system

Abstract

The system according to this embodiment aims to optimize the method of conducting health guidance based on the results of health checkups. [Solution] The system according to the embodiment comprises an anonymization unit, a pattern extraction unit, an analysis unit, and a provision unit. The anonymization unit anonymizes the results of the health checkup. The pattern extraction unit analyzes the data anonymized by the anonymization unit and extracts elements that have a decisive influence on the content and results of health guidance. The analysis unit optimizes the method of conducting health guidance based on the elements extracted by the pattern extraction unit. The provision unit provides the method of conducting health guidance optimized by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the method of conducting health guidance based on the results of a health check is uniform, and optimal guidance according to individual situations is not performed.

[0005] The system according to the embodiment aims to optimize the method of conducting health guidance based on the results of a health check.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an anonymization unit, a pattern extraction unit, an analysis unit, and a provision unit. The anonymization unit anonymizes the results of health checkups. The pattern extraction unit analyzes the data anonymized by the anonymization unit and extracts elements that have a decisive influence on the content and results of health guidance. The analysis unit optimizes the method of conducting health guidance based on the elements extracted by the pattern extraction unit. The provision unit provides the method of conducting health guidance optimized by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the method of health guidance based on the results of health checkups. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health guidance support system according to an embodiment of the present invention is a system that anonymizes the results of health checkups and identifies elements that decisively influence the content and results of health guidance through pattern extraction by a generative AI. This health guidance support system identifies individuals at risk from the results of health checkups and anonymizes the records of their specific health guidance. Next, it analyzes the anonymized records using a generative AI and extracts elements that decisively influence the content and results of health guidance. Furthermore, it optimizes the method of conducting health guidance based on the patterns and trends extracted by the generative AI. For example, if a particular guidance method is effective for a particular risk group, that method can be recommended. This maximizes the effectiveness of specific health guidance. In addition, based on the analysis results by the generative AI, it advises medical institutions, companies, and health insurance associations that conduct health guidance on effective guidance and outreach methods. This system is an independent system with robust information security and analyzes specific health guidance information managed by each health insurance association, company, etc. This allows for the proposal of effective guidance methods based on actual initiatives (successful and unsuccessful examples). This allows the health guidance support system to anonymize health checkup results and identify factors that have a decisive impact on the content and results of health guidance through pattern extraction using generating AI.

[0029] The health guidance support system according to the embodiment comprises an anonymization unit, a pattern extraction unit, an analysis unit, and a provision unit. The anonymization unit anonymizes the results of health checkups. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. The anonymization unit completely deletes personally identifiable information by deleting data. The anonymization unit hides personally identifiable information by masking. The anonymization unit replaces personally identifiable information with other information by performing pseudo-anonymization. The pattern extraction unit analyzes the anonymized data using a generation AI and extracts elements that have a decisive impact on the content and results of health guidance. The pattern extraction unit extracts patterns using methods such as detection of frequently occurring patterns and correlation analysis. The pattern extraction unit identifies patterns that frequently appear in the data by detecting frequently occurring patterns. The pattern extraction unit clarifies the correlation between data by performing correlation analysis. Some or all of the above processing in the pattern extraction unit is performed using a generation AI. The analysis unit optimizes the method of health guidance based on the extracted elements. The analysis unit performs optimization using methods such as algorithm selection and evaluation criteria. The analysis unit determines the optimal method of implementation by selecting an algorithm. The analysis unit evaluates the effectiveness of the implementation method using evaluation criteria. The provision unit provides the optimized method of implementation to medical institutions, companies, and health insurance associations that provide health guidance. The provision unit provides information using methods such as digital distribution and distribution of printed materials. The provision unit provides information quickly by digital distribution. The provision unit provides information in paper format by distributing printed materials. As a result, the health guidance support system according to the embodiment can anonymize the results of health checkups and identify elements that have a decisive impact on the content and results of health guidance through pattern extraction by generating AI.

[0030] The anonymization unit anonymizes the results of health checkups. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. Specifically, data deletion completely removes personally identifiable information. For example, personally identifiable information such as names, addresses, and phone numbers is completely removed from the dataset. When masking is performed, specific data fields are replaced with random strings or specific symbols to hide personally identifiable information. For example, by changing the name field to "XXXX", personal identification becomes impossible. When pseudo-anonymization is performed, personally identifiable information is replaced with other information. For example, by replacing names with randomly generated IDs, it is made impossible to directly link them to the original personal information. This allows the anonymization unit to maintain the usefulness of the data while minimizing the risk of personal information leakage. Furthermore, the anonymization unit records logs of the anonymization process and stores them in a verifiable format for later reference. This allows verification that the anonymization process was performed appropriately. In addition, the anonymization unit regularly updates its algorithms and methods to comply with the latest privacy protection technologies and legal regulations. This allows the anonymization unit to consistently provide a high level of privacy protection and ensure data security.

[0031] The pattern extraction unit analyzes anonymized data using generative AI and extracts elements that have a decisive impact on the content and results of health guidance. Specifically, it extracts patterns using methods such as frequent pattern detection and correlation analysis. By detecting frequent patterns, it identifies patterns that appear frequently in the data. For example, it can identify health risk factors that are commonly observed in specific age groups or genders. By performing correlation analysis, it clarifies the correlation between data. For example, if there is a strong correlation between a particular lifestyle habit and health checkup results, it can evaluate the impact of that lifestyle habit on health. Some or all of the above processing in the pattern extraction unit is performed using generative AI. Generative AI has the ability to analyze large amounts of data quickly and accurately, and can find complex patterns and correlations. For example, generative AI uses natural language processing technology to analyze text data of health checkup results and extract important keywords and phrases. Generative AI also uses machine learning algorithms to cluster and classify data and clarify the characteristics between different groups. As a result, the pattern extraction unit can efficiently extract elements that have a decisive impact on the content and results of health guidance, thereby improving the quality of health guidance. Furthermore, the pattern extraction unit visualizes the extracted patterns and correlations, providing them in a format easily understandable to medical professionals and health educators. This allows the pattern extraction unit to provide crucial information for maximizing the effectiveness of health guidance.

[0032] The analysis unit optimizes the health guidance process based on the extracted elements. Specifically, optimization is performed using methods such as algorithm selection and evaluation criteria. By selecting an algorithm, the optimal process is determined. For example, based on patterns extracted by the generative AI, the optimal guidance method for individual health risks is selected. The effectiveness of the process is evaluated using evaluation criteria. For example, the effectiveness of the guidance method is quantitatively evaluated based on changes in health checkup results before and after health guidance, and feedback from individuals who received guidance. Based on these evaluation results, the analysis unit continuously improves the guidance method. Furthermore, the analysis unit can optimize the health guidance process in real time using the generative AI. For example, data collected during guidance can be immediately analyzed, and the guidance content can be adjusted on the spot. This allows the analysis unit to provide flexible guidance tailored to individual circumstances and maximize the effectiveness of health guidance. In addition, the analysis unit analyzes trends and patterns in health guidance based on long-term data and provides information for formulating future guidance policies. This allows the analysis unit to continuously improve the quality of health guidance.

[0033] The information provision department provides optimized guidance methods to healthcare institutions, companies, and health insurance associations that conduct health guidance. Specifically, it provides information using methods such as digital distribution and printed materials. Digital distribution allows for rapid information delivery. For example, optimized guidance methods and related materials are sent to healthcare institutions and companies via email or a dedicated web portal. This allows stakeholders to receive the latest information in real time. Printed materials provide information in paper format. For example, detailed guidance manuals and guidelines are printed and distributed to healthcare institutions and companies. This ensures that necessary information is provided even if access to a digital environment is unavailable. Furthermore, the information provision department collects confirmation of receipt of the provided information and feedback, and evaluates the effectiveness of the information provision. For example, it improves the content and methods of provision based on feedback from healthcare institutions and companies that received the information. The information provision department can also respond quickly when updates or additions to information are necessary. In this way, the information provision department can always provide stakeholders with the latest and most optimal health guidance methods, maximizing the effectiveness of health guidance.

[0034] The anonymization unit can analyze anonymized data using generative AI and extract elements that have a decisive impact on the content and results of health guidance. For example, the anonymization unit can analyze data patterns using generative AI to identify elements that influence the content and results of health guidance. The anonymization unit can also analyze data correlations using generative AI to extract elements that influence the content and results of health guidance. The anonymization unit can also analyze data trends using generative AI to identify elements that influence the content and results of health guidance. As a result, by using generative AI, elements that have a decisive impact on the content and results of health guidance can be extracted with high accuracy. Some or all of the above processing in the anonymization unit is performed using generative AI.

[0035] The analysis unit can optimize the method of health guidance based on the extracted elements. For example, the analysis unit can determine the optimal method of health guidance based on the extracted elements. The analysis unit can also adjust the method of health guidance based on the extracted elements. The analysis unit can also improve the method of health guidance based on the extracted elements. By optimizing the method of health guidance based on the extracted elements, the effectiveness of health guidance can be maximized. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can optimize the method of health guidance using an AI model that takes the extracted elements as input and outputs the optimal method.

[0036] The service provider can provide optimized guidance methods to medical institutions, companies, and health insurance associations that conduct health guidance. The service provider can provide optimized guidance methods quickly, for example, by digital distribution. The service provider can also provide optimized guidance methods in paper format by distributing printed materials. The service provider can also provide optimized guidance methods via email. By providing optimized guidance methods, the effectiveness of health guidance can be improved. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide optimized guidance methods using an AI model that takes optimized guidance methods as input and outputs guidance methods.

[0037] The anonymization unit can apply different anonymization algorithms depending on the type of data when anonymizing health check results. For example, the anonymization unit can apply an anonymization algorithm with a specific pattern to blood test results to maintain data consistency. The anonymization unit can also apply an image-specific anonymization algorithm to image data to prevent the identification of individuals. The anonymization unit can also apply an anonymization algorithm using natural language processing to text data to automatically mask parts containing personal information. This allows for anonymization while maintaining data consistency by applying different anonymization algorithms depending on the type of data. Different anonymization algorithms can be implemented using techniques such as k-anonymization and l-diversity. Some or all of the above-described processes in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input blood test result data into a generating AI and have the generating AI execute an anonymization algorithm with a specific pattern.

[0038] The anonymization unit can adjust the level of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can apply a more stringent anonymization algorithm to high-importance data to minimize the risk of personal information leakage. The anonymization unit can also apply a simplified anonymization algorithm to low-importance data to improve processing speed. The anonymization unit can also apply a balanced anonymization algorithm to medium-importance data, taking both security and processing speed into consideration. This minimizes the risk of personal information leakage by adjusting the level of anonymization based on the importance of the data. Data importance is evaluated using criteria such as data confidentiality and frequency of use. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the level of anonymization using an AI model that evaluates data importance.

[0039] The anonymization unit can adjust the order of anonymization based on the data submission date during the anonymization process. For example, the anonymization unit can prioritize anonymizing the most recent data and send it for analysis quickly. The anonymization unit can also postpone processing older data and prioritize the most recent data. The anonymization unit can also group data with similar submission dates and anonymize them all at once. This allows the latest data to be sent for analysis quickly by adjusting the order of anonymization based on the data submission date. The data submission date is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the order of anonymization using an AI model that evaluates the data submission date.

[0040] The anonymization unit can perform anonymization while considering the geographical distribution of the data during the anonymization process. For example, the anonymization unit can group geographically close data and anonymize them all at once. The anonymization unit can also anonymize geographically distant data separately, taking into account the characteristics of each region. The anonymization unit can also apply different anonymization algorithms based on the geographical distribution. This makes it possible to perform anonymization that takes into account the characteristics of each region by considering the geographical distribution of the data. The geographical distribution is evaluated using criteria such as the data distribution by region or geographical clusters. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the anonymization method using an AI model that evaluates the geographical distribution.

[0041] The pattern extraction unit can improve the accuracy of pattern extraction by considering the interrelationships between data. For example, the pattern extraction unit can analyze the correlations between data and extract highly relevant patterns. The pattern extraction unit can also consider the interdependence of data and integrate multiple datasets to extract patterns. The pattern extraction unit can also visualize the interrelationships of data to facilitate understanding of the extracted patterns. This improves the accuracy of extraction by considering the interrelationships of data. The interrelationships of data are evaluated using criteria such as correlation coefficients or co-occurrence networks. Some or all of the above processing in the pattern extraction unit may be performed using generative AI, or not. For example, the pattern extraction unit can improve the accuracy of pattern extraction by using a generative AI model that evaluates the correlations of data.

[0042] The pattern extraction unit can apply different extraction algorithms to each data category during pattern extraction. For example, the pattern extraction unit can apply an extraction algorithm based on specific health indicators to health checkup result data. The pattern extraction unit can also apply an extraction algorithm based on the content of health guidance to health guidance record data. The pattern extraction unit can also apply an extraction algorithm based on lifestyle patterns to lifestyle data. By applying different extraction algorithms to each data category, the accuracy of extraction is improved. Different extraction algorithms can be implemented using techniques such as clustering or classification algorithms. Some or all of the above processing in the pattern extraction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pattern extraction unit can input health checkup result data into a generative AI and have the generative AI execute an extraction algorithm based on specific health indicators.

[0043] The pattern extraction unit can determine the extraction priority based on the data submission date during pattern extraction. For example, the pattern extraction unit can prioritize the extraction of the latest data and send it for analysis quickly. The pattern extraction unit can also postpone processing older data and prioritize the processing of the latest data. The pattern extraction unit can also group data with similar submission dates and extract them all at once. This allows the latest data to be sent for analysis quickly by determining the extraction priority based on the data submission date. The data submission date is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the pattern extraction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the pattern extraction unit can determine the extraction priority using a generative AI model that evaluates the data submission date.

[0044] The pattern extraction unit can improve the accuracy of pattern extraction by referring to relevant literature for the data. For example, the pattern extraction unit evaluates the reliability of the extracted patterns based on the relevant literature. The pattern extraction unit can also optimize the extraction algorithm by incorporating insights from the relevant literature. The pattern extraction unit can also supplement the interpretation of the extracted patterns by referring to relevant literature. This improves the accuracy of extraction by referring to relevant literature for the data. Relevant literature is evaluated using criteria such as the use of literature databases or the analysis of citation relationships. Some or all of the above processing in the pattern extraction unit may be performed using generative AI or not. For example, the pattern extraction unit can improve the accuracy of extraction by using a generative AI model to evaluate relevant literature.

[0045] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the algorithm by feeding back past analysis results. The analysis unit can also evaluate the reliability of the analysis by referring to past analysis data. This improves the accuracy of the analysis algorithm by referring to past analysis data. Past analysis data is evaluated using criteria such as the use of a database or statistical analysis of past analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can optimize the analysis algorithm using an AI model that evaluates past analysis data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, analyzing even the smallest details. For low-importance data, the analysis unit can perform a simplified analysis to improve processing speed. For medium-importance data, the analysis unit can perform a balanced analysis, considering both security and processing speed. This improves the accuracy of the analysis by adjusting the level of detail based on the importance of the data. Data importance is evaluated using criteria such as data confidentiality and frequency of use. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates data importance.

[0047] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can group geographically close data and perform analysis all at once. The analysis unit can also analyze geographically distant data separately and consider the characteristics of each region. The analysis unit can also apply different analysis algorithms based on the geographical distribution. This makes it possible to perform analysis that takes regional characteristics into account by considering the geographical distribution of the data. Geographical distribution is evaluated using criteria such as the data distribution by region or geographical clusters. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the analysis method using an AI model that evaluates geographical distribution.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis process. For example, the analysis unit can evaluate the reliability of the analysis results based on relevant market data. The analysis unit can also optimize its analysis algorithm by incorporating insights from relevant market data. The analysis unit can also supplement the interpretation of the analysis results by referring to relevant market data. This improves the accuracy of the analysis by referring to relevant market data. Relevant market data is evaluated using criteria such as the use of market research data or competitive analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of its analysis by using an AI model to evaluate relevant market data.

[0049] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider may provide detailed explanations for high-importance information. The provider may also provide concise explanations for low-importance information. The provider may also provide a balanced level of detail for moderate-importance information. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. The importance of information is evaluated using criteria such as the confidentiality of the information and the frequency of use. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can adjust the level of detail using an AI model that evaluates the importance of information.

[0050] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the information provider can apply a provision algorithm based on specific health indicators to health checkup results. The information provider can also apply a provision algorithm based on the content of health guidance to health guidance records. The information provider can also apply a provision algorithm based on lifestyle patterns to lifestyle information. By applying different information provision algorithms depending on the category of information, it becomes possible to provide more appropriate information. Different information provision algorithms are implemented using technologies such as recommendation algorithms and filtering algorithms. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input health checkup results into a generating AI and have the generating AI execute a provision algorithm based on specific health indicators.

[0051] The information delivery unit can adjust the order of delivery based on the timing of information submission. For example, the delivery unit can prioritize the delivery of the latest information and send it for analysis quickly. The delivery unit can also postpone older information and prioritize the processing of the latest information. The delivery unit can also group information with similar submission times and deliver it all at once. This allows for the rapid delivery of the latest information by adjusting the order of delivery based on the timing of information submission. The timing of information submission is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can adjust the order of delivery using an AI model that evaluates the timing of information submission.

[0052] The information provider can improve the accuracy of its provision by referring to relevant literature at the time of provision. For example, the provider can evaluate the reliability of the provided information based on relevant literature. The provider can also optimize its provision algorithm by incorporating insights from relevant literature. The provider can also supplement the interpretation of the provided information by referring to relevant literature. This improves the accuracy of provision by referring to relevant literature. Relevant literature is evaluated using criteria such as the use of literature databases and the analysis of citation relationships. Some or all of the above processes in the information provider may be performed using AI or not. For example, the provider can improve the accuracy of provision by using an AI model to evaluate relevant literature.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] A health guidance support system can collect users' lifestyle data and optimize the content of health guidance based on this data. For example, it can analyze users' meal records and suggest improvements to nutritional balance. It can also analyze users' exercise records and provide appropriate exercise plans. It can analyze users' sleep records and provide advice to improve sleep quality. By optimizing the content of health guidance based on users' lifestyle data, the effectiveness of the guidance can be maximized. Lifestyle data can be collected using, for example, wearable devices or smartphone applications. Some or all of the above-described processes in the health guidance support system may be performed using AI or not. For example, a health guidance support system can optimize the content of health guidance using an AI model that collects and analyzes users' lifestyle data.

[0055] The health guidance support system can analyze a user's health checkup results in real time and provide immediate health guidance. For example, it can provide specific guidance for high-risk items immediately after the health checkup results are released. It can also immediately suggest improvements to diet and exercise based on the health checkup results. It can also recommend visits to necessary medical institutions based on the health checkup results. This maximizes the effectiveness of guidance by analyzing health checkup results in real time and providing immediate health guidance. Real-time analysis is achieved, for example, using cloud computing or high-speed data processing technology. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, the health guidance support system can provide health guidance using an AI model that analyzes health checkup results in real time.

[0056] A health guidance support system can monitor users' health data over the long term, analyze trends, and optimize the content of health guidance. For example, it can analyze long-term fluctuations in a user's weight and blood pressure to provide appropriate guidance. It can also analyze changes in a user's exercise habits and adjust their exercise plan. It can analyze changes in a user's eating habits and suggest improvements to their nutritional balance. In this way, by monitoring users' health data over the long term and analyzing trends, the effectiveness of guidance can be maximized. Long-term monitoring is performed using, for example, wearable devices or smartphone apps. Some or all of the above-described processes in the health guidance support system may be performed using AI or not. For example, a health guidance support system can optimize the content of health guidance using an AI model that monitors users' health data over the long term and analyzes trends.

[0057] A health guidance support system can compare a user's health data with that of other users and provide benchmarks. For example, a user can compare their health data with that of other users of the same age group to understand their own health status. They can also compare their health data with regional health data to understand region-specific health risks. They can also compare their health data with occupation-specific health risks to understand occupation-specific health risks. This allows users to objectively evaluate their own health status by comparing their health data with that of other users. The collection of comparative data is carried out using, for example, databases or statistical data. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, a health guidance support system can evaluate a user's health status using an AI model that compares a user's health data with that of other users and provides benchmarks.

[0058] The health guidance support system can analyze users' health data, predict seasonal health risks, and provide health guidance accordingly. For example, in winter, the risk of influenza increases, so it can recommend vaccination and advise thorough handwashing. In summer, the risk of heatstroke increases, so it can recommend hydration and adequate rest. In spring, the risk of hay fever increases, so it can provide guidance on allergy countermeasures. By predicting seasonal health risks and providing health guidance accordingly, the effectiveness of the guidance can be maximized. Seasonal health risk predictions are made using, for example, historical data and weather data. Some or all of the above processing in the health guidance support system may be performed using AI, or not. For example, the health guidance support system can provide health guidance using an AI model that predicts seasonal health risks.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The anonymization unit anonymizes the results of the health checkup. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. Data deletion completely removes personally identifiable information, masking hides personally identifiable information, and pseudo-anonymization replaces personally identifiable information with other information. Step 2: The pattern extraction unit analyzes the anonymized data using generation AI and extracts elements that have a decisive impact on the content and results of health guidance. The pattern extraction unit extracts patterns using methods such as detection of frequent patterns and correlation analysis. Detection of frequent patterns identifies patterns that appear frequently in the data, and correlation analysis reveals the correlations between data. Step 3: The analysis unit optimizes the health guidance process based on the extracted elements. The analysis unit performs optimization using methods such as algorithm selection and evaluation criteria. The optimal process is determined by algorithm selection, and the effectiveness of the process is evaluated using evaluation criteria. Step 4: The service provider provides the optimized implementation method to healthcare institutions, companies, and health insurance associations that conduct health guidance. The service provider delivers information using methods such as digital distribution and printed materials. Information is delivered quickly through digital distribution and in paper format through printed materials.

[0061] (Example of form 2) The health guidance support system according to an embodiment of the present invention is a system that anonymizes the results of health checkups and identifies elements that decisively influence the content and results of health guidance through pattern extraction by a generative AI. This health guidance support system identifies individuals at risk from the results of health checkups and anonymizes the records of their specific health guidance. Next, it analyzes the anonymized records using a generative AI and extracts elements that decisively influence the content and results of health guidance. Furthermore, it optimizes the method of conducting health guidance based on the patterns and trends extracted by the generative AI. For example, if a particular guidance method is effective for a particular risk group, that method can be recommended. This maximizes the effectiveness of specific health guidance. In addition, based on the analysis results by the generative AI, it advises medical institutions, companies, and health insurance associations that conduct health guidance on effective guidance and outreach methods. This system is an independent system with robust information security and analyzes specific health guidance information managed by each health insurance association, company, etc. This allows for the proposal of effective guidance methods based on actual initiatives (successful and unsuccessful examples). This allows the health guidance support system to anonymize health checkup results and identify factors that have a decisive impact on the content and results of health guidance through pattern extraction using generating AI.

[0062] The health guidance support system according to the embodiment comprises an anonymization unit, a pattern extraction unit, an analysis unit, and a provision unit. The anonymization unit anonymizes the results of health checkups. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. The anonymization unit completely deletes personally identifiable information by deleting data. The anonymization unit hides personally identifiable information by masking. The anonymization unit replaces personally identifiable information with other information by performing pseudo-anonymization. The pattern extraction unit analyzes the anonymized data using a generation AI and extracts elements that have a decisive impact on the content and results of health guidance. The pattern extraction unit extracts patterns using methods such as detection of frequently occurring patterns and correlation analysis. The pattern extraction unit identifies patterns that frequently appear in the data by detecting frequently occurring patterns. The pattern extraction unit clarifies the correlation between data by performing correlation analysis. Some or all of the above processing in the pattern extraction unit is performed using a generation AI. The analysis unit optimizes the method of health guidance based on the extracted elements. The analysis unit performs optimization using methods such as algorithm selection and evaluation criteria. The analysis unit determines the optimal method of implementation by selecting an algorithm. The analysis unit evaluates the effectiveness of the implementation method using evaluation criteria. The provision unit provides the optimized method of implementation to medical institutions, companies, and health insurance associations that provide health guidance. The provision unit provides information using methods such as digital distribution and distribution of printed materials. The provision unit provides information quickly by digital distribution. The provision unit provides information in paper format by distributing printed materials. As a result, the health guidance support system according to the embodiment can anonymize the results of health checkups and identify elements that have a decisive impact on the content and results of health guidance through pattern extraction by generating AI.

[0063] The anonymization unit anonymizes the results of health checkups. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. Specifically, data deletion completely removes personally identifiable information. For example, personally identifiable information such as names, addresses, and phone numbers is completely removed from the dataset. When masking is performed, specific data fields are replaced with random strings or specific symbols to hide personally identifiable information. For example, by changing the name field to "XXXX", personal identification becomes impossible. When pseudo-anonymization is performed, personally identifiable information is replaced with other information. For example, by replacing names with randomly generated IDs, it is made impossible to directly link them to the original personal information. This allows the anonymization unit to maintain the usefulness of the data while minimizing the risk of personal information leakage. Furthermore, the anonymization unit records logs of the anonymization process and stores them in a verifiable format for later reference. This allows verification that the anonymization process was performed appropriately. In addition, the anonymization unit regularly updates its algorithms and methods to comply with the latest privacy protection technologies and legal regulations. This allows the anonymization unit to consistently provide a high level of privacy protection and ensure data security.

[0064] The pattern extraction unit analyzes anonymized data using generative AI and extracts elements that have a decisive impact on the content and results of health guidance. Specifically, it extracts patterns using methods such as frequent pattern detection and correlation analysis. By detecting frequent patterns, it identifies patterns that appear frequently in the data. For example, it can identify health risk factors that are commonly observed in specific age groups or genders. By performing correlation analysis, it clarifies the correlation between data. For example, if there is a strong correlation between a particular lifestyle habit and health checkup results, it can evaluate the impact of that lifestyle habit on health. Some or all of the above processing in the pattern extraction unit is performed using generative AI. Generative AI has the ability to analyze large amounts of data quickly and accurately, and can find complex patterns and correlations. For example, generative AI uses natural language processing technology to analyze text data of health checkup results and extract important keywords and phrases. Generative AI also uses machine learning algorithms to cluster and classify data and clarify the characteristics between different groups. As a result, the pattern extraction unit can efficiently extract elements that have a decisive impact on the content and results of health guidance, thereby improving the quality of health guidance. Furthermore, the pattern extraction unit visualizes the extracted patterns and correlations, providing them in a format easily understandable to medical professionals and health educators. This allows the pattern extraction unit to provide crucial information for maximizing the effectiveness of health guidance.

[0065] The analysis unit optimizes the health guidance process based on the extracted elements. Specifically, optimization is performed using methods such as algorithm selection and evaluation criteria. By selecting an algorithm, the optimal process is determined. For example, based on patterns extracted by the generative AI, the optimal guidance method for individual health risks is selected. The effectiveness of the process is evaluated using evaluation criteria. For example, the effectiveness of the guidance method is quantitatively evaluated based on changes in health checkup results before and after health guidance, and feedback from individuals who received guidance. Based on these evaluation results, the analysis unit continuously improves the guidance method. Furthermore, the analysis unit can optimize the health guidance process in real time using the generative AI. For example, data collected during guidance can be immediately analyzed, and the guidance content can be adjusted on the spot. This allows the analysis unit to provide flexible guidance tailored to individual circumstances and maximize the effectiveness of health guidance. In addition, the analysis unit analyzes trends and patterns in health guidance based on long-term data and provides information for formulating future guidance policies. This allows the analysis unit to continuously improve the quality of health guidance.

[0066] The information provision department provides optimized guidance methods to healthcare institutions, companies, and health insurance associations that conduct health guidance. Specifically, it provides information using methods such as digital distribution and printed materials. Digital distribution allows for rapid information delivery. For example, optimized guidance methods and related materials are sent to healthcare institutions and companies via email or a dedicated web portal. This allows stakeholders to receive the latest information in real time. Printed materials provide information in paper format. For example, detailed guidance manuals and guidelines are printed and distributed to healthcare institutions and companies. This ensures that necessary information is provided even if access to a digital environment is unavailable. Furthermore, the information provision department collects confirmation of receipt of the provided information and feedback, and evaluates the effectiveness of the information provision. For example, it improves the content and methods of provision based on feedback from healthcare institutions and companies that received the information. The information provision department can also respond quickly when updates or additions to information are necessary. In this way, the information provision department can always provide stakeholders with the latest and most optimal health guidance methods, maximizing the effectiveness of health guidance.

[0067] The anonymization unit can analyze anonymized data using generative AI and extract elements that have a decisive impact on the content and results of health guidance. For example, the anonymization unit can analyze data patterns using generative AI to identify elements that influence the content and results of health guidance. The anonymization unit can also analyze data correlations using generative AI to extract elements that influence the content and results of health guidance. The anonymization unit can also analyze data trends using generative AI to identify elements that influence the content and results of health guidance. As a result, by using generative AI, elements that have a decisive impact on the content and results of health guidance can be extracted with high accuracy. Some or all of the above processing in the anonymization unit is performed using generative AI.

[0068] The analysis unit can optimize the method of health guidance based on the extracted elements. For example, the analysis unit can determine the optimal method of health guidance based on the extracted elements. The analysis unit can also adjust the method of health guidance based on the extracted elements. The analysis unit can also improve the method of health guidance based on the extracted elements. By optimizing the method of health guidance based on the extracted elements, the effectiveness of health guidance can be maximized. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can optimize the method of health guidance using an AI model that takes the extracted elements as input and outputs the optimal method.

[0069] The service provider can provide optimized guidance methods to medical institutions, companies, and health insurance associations that conduct health guidance. The service provider can provide optimized guidance methods quickly, for example, by digital distribution. The service provider can also provide optimized guidance methods in paper format by distributing printed materials. The service provider can also provide optimized guidance methods via email. By providing optimized guidance methods, the effectiveness of health guidance can be improved. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide optimized guidance methods using an AI model that takes optimized guidance methods as input and outputs guidance methods.

[0070] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated emotions. For example, if the user is feeling anxious, the anonymization unit can perform more rigorous data anonymization to minimize the risk of personal information leakage. If the user is relaxed, the anonymization unit can also simplify the anonymization process and process the data quickly. If the user is in a hurry, the anonymization unit can prioritize the speed of anonymization and process the data quickly. This minimizes the risk of personal information leakage by adjusting the anonymization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The anonymization unit can apply different anonymization algorithms depending on the type of data when anonymizing health check results. For example, the anonymization unit can apply an anonymization algorithm with a specific pattern to blood test results to maintain data consistency. The anonymization unit can also apply an image-specific anonymization algorithm to image data to prevent the identification of individuals. The anonymization unit can also apply an anonymization algorithm using natural language processing to text data to automatically mask parts containing personal information. This allows for anonymization while maintaining data consistency by applying different anonymization algorithms depending on the type of data. Different anonymization algorithms can be implemented using techniques such as k-anonymization and l-diversity. Some or all of the above-described processes in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input blood test result data into a generating AI and have the generating AI execute an anonymization algorithm with a specific pattern.

[0072] The anonymization unit can adjust the level of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can apply a more stringent anonymization algorithm to high-importance data to minimize the risk of personal information leakage. The anonymization unit can also apply a simplified anonymization algorithm to low-importance data to improve processing speed. The anonymization unit can also apply a balanced anonymization algorithm to medium-importance data, taking both security and processing speed into consideration. This minimizes the risk of personal information leakage by adjusting the level of anonymization based on the importance of the data. Data importance is evaluated using criteria such as data confidentiality and frequency of use. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the level of anonymization using an AI model that evaluates data importance.

[0073] The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated emotions. For example, if the user is feeling anxious, the anonymization unit will prioritize the anonymization of personal information. If the user is relaxed, the anonymization unit can also determine the priority of anonymization based on the importance of the data. If the user is in a hurry, the anonymization unit can also prioritize the anonymization of data that can be processed quickly. This minimizes the risk of personal information leakage by determining the priority of anonymization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The anonymization unit can adjust the order of anonymization based on the data submission date during the anonymization process. For example, the anonymization unit can prioritize anonymizing the most recent data and send it for analysis quickly. The anonymization unit can also postpone processing older data and prioritize the most recent data. The anonymization unit can also group data with similar submission dates and anonymize them all at once. This allows the latest data to be sent for analysis quickly by adjusting the order of anonymization based on the data submission date. The data submission date is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the order of anonymization using an AI model that evaluates the data submission date.

[0075] The anonymization unit can perform anonymization while considering the geographical distribution of the data during the anonymization process. For example, the anonymization unit can group geographically close data and anonymize them all at once. The anonymization unit can also anonymize geographically distant data separately, taking into account the characteristics of each region. The anonymization unit can also apply different anonymization algorithms based on the geographical distribution. This makes it possible to perform anonymization that takes into account the characteristics of each region by considering the geographical distribution of the data. The geographical distribution is evaluated using criteria such as the data distribution by region or geographical clusters. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can adjust the anonymization method using an AI model that evaluates the geographical distribution.

[0076] The pattern extraction unit can estimate the user's emotions and adjust the pattern extraction criteria based on the estimated emotions. For example, if the user is relaxed, the pattern extraction unit can perform detailed pattern extraction and analyze even the smallest elements. If the user is in a hurry, the pattern extraction unit can also narrow down the extraction to the main patterns. If the user is feeling anxious, the pattern extraction unit can also perform pattern extraction that provides a sense of security. By adjusting the pattern extraction criteria according to the user's emotions, more appropriate pattern extraction becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pattern extraction unit may be performed using a generative AI or not. For example, the pattern extraction unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The pattern extraction unit can improve the accuracy of pattern extraction by considering the interrelationships between data. For example, the pattern extraction unit can analyze the correlations between data and extract highly relevant patterns. The pattern extraction unit can also consider the interdependence of data and integrate multiple datasets to extract patterns. The pattern extraction unit can also visualize the interrelationships of data to facilitate understanding of the extracted patterns. This improves the accuracy of extraction by considering the interrelationships of data. The interrelationships of data are evaluated using criteria such as correlation coefficients or co-occurrence networks. Some or all of the above processing in the pattern extraction unit may be performed using generative AI, or not. For example, the pattern extraction unit can improve the accuracy of pattern extraction by using a generative AI model that evaluates the correlations of data.

[0078] The pattern extraction unit can apply different extraction algorithms to each data category during pattern extraction. For example, the pattern extraction unit can apply an extraction algorithm based on specific health indicators to health checkup result data. The pattern extraction unit can also apply an extraction algorithm based on the content of health guidance to health guidance record data. The pattern extraction unit can also apply an extraction algorithm based on lifestyle patterns to lifestyle data. By applying different extraction algorithms to each data category, the accuracy of extraction is improved. Different extraction algorithms can be implemented using techniques such as clustering or classification algorithms. Some or all of the above processing in the pattern extraction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the pattern extraction unit can input health checkup result data into a generative AI and have the generative AI execute an extraction algorithm based on specific health indicators.

[0079] The pattern extraction unit can estimate the user's emotions and adjust the display method of the extracted patterns based on the estimated user emotions. For example, if the user is relaxed, the pattern extraction unit can visually display detailed patterns. If the user is in a hurry, the pattern extraction unit can also concisely display only the main patterns. If the user is feeling anxious, the pattern extraction unit can also adopt a display method that provides a sense of security. By adjusting the display method of the extracted patterns according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pattern extraction unit may be performed using the generative AI or not. For example, the pattern extraction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The pattern extraction unit can determine the extraction priority based on the data submission date during pattern extraction. For example, the pattern extraction unit can prioritize the extraction of the latest data and send it for analysis quickly. The pattern extraction unit can also postpone processing older data and prioritize the processing of the latest data. The pattern extraction unit can also group data with similar submission dates and extract them all at once. This allows the latest data to be sent for analysis quickly by determining the extraction priority based on the data submission date. The data submission date is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the pattern extraction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the pattern extraction unit can determine the extraction priority using a generative AI model that evaluates the data submission date.

[0081] The pattern extraction unit can improve the accuracy of pattern extraction by referring to relevant literature for the data. For example, the pattern extraction unit evaluates the reliability of the extracted patterns based on the relevant literature. The pattern extraction unit can also optimize the extraction algorithm by incorporating insights from the relevant literature. The pattern extraction unit can also supplement the interpretation of the extracted patterns by referring to relevant literature. This improves the accuracy of extraction by referring to relevant literature for the data. Relevant literature is evaluated using criteria such as the use of literature databases or the analysis of citation relationships. Some or all of the above processing in the pattern extraction unit may be performed using generative AI or not. For example, the pattern extraction unit can improve the accuracy of extraction by using a generative AI model to evaluate relevant literature.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis, analyzing even the smallest details. If the user is in a hurry, the analysis unit can focus on the main elements. If the user is feeling anxious, the analysis unit can perform an analysis that provides reassurance. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the algorithm by feeding back past analysis results. The analysis unit can also evaluate the reliability of the analysis by referring to past analysis data. This improves the accuracy of the analysis algorithm by referring to past analysis data. Past analysis data is evaluated using criteria such as the use of a database or statistical analysis of past analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can optimize the analysis algorithm using an AI model that evaluates past analysis data.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, analyzing even the smallest details. For low-importance data, the analysis unit can perform a simplified analysis to improve processing speed. For medium-importance data, the analysis unit can perform a balanced analysis, considering both security and processing speed. This improves the accuracy of the analysis by adjusting the level of detail based on the importance of the data. Data importance is evaluated using criteria such as data confidentiality and frequency of use. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates data importance.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize the analysis of personal information. If the user is relaxed, the analysis unit can also determine the priority of analysis based on the importance of the data. If the user is in a hurry, the analysis unit can prioritize the analysis of data that can be processed quickly. This allows for more appropriate analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can group geographically close data and perform analysis all at once. The analysis unit can also analyze geographically distant data separately and consider the characteristics of each region. The analysis unit can also apply different analysis algorithms based on the geographical distribution. This makes it possible to perform analysis that takes regional characteristics into account by considering the geographical distribution of the data. Geographical distribution is evaluated using criteria such as the data distribution by region or geographical clusters. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the analysis method using an AI model that evaluates geographical distribution.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis process. For example, the analysis unit can evaluate the reliability of the analysis results based on relevant market data. The analysis unit can also optimize its analysis algorithm by incorporating insights from relevant market data. The analysis unit can also supplement the interpretation of the analysis results by referring to relevant market data. This improves the accuracy of the analysis by referring to relevant market data. Relevant market data is evaluated using criteria such as the use of market research data or competitive analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of its analysis by using an AI model to evaluate relevant market data.

[0088] The information provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is relaxed, the information provider may visually display detailed information. If the user is in a hurry, the information provider may also concisely display only the main information. If the user is feeling anxious, the information provider may also adopt a reassuring presentation style. This allows for more appropriate information provision by adjusting the presentation of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider may provide detailed explanations for high-importance information. The provider may also provide concise explanations for low-importance information. The provider may also provide a balanced level of detail for moderate-importance information. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. The importance of information is evaluated using criteria such as the confidentiality of the information and the frequency of use. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can adjust the level of detail using an AI model that evaluates the importance of information.

[0090] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the information provider can apply a provision algorithm based on specific health indicators to health checkup results. The information provider can also apply a provision algorithm based on the content of health guidance to health guidance records. The information provider can also apply a provision algorithm based on lifestyle patterns to lifestyle information. By applying different information provision algorithms depending on the category of information, it becomes possible to provide more appropriate information. Different information provision algorithms are implemented using technologies such as recommendation algorithms and filtering algorithms. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input health checkup results into a generating AI and have the generating AI execute a provision algorithm based on specific health indicators.

[0091] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the provider will prioritize providing information that provides reassurance. If the user is relaxed, the provider can also prioritize information based on its importance. If the user is in a hurry, the provider can prioritize providing information that can be delivered quickly. This allows for more appropriate information to be provided by prioritizing the information to be delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The information delivery unit can adjust the order of delivery based on the timing of information submission. For example, the delivery unit can prioritize the delivery of the latest information and send it for analysis quickly. The delivery unit can also postpone older information and prioritize the processing of the latest information. The delivery unit can also group information with similar submission times and deliver it all at once. This allows for the rapid delivery of the latest information by adjusting the order of delivery based on the timing of information submission. The timing of information submission is evaluated using criteria such as submission date and time or submission frequency. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can adjust the order of delivery using an AI model that evaluates the timing of information submission.

[0093] The information provider can improve the accuracy of its provision by referring to relevant literature at the time of provision. For example, the provider can evaluate the reliability of the provided information based on relevant literature. The provider can also optimize its provision algorithm by incorporating insights from relevant literature. The provider can also supplement the interpretation of the provided information by referring to relevant literature. This improves the accuracy of provision by referring to relevant literature. Relevant literature is evaluated using criteria such as the use of literature databases and the analysis of citation relationships. Some or all of the above processes in the information provider may be performed using AI or not. For example, the provider can improve the accuracy of provision by using an AI model to evaluate relevant literature.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The health guidance support system can estimate the user's emotions and customize the content of health guidance based on those emotions. For example, if the user is feeling stressed, the system can prioritize providing guidance that promotes relaxation. If the user is feeling motivated, the system can provide guidance that sets challenging goals. If the user is feeling anxious, the system can provide guidance that provides a sense of security. By customizing the content of health guidance according to the user's emotions, the effectiveness of the guidance can be maximized. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the health guidance support system may be performed using AI or not. For example, the health guidance support system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] A health guidance support system can collect users' lifestyle data and optimize the content of health guidance based on this data. For example, it can analyze users' meal records and suggest improvements to nutritional balance. It can also analyze users' exercise records and provide appropriate exercise plans. It can analyze users' sleep records and provide advice to improve sleep quality. By optimizing the content of health guidance based on users' lifestyle data, the effectiveness of the guidance can be maximized. Lifestyle data can be collected using, for example, wearable devices or smartphone applications. Some or all of the above-described processes in the health guidance support system may be performed using AI or not. For example, a health guidance support system can optimize the content of health guidance using an AI model that collects and analyzes users' lifestyle data.

[0097] The health guidance support system can estimate the user's emotions and adjust the pace of the health guidance based on the estimated emotions. For example, if the user is stressed, the pace can be slowed to allow the user to relax. If the user is motivated, the pace can be increased and challenging goals can be set. If the user is anxious, the pace can be adjusted to provide a sense of security. By adjusting the pace of the health guidance according to the user's emotions, the effectiveness of the guidance can be maximized. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, the health guidance support system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The health guidance support system can analyze a user's health checkup results in real time and provide immediate health guidance. For example, it can provide specific guidance for high-risk items immediately after the health checkup results are released. It can also immediately suggest improvements to diet and exercise based on the health checkup results. It can also recommend visits to necessary medical institutions based on the health checkup results. This maximizes the effectiveness of guidance by analyzing health checkup results in real time and providing immediate health guidance. Real-time analysis is achieved, for example, using cloud computing or high-speed data processing technology. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, the health guidance support system can provide health guidance using an AI model that analyzes health checkup results in real time.

[0099] A health guidance support system can estimate a user's emotions and customize health guidance feedback based on those emotions. For example, if a user is stressed, positive feedback can be prioritized. If a user is motivated, challenging feedback can be provided. If a user is anxious, reassuring feedback can be provided. This allows for maximizing the effectiveness of guidance by customizing feedback according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, the health guidance support system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] A health guidance support system can monitor users' health data over the long term, analyze trends, and optimize the content of health guidance. For example, it can analyze long-term fluctuations in a user's weight and blood pressure to provide appropriate guidance. It can also analyze changes in a user's exercise habits and adjust their exercise plan. It can analyze changes in a user's eating habits and suggest improvements to their nutritional balance. In this way, by monitoring users' health data over the long term and analyzing trends, the effectiveness of guidance can be maximized. Long-term monitoring is performed using, for example, wearable devices or smartphone apps. Some or all of the above-described processes in the health guidance support system may be performed using AI or not. For example, a health guidance support system can optimize the content of health guidance using an AI model that monitors users' health data over the long term and analyzes trends.

[0101] The health guidance support system can estimate the user's emotions and adjust the health guidance goals based on those emotions. For example, if the user is feeling stressed, realistic and achievable goals can be set. If the user is feeling motivated, challenging goals can be set. If the user is feeling anxious, goals that provide reassurance can be set. By adjusting the health guidance goals according to the user's emotions, the effectiveness of the guidance can be maximized. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, the health guidance support system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] A health guidance support system can compare a user's health data with that of other users and provide benchmarks. For example, a user can compare their health data with that of other users of the same age group to understand their own health status. They can also compare their health data with regional health data to understand region-specific health risks. They can also compare their health data with occupation-specific health risks to understand occupation-specific health risks. This allows users to objectively evaluate their own health status by comparing their health data with that of other users. The collection of comparative data is carried out using, for example, databases or statistical data. Some or all of the above processing in the health guidance support system may be performed using AI or not. For example, a health guidance support system can evaluate a user's health status using an AI model that compares a user's health data with that of other users and provides benchmarks.

[0103] The health guidance support system can estimate the user's emotions and adjust the communication method of health guidance based on the estimated emotions. For example, if the user is feeling stressed, the guidance can be given in a gentle tone. If the user is feeling motivated, encouraging words can be used frequently. If the user is feeling anxious, words that provide reassurance can be chosen. In this way, the effectiveness of the guidance can be maximized by adjusting the communication method of health guidance according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health guidance support system may be performed using AI or not using AI. For example, the health guidance support system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The health guidance support system can analyze users' health data, predict seasonal health risks, and provide health guidance accordingly. For example, in winter, the risk of influenza increases, so it can recommend vaccination and advise thorough handwashing. In summer, the risk of heatstroke increases, so it can recommend hydration and adequate rest. In spring, the risk of hay fever increases, so it can provide guidance on allergy countermeasures. By predicting seasonal health risks and providing health guidance accordingly, the effectiveness of the guidance can be maximized. Seasonal health risk predictions are made using, for example, historical data and weather data. Some or all of the above processing in the health guidance support system may be performed using AI, or not. For example, the health guidance support system can provide health guidance using an AI model that predicts seasonal health risks.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The anonymization unit anonymizes the results of the health checkup. The anonymization unit performs anonymization using methods such as data deletion, masking, and pseudo-anonymization. Data deletion completely removes personally identifiable information, masking hides personally identifiable information, and pseudo-anonymization replaces personally identifiable information with other information. Step 2: The pattern extraction unit analyzes the anonymized data using generation AI and extracts elements that have a decisive impact on the content and results of health guidance. The pattern extraction unit extracts patterns using methods such as detection of frequent patterns and correlation analysis. Detection of frequent patterns identifies patterns that appear frequently in the data, and correlation analysis reveals the correlations between data. Step 3: The analysis unit optimizes the health guidance process based on the extracted elements. The analysis unit performs optimization using methods such as algorithm selection and evaluation criteria. The optimal process is determined by algorithm selection, and the effectiveness of the process is evaluated using evaluation criteria. Step 4: The service provider provides the optimized implementation method to healthcare institutions, companies, and health insurance associations that conduct health guidance. The service provider delivers information using methods such as digital distribution and printed materials. Information is delivered quickly through digital distribution and in paper format through printed materials.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the anonymization unit, pattern extraction unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart device 14 and anonymizes the results of the health checkup. The pattern extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the anonymized data using a generating AI to extract elements that have a decisive impact on the content and results of health guidance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes the method of conducting health guidance based on the extracted elements. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the optimized method of conducting health guidance to medical institutions, companies, and health insurance associations that conduct health guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the anonymization unit, pattern extraction unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart glasses 214 and anonymizes the results of the health checkup. The pattern extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the anonymized data using generating AI to extract elements that have a decisive impact on the content and results of health guidance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes the method of conducting health guidance based on the extracted elements. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the optimized method of conducting health guidance to medical institutions, companies, and health insurance associations that conduct health guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the anonymization unit, pattern extraction unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the headset terminal 314 and anonymizes the results of the health checkup. The pattern extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the anonymized data using a generation AI to extract elements that have a decisive impact on the content and results of health guidance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes the method of conducting health guidance based on the extracted elements. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the optimized method of conducting health guidance to medical institutions, companies, and health insurance associations that conduct health guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the anonymization unit, pattern extraction unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the robot 414 and anonymizes the results of the health checkup. The pattern extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the anonymized data using a generated AI to extract elements that have a decisive impact on the content and results of health guidance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes the method of conducting health guidance based on the extracted elements. The provision unit is implemented by the control unit 46A of the robot 414 and provides the optimized method of conducting health guidance to medical institutions, companies, and health insurance associations that conduct health guidance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) An anonymization unit that anonymizes the results of health checkups, A pattern extraction unit analyzes the anonymized data from the anonymization unit and extracts elements that have a decisive influence on the content and results of health guidance. An analysis unit optimizes the method of health guidance based on the elements extracted by the pattern extraction unit, The system includes a providing unit that provides an optimized method of execution by the analysis unit. A system characterized by the following features. (Note 2) The anonymization unit is, We analyze anonymized data using generative AI to extract factors that have a decisive impact on the content and results of health guidance. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Optimize the health guidance process based on the extracted elements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide optimized methods for health guidance to medical institutions, companies, and health insurance associations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The anonymization unit is, When anonymizing health checkup results, different anonymization algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The anonymization unit is, During the anonymization process, the level of anonymization is adjusted based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The anonymization unit is, During the anonymization process, the order of anonymization is adjusted based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 10) The anonymization unit is, The anonymization process takes into account the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The pattern extraction unit, We estimate the user's emotions and adjust the pattern extraction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The pattern extraction unit, When extracting patterns, consider the interrelationships between data to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The pattern extraction unit, When extracting patterns, different extraction algorithms are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The pattern extraction unit, It estimates the user's emotions and adjusts how patterns extracted based on those estimated emotions are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 15) The pattern extraction unit, When extracting patterns, the extraction priority is determined based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 16) The pattern extraction unit, When extracting patterns, we improve the accuracy of the extraction by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, we refer to relevant market data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, we refer to relevant literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An anonymization unit that anonymizes the results of health checkups, A pattern extraction unit analyzes the anonymized data from the anonymization unit and extracts elements that have a decisive influence on the content and results of health guidance. An analysis unit optimizes the method of health guidance based on the elements extracted by the pattern extraction unit, The system includes a providing unit that provides an optimized method of execution by the analysis unit. A system characterized by the following features.

2. The anonymization unit is, We analyze anonymized data using generative AI to extract elements that have a decisive impact on the content and results of health guidance. The system according to feature 1.

3. The aforementioned analysis unit, Optimize the health guidance process based on the extracted elements. The system according to feature 1.

4. The aforementioned supply unit is, We provide optimized methods for health guidance to medical institutions, companies, and health insurance associations. The system according to feature 1.

5. The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system according to feature 1.

6. The anonymization unit is, When anonymizing health checkup results, different anonymization algorithms are applied depending on the type of data. The system according to feature 1.

7. The anonymization unit is, During the anonymization process, the level of anonymization is adjusted based on the importance of the data. The system according to feature 1.

8. The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system according to feature 1.

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