Improve the accuracy of text understanding |
10 | Cross-modal Randomized Privacy Protection Method and System for Essence Computation and Reasoning | Randomized processing, privacy protection | Big data processing, privacy protection systems | Dynamic, intelligent privacy management |
11 | DIKW Resource Interaction Filling System for Purpose Computation and Reasoning | Interactive filling, purpose-driven | Data entry, automated data processing | Intelligent data collection and filling |
12 | Cross-modal Feature Mining Method and Component for Essence Computation | Feature mining, cross-modal fusion | Data mining, intelligent analysis | Improve the accuracy and efficiency of data mining |
13 | Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation | Privacy protection, modal conversion | Big data privacy management, intelligent analysis systems | More efficient privacy protection methods |
14 | User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities | User differential privacy protection, multimodal data integration | Social network privacy management, data sharing platforms | Differentiated privacy protection strategies, enhance user trust |
15 | Cross-modal User Medical Data Analysis Method for Essence Computation | Cross-modal medical data analysis, privacy protection | Remote diagnosis, medical data sharing | Enhance the comprehensiveness and privacy of medical data analysis |
16 | DIKW Modal Transmission and Optimization System for Purpose Computation | Purpose computation, cross-modal transmission optimization | Data transmission, intelligent network management | Improve data transmission efficiency and security |
17 | Intent Recognition Method and Device Based on DIKW Graph | DIKW graph modeling, intent recognition | Intelligent customer service systems, automated service platforms | Improve the accuracy of intent recognition |
18 | Purpose-driven Multimodal DIKW Content Transmission Method | DIKW model, purpose-driven | Data transmission, intelligent services | Dynamic transmission management, optimize user experience |
19 | Cross-modal Recommendation Method and Device for Essence Computation and Reasoning | Cross-modal recommendation, intelligent reasoning | Personalized recommendation systems, e-commerce platforms | Enhance multimodal processing capability of recommendation algorithms |
20 | Essence Content Processing Method and System for Multimodal Resources Based on Commonsense Reasoning | Commonsense reasoning, multimodal resource processing | AI systems, knowledge management platforms | Improve the commonsense processing ability of AI systems |
21 | Purpose-driven Interactive Form Filling Method for DIKW Content | Purpose-driven, automated form filling | Data collection, form automation | Improve the intelligence and accuracy of form filling |
22 | DIKW Resource Analysis Method and System for Purpose Computation and Reasoning | Resource analysis, purpose computation | Intelligent analysis systems, data mining | Optimize data analysis, enhance intelligent reasoning ability |
23 | Warning Method and Component Across Data, Information, Knowledge Modalities and Dimensions | Multimodal warning, intelligent warning systems | Security monitoring systems, intelligent warning platforms | Dynamic warning management, improve warning accuracy |
24 | Cross-modal User Medical Data Analysis Method for Essence Computation and Reasoning | Medical data analysis, privacy protection | Medical data sharing, intelligent diagnosis | Improve the accuracy and privacy of medical data analysis |
25 | Cross-DIKW Modal Privacy Resource Protection Method for Essence Computation | Privacy protection, dynamic data processing | Cloud computing platforms, privacy management | Dynamic privacy protection, adapt to changing data environments |
26 | Cross-modal User Medical Data Analysis Method for Essence Computation | Cross-modal analysis, privacy protection | Remote healthcare, medical data analysis | Enhance privacy protection and data processing capability of medical data |
27 | Multimodal DIKW Content Multi-Semantic Analysis Method for Essence Computation | Multi-semantic analysis, cross-modal content processing | Natural language processing, multimodal data analysis | Improve the accuracy of multimodal semantic analysis |
28 | Emotional Communication Method Based on DIKW Content Object | DIKW model, emotional communication | Intelligent communication systems, emotion analysis platforms | Enhance the accuracy of emotional expression and understanding |
29 | DIKW Resource Analysis Method and System for Purpose Computation and Reasoning | Purpose reasoning, resource analysis | Intelligent analysis systems, data processing | Improve the intelligence and accuracy of data analysis |
30 | Task Processing Method and Component Across Data, Information, Knowledge Modalities | Task processing, multimodal integration | Intelligent task management systems, data processing | Optimize task processing workflow, improve data integration ability |
31 | Spatial Display Combination Optimization Method for Groups | Dynamic display optimization, improve user satisfaction | Public display systems, advertising platforms | Improve display effects, enhance user interaction experience |
32 | Active Adaptation Algorithm for Angle Distance of Spatial Display Platforms | Display platform that automatically adapts to user perspective and location | VR display, public display systems | Accurate content display, improve user experience |
33 | Method for Organizing and Optimizing Personalized Network Personnel and Content | Social network content personalization optimization, recommendation engine | Social media, content recommendation systems | Increase user stickiness, optimize content recommendation |
34 | Region Definition, Display, and Recognition Method for Customizable Interaction Areas | Customizable interactive area design and display | Touch devices, UI design | Improve interaction experience, enhance user friendliness |
35 | Typified Medical Resource Processing System Design Method for Edge Computing | Edge computing optimization, efficient medical resource processing | Remote healthcare, Internet of Things | Improve processing efficiency of edge computing, reduce bandwidth requirements |
36 | Liquid Temperature Measurement, Change Simulation, and Display System in Containers | Liquid temperature monitoring and display system | Medical devices, smart containers | Improve monitoring accuracy, optimize user experience |
37 | Environmentally Friendly Interactive Cookware Customization System for Defining Healthy Cooking | Personalized cooking control and environmental design | Smart home, healthy cookware | Enhance personalization and health management of smart cooking |
38 | Interaction Cost-Driven Security Protection Method for Typified Resources | Security resource protection, dynamic cost calculation | Data protection, network security | Improve network security, optimize resource management |
39 | User Satisfaction Modeling and Display Space Adjustment Method Integrating Fairness, Experience, and Price | User satisfaction modeling, display optimization | Public display systems, service computing | Improve user satisfaction with display systems |
40 | Multi-dimensional Systematic Interaction Mechanism with Definable Privacy Ambiguity | Balance between privacy protection and user experience | Social network privacy management | Enhance user privacy protection and social experience |
41 | Multi-dimensional value-oriented object-oriented numerical computation method for purpose | Multi-dimensional computation and value evaluation | Complex system computation, intelligent decision | Improve the multi-dimensional analysis capability of numerical computation |
42 | Interactive region division and transmission optimization processing mechanism based on Data Graph, Information Graph, and Knowledge Graph | Cross-modal interaction and transmission optimization | Intelligent devices, Internet of Things | Optimize transmission mechanisms and improve interaction efficiency |
43 | Purpose-driven content filling system for adapting to competitive and cooperative intentions | Content optimization for competitive and cooperative intentions | Intelligent content management, cooperation systems | Improve the accuracy and efficiency of content filling |
44 | Intelligent reminder mechanism for matching scenarios, events, people, and purposes | Purpose matching reminders for scenarios and events | Intelligent reminder systems, personal assistants | Improve the accuracy and personalization of intelligent reminders |
45 | Content transmission modeling and processing optimization mechanism based on Data Graph, Information Graph, and Knowledge Graph | Cross-modal content transmission and modeling | Content management, intelligent transmission | Improve the accuracy and efficiency of content transmission |
46 | Value-driven purpose-oriented fusion optimization system | Value-driven optimization and integration | Intelligent control systems, resource optimization | Improve system resource utilization and user experience |
47 | Value-driven dynamic recommendation system with multi-factor dimensional space multi-scale fusion | Multi-dimensional recommendation and value-driven | Personalized recommendation systems, e-commerce | Improve the precision of recommendation systems and user satisfaction |
48 | Personalized convenient adaptive multi-level interactive region optimization configuration method | Adaptive interactive region design | Touch devices, user interface design | Improve interaction experience, enhance user-friendliness |
49 | Virtual community resource processing methods and components across DIKW modalities for essence computation | Virtual community resource optimization and processing | Virtual communities, social platforms | Improve resource processing and user engagement in virtual communities |
50 | Cross-modal user medical data analysis methods for essence computation | Cross-modal medical data analysis and privacy protection | Medical data sharing, intelligent diagnosis | Improve the accuracy of medical data analysis and privacy security |
51 | Intelligent carrier scheduling method for value exchange of Data and Information rights | Intelligent scheduling and value exchange mechanism | Logistics management, intelligent scheduling systems | Improve scheduling efficiency, optimize resource utilization |
52 | Cross-DIKW modality privacy resource protection methods for essence computation and reasoning | Privacy protection, dynamic data processing | Cloud computing platforms, privacy management | Dynamic privacy protection, adapt to changing data environments |
53 | Cross-modal recommendation methods and devices for essence computation and reasoning | Cross-modal recommendation, intelligent reasoning | Personalized recommendation systems, e-commerce platforms | Improve multi-modal processing capability of recommendation algorithms |
54 | Multi-modal privacy protection methods with technicalization of fairness, justice, and transparency | Privacy protection, multi-modal fusion | Social network privacy management, data sharing | Balance privacy protection and data sharing, enhance user trust |
55 | Cross-DIKW modality text ambiguity processing methods for essence computation and reasoning | Text ambiguity processing, multi-modal analysis | Natural language processing, AI dialogue systems | Improve the accuracy of text understanding |
56 | Essence recognition methods and components across data, information, knowledge modalities and dimensions | Essence recognition, multi-modal integration | Data recognition, intelligent analysis systems | Optimize recognition accuracy, improve data processing efficiency |
57 | Device sharing methods for data and information fusion for purpose computation and reasoning | Data and information fusion, intelligent scheduling | Logistics management, intelligent scheduling systems | Improve scheduling efficiency, optimize resource utilization |
58 | Feature mining methods and components across data, information, and knowledge multi-modalities | Feature mining, cross-modal fusion | Data mining, intelligent analysis | Improve the accuracy and efficiency of data mining |
59 | Differential content recommendation methods oriented to essence computation across data, information, and knowledge modalities | Differential recommendation, cross-modal integration | Personalized recommendation systems, data analysis | Improve the accuracy and personalization of recommended content |
60 | Task processing methods and components across data, information, knowledge modalities and dimensions | Task processing, multi-modal integration | Intelligent task management systems, data processing | Optimize task processing flows, enhance data integration capability |
61 | Differential privacy protection methods and systems for typed privacy information resources across DIKW modalities | Differential privacy protection, multi-modal integration | Data protection, privacy management systems | Improve the accuracy and security of privacy protection |
62 | Early warning methods and components across data, information, knowledge modalities and dimensions | Multi-modal early warning, intelligent early warning systems | Security monitoring systems, intelligent warning platforms | Dynamic early warning management, improve warning accuracy |
63 | Purpose-driven group differential privacy protection methods and devices for DIKW systems | Differential privacy protection, purpose-driven | Group data management, privacy protection systems | Improve the privacy protection capabilities of group data |
64 | Multi-modal DIKW content multi-semantics analysis methods for essence computation | Multi-semantics analysis, cross-modal content processing | Natural language processing, multi-modal data analysis | Improve the accuracy of multi-modal semantic analysis |
65 | User behavior content encoding and decoding methods across data, information, knowledge modalities | User behavior analysis, content encoding and decoding | User behavior analysis, content recommendation systems | Optimize user behavior analysis, improve content recommendation precision |
66 | Cross-modal randomized privacy protection methods and systems for essence computation and reasoning | Randomized processing, privacy protection | Big data processing, privacy protection systems | Dynamic, intelligent privacy management |
67 | Processing methods and components for DIKW privacy resources for essence computation | Privacy processing, modality conversion | Privacy management, big data processing | Improve the security of privacy resource processing |
68 | Resource identification methods, related devices, and readable media based on DIKW Graph | DIKW Graph modeling, resource identification | Data collection processing, intelligent system integration | Improve semantic-oriented content fusion processing coverage and efficiency |
69 | Relative differential privacy protection methods across DIKW modalities for essence computation | Relative differential privacy protection | Privacy protection, data security | Improve the accuracy and adaptability of data privacy protection |
70 | Cross-modal feature mining methods and components for essence computation | Multi-modal feature mining | Data mining, intelligent analysis | Enhance multi-modal processing capability of data mining |
71 | Carrier method and system for value exchange conversion of data and information profiles | Value conversion of data and information profiles | Big data, intelligent logistics | Improve the processing and conversion efficiency of information profiles |
72 | Essence content processing methods and systems of multi-modal resources based on common sense reasoning | Common sense reasoning, multi-modal resource processing | Data processing, intelligent systems | Optimize resource processing flow, enhance data intelligent analysis |
73 | DIKW model construction methods and devices oriented to purpose computation and reasoning | DIKW model construction, purpose-driven | Data modeling, intelligent decision systems | Enhance the intelligent decision-making capability of the DIKW model |
74 | User Differential Privacy Protection Method Across Data, Information, and Knowledge Modalities | User Differential Privacy Protection | Privacy Management, Data Security | Enhancing user privacy protection and optimizing data management |
75 | Purpose-driven Multimodal DIKW Content Transmission Method | Multimodal Content Transmission, Purpose-driven | Communication Systems, Content Transmission | Improving the efficiency and intelligence of content transmission |
76 | Affective Communication Method Based on DIKW Content Objects | Affective Communication, Content Processing | Social Networks, Affective Computing | Enhancing the accuracy of affective communication in social networks |
77 | Personality Analysis and Content Recommendation Method for Virtual Community Based on DIKW Graphs | Virtual Community Personality Analysis, Content Recommendation | Virtual Community, Personalized Recommendation | Improving user engagement and content matching in virtual communities |
78 | Fairness-oriented Affective Content DIKW Mapping and Transmission Method | Fairness-oriented, Affective Content Processing | Social Networks, Affective Computing | Enhancing fairness and accuracy of affective transmission in social networks |
79 | Blockchain Consensus Method Based on the DIKWP Model | DIKWP Model, Blockchain Consensus | Blockchain Technology, Smart Contracts | Improving blockchain consensus efficiency and optimizing smart contract execution |
80 | Cross-DIKW Modal Transmission and Optimization System Oriented to Purpose Computing | Cross-modal Transmission, Purpose Optimization | Intelligent Systems, Content Transmission | Optimizing the intelligence and efficiency of the transmission system |
81 | Personalized English Letter Display Style Transformation Method | Personalized Display, Style Transformation | Font Design, User Interface | Enhancing the personalization and aesthetics of text display |
82 | DIKW Resource Analysis Method and System Oriented to Purpose Computing and Reasoning | DIKW Resource Analysis, Purpose Reasoning | Data Analysis, Intelligent Decision-making | Improving the accuracy and reasoning ability of intelligent decision-making |
83 | DIKW Resource Interactive Filling System Oriented to Purpose Computing | DIKW Resource Filling, Purpose Computing | Data Management, Automatic Form Filling | Optimizing the intelligence and user experience of form filling |
84 | Affective Expression Mapping, Measurement, and Optimization Transmission System for DIKW Resources | Affective Expression, DIKW Resource Mapping | Social Networks, Affective Computing | Enhancing the accuracy and effectiveness of affective transmission |
85 | Cross-DIKW Modal Text Ambiguity Processing Method for Essence Computation and Reasoning (Canada) | Text Ambiguity Processing, International Patent | Natural Language Processing, Cross-border Applications | Promoting global text processing applications and enhancing international support |
86 | Vehicle Path Planning Method Based on DIKW | DIKW Path Planning, Intelligent Navigation | Intelligent Transportation, Autonomous Driving | Improving the intelligence and efficiency of path planning |
87 | DIKW Content Processing Method and System Based on Purpose-driven Approach | DIKW Content Processing, Purpose-driven | Content Management, Data Processing | Optimizing the intelligence and efficiency of content processing |
88 | Regional Awareness and Passage Guidance Method Based on DIKW | Regional Awareness, Intelligent Passage Guidance | Intelligent Transportation, Regional Management | Enhancing regional awareness and improving intelligent traffic management |
89 | The Recommendation Method of Hybrid Computation and Reasoning Across Data, Information and Knowledge (Australia) | Multimodal Feature Mining, International Patent | Data Mining, International Applications | Promoting the development of global data mining technology |
90 | Purpose-driven Interactive Form Filling Method for DIKW Content | Interactive Form Filling, Purpose-driven | Data Filling, Intelligent Forms | Optimizing the intelligence and user experience of form filling |
This table covers a detailed analysis of all 90 invention patents, showcasing the innovation points, application scenarios, and future development directions of each patent. It is hoped that this format will help better understand the uniqueness of each patent, as well as its value in practical applications and future potential.
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