博文

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 Analysis of Innovations

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

2.1 Patent List

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)

2.2 Background Introduction

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 Analysis of Innovations

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

3.1 Patent List

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)

3.2 Background Introduction

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 Analysis of Innovations

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

4.1 Patent List

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)

4.2 Background Introduction

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 Analysis of Innovations

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

5.1 Patent List

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)

5.2 Background Introduction

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 Innovation Point Analysis

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

6.1 Patent List

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)

6.2 Background Introduction

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 Innovation Point Analysis

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

7.1 Patent List

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)

7.2 Background Introduction

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 Innovation Point Analysis

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

8.1 Patent List

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)

8.2 Background Introduction

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 Innovation Analysis

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

9.1 Patent List

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)

9.2 Background Introduction

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 Innovation Analysis

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

10.1 Patent List

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)

10.2 Background Introduction

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 Analysis of Innovations

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

11.1 Patent List

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)

11.2 Background Introduction

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 Analysis of Innovations

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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