system

Abstract

The system according to this embodiment aims to appropriately collect and manage health data, enabling accurate information provision to doctors and sharing with family members. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a presentation unit, a sharing unit, and an accumulation unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents the analysis results obtained by the analysis unit to the doctor. The sharing unit shares the data collected by the collection unit with family members. The accumulation unit anonymizes and accumulates the data analyzed 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot 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 collection and management of health data are not sufficiently performed, and it is difficult to provide accurate information to doctors and share it with family members.

[0005] The system according to the embodiment aims to appropriately collect and manage health data and enable accurate information provision to doctors and sharing with family members.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, a sharing unit, and an aggregation unit. The data collection unit collects health data. The analysis unit analyzes the data collected by the data collection unit. The presentation unit presents the analysis results obtained by the analysis unit to the physician. The sharing unit shares the data collected by the data collection unit with family members. The aggregation unit anonymizes and aggregates the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately collect and manage health data, enabling accurate information provision to doctors and sharing with family members. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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 management system according to an embodiment of the present invention is a system that supports individual health management by combining a generative AI and a communication application. In this health management system, the user collects and manages daily health data, and the generative AI analyzes this information. The generative AI generates a report to present to a doctor based on the analysis results. Furthermore, the user's health data can be shared with family members through the communication application, enabling early detection of abnormalities and prompt response. Finally, the collected health data is anonymized, accumulated as big data, and utilized for medical research and medical innovation. For example, the health management system allows the user to collect and manage daily health data. In this process, data such as calorie intake, exercise amount, and sleep duration are input into the generative AI. The generative AI analyzes this data and generates a report to present to a doctor. Next, the health management system shares the user's health data with family members through the communication application. Family members can review the data and respond immediately if there are any abnormalities. Finally, the health management system anonymizes the collected health data and accumulates it as big data. This data is utilized for medical research and medical innovation, contributing to the provision of better medical services. As a result, the health management system can support the user's health management more efficiently and effectively.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, a sharing unit, and an aggregation unit. The data collection unit collects the user's daily health data. The data collection unit can collect data such as calorie intake, exercise volume, and sleep duration entered by the user. The data collection unit can also collect the user's vital signs using sensors. For example, the data collection unit can measure heart rate and blood pressure using a wearable device and collect the data. Furthermore, the data collection unit can acquire data from applications used by the user. For example, the data collection unit can acquire calorie intake data from a meal management application used by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. For example, the analysis unit can analyze the user's exercise data to understand trends in exercise volume. The analysis unit can also analyze the user's sleep data to evaluate sleep quality. Furthermore, the analysis unit can analyze the user's vital signs to understand their health status. The presentation unit generates a report to present the analysis results obtained by the analysis unit to a physician. The presentation unit can, for example, generate analysis results as text reports or graph reports. For example, the presentation unit can generate a graph showing the user's health status and present it to the doctor. The presentation unit can also send the analysis results to the doctor via email. Furthermore, the presentation unit can upload the analysis results to a cloud service that the doctor can access. The sharing unit shares the data collected by the collection unit with the family. For example, the sharing unit can send the user's health data to the family via email. Furthermore, the sharing unit can upload the user's health data to a cloud service that the family can access. Furthermore, the sharing unit can also share the user's health data with the family through a communication application. For example, the sharing unit notifies the family of the user's health data through a communication application. The aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, the aggregation unit can remove personal information and anonymize the data. Furthermore, the aggregation unit can anonymize the data by masking it.Furthermore, the data aggregation unit can collect anonymized data as big data and utilize it for medical research and medical innovation. As a result, the health management system according to this embodiment can efficiently collect, analyze, present, share, and aggregate users' health data.

[0030] The data collection unit collects the user's daily health data. For example, it can collect data such as calorie intake, exercise levels, and sleep duration entered by the user. Specifically, it collects data entered by the user using a smartphone or computer through a dedicated application. This allows users to easily record details of their daily meals, exercise, and sleep duration. The data collection unit can also collect the user's vital signs using sensors. For example, it can measure heart rate and blood pressure using wearable devices and collect data. These wearable devices are attached to the user's wrist or chest and monitor vital signs 24 hours a day. This allows for real-time monitoring of the user's health status. Furthermore, the data collection unit can acquire data from applications used by the user. For example, it can acquire calorie intake data from a meal management application used by the user. This eliminates the need for the user to manually record their daily meals. The data collection unit centrally manages this data and utilizes it as foundational data for comprehensively understanding the user's health status. The collected data is stored on a secure cloud server, making it accessible to the analysis and presentation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis and machine learning algorithms. Specifically, it analyzes the user's exercise data to understand trends in exercise volume. For example, it analyzes the type, duration, and intensity of exercise performed by the user daily to identify patterns in exercise habits. The analysis unit can also analyze the user's sleep data to evaluate sleep quality. For example, it analyzes the user's sleep duration and the ratio of deep to light sleep to quantify sleep quality. Furthermore, the analysis unit can analyze the user's vital signs to understand their health status. For example, it analyzes fluctuations in heart rate and blood pressure to detect abnormal patterns. The analysis unit comprehensively analyzes this data to evaluate the user's health status. Advanced AI-powered algorithms are used for analysis, enabling accurate identification of data patterns and trends. Additionally, the analysis unit can utilize historical data and statistical information to predict long-term changes in health status. This allows the analysis unit to understand the user's health status in real time and support appropriate health management.

[0032] The presentation unit generates a report to present the analysis results obtained by the analysis unit to the physician. The presentation unit can generate the analysis results as a text report or a graph report, for example. Specifically, it can generate a graph showing the user's health status and present it to the physician. For example, it can graph fluctuations in heart rate and blood pressure to visually indicate abnormal patterns. The presentation unit can also send the analysis results to the physician via email. This allows the physician to quickly understand the user's health status. Furthermore, the presentation unit can upload the analysis results to a cloud service that physicians can access. This allows physicians to access the user's health data anytime, anywhere, and make appropriate diagnoses and treatments. By accurately understanding the user's health status and providing appropriate information to physicians, the presentation unit supports the user's health management.

[0033] The sharing unit shares data collected by the collection unit with the family. For example, the sharing unit can send the user's health data to the family via email. Specifically, it can regularly report the user's health status and daily activity data to the family. The sharing unit can also upload the user's health data to a cloud service that the family can access. This allows the family to stay informed about the user's health status at any time. Furthermore, the sharing unit can also share the user's health data with the family through a communication application. For example, the sharing unit can notify the family of the user's health data through a communication application. This allows the family to understand the user's health status in real time and provide necessary support. By sharing the user's health data with the family, the sharing unit supports the user's health management and enhances the family's sense of security.

[0034] The data aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, the data aggregation unit can anonymize data by deleting personal information. Specifically, it can delete personal information such as users' names and addresses to anonymize the data. The data aggregation unit can also anonymize data by masking it. For example, it can process the data so that specific individuals cannot be identified. Furthermore, the data aggregation unit can aggregate the anonymized data as big data and utilize it for medical research and medical innovation. For example, the data aggregation unit can use the anonymized data to analyze the incidence trends and treatment effects of specific diseases. This allows medical researchers and medical institutions to develop more effective treatments and preventive measures. By ensuring data anonymity and contributing to medical research and medical innovation, the data aggregation unit contributes to improving the health of society as a whole.

[0035] The data collection unit can collect the user's daily health data. For example, the data collection unit can collect data such as calorie intake, exercise level, and sleep duration entered by the user. For example, the data collection unit can collect data such as weight, blood pressure, and heart rate entered by the user daily. The data collection unit can also obtain data from applications used by the user. For example, the data collection unit can obtain calorie intake data from a meal management application used by the user. In this way, the data collection unit can support health management by collecting the user's daily health data. Daily health data includes, but is not limited to, daily weight, blood pressure, and heart rate. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data entered by the user into a generating AI and have the generating AI perform data collection.

[0036] The analysis unit can analyze the collected health data. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can analyze the user's exercise data to understand trends in exercise volume. The analysis unit can analyze the user's sleep data to evaluate sleep quality. The analysis unit can analyze the user's vital signs to understand their health status. Thus, the analysis unit can understand the health status by analyzing the collected health data. The collected health data includes, but is not limited to, data type and data format. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0037] The presentation unit can generate a report to present the analysis results to a physician. For example, the presentation unit can generate the analysis results as a text report or a graph report. For example, the presentation unit can generate a graph showing the user's health status and present it to the physician. For example, the presentation unit can send the analysis results to the physician via email. For example, the presentation unit can upload the analysis results to a cloud service that the physician can access. This allows the presentation unit to present the analysis results to the physician, enabling them to receive appropriate medical care. The report includes, but is not limited to, text reports and graph reports. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the analysis results into a generating AI and have the generating AI perform the report generation.

[0038] The sharing unit can share collected health data with family members. For example, the sharing unit can send the user's health data to family members via email. For example, the sharing unit can upload the user's health data to a cloud service that family members can access. For example, the sharing unit can share the user's health data with family members through a communication application. This allows the sharing unit to detect abnormalities early by sharing health data with family members. Sharing with family members includes, but is not limited to, the types of data to be shared and the frequency of sharing. Some or all of the above-described processes in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the user's health data into a generating AI and have the generating AI perform the data sharing.

[0039] The aggregation unit can anonymize the analyzed data and aggregate it as big data. The aggregation unit can, for example, remove personal information and anonymize the data. The aggregation unit can, for example, mask the data and anonymize it. The aggregation unit can, for example, aggregate the anonymized data as big data and utilize it for medical research and medical innovation. In this way, the aggregation unit can contribute to medical research and medical innovation by anonymizing and aggregating the analyzed data. Big data includes, for example, the type of data and the purpose of data use, but is not limited to such examples. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the analyzed data into a generating AI and have the generating AI perform data anonymization.

[0040] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can analyze the user's past collection frequency and suggest the optimal collection schedule. For example, the data collection unit can analyze the user's past data collection patterns and suggest the optimal collection timing. In this way, the data collection unit can select the optimal collection method by analyzing the past collection history. Past health data collection history includes, but is not limited to, the types of data collected and the frequency of collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting data on calorie intake. For example, if the user prioritizes exercise, the data collection unit can prioritize collecting data on exercise volume. For example, if the user wants to improve the quality of their sleep, the data collection unit can prioritize collecting sleep data. In this way, the data collection unit can collect more relevant data by filtering the data based on the user's lifestyle and areas of interest. Lifestyle includes, but is not limited to, lifestyle habits and daily activities. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to oxygen concentration. For example, if the user is in an urban area, the data collection unit can prioritize the collection of data related to air pollution. For example, if the user is at the beach, the data collection unit can prioritize the collection of data related to humidity. In this way, the data collection unit can obtain data appropriate to the environment by collecting data based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the data collection.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit can prioritize collecting exercise data. For example, if a user posts about food, the data collection unit can prioritize collecting data about calorie intake. For example, if a user posts about sleep, the data collection unit can prioritize collecting sleep data. In this way, by analyzing social media activity, the data collection unit can collect data that is relevant to the user's interests. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a standard analysis on general health data. For example, the analysis unit can perform a simplified analysis on supplementary health data. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the health data. The importance of health data includes, but is not limited to, examples such as physician evaluation and data urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can apply a nutrition analysis algorithm to diet data. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. By applying analysis algorithms according to the category of health data, the analysis unit can provide more accurate analysis results. Categories of health data include, but are not limited to, vital signs and lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent health data. For example, the analysis unit may perform analysis while referring to past health data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the timing of health data collection. The timing of health data collection includes, but is not limited to, the date of data collection and the frequency of collection. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the timing of health data collection into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the health data. The relevance of health data includes, but is not limited to, data correlations and co-occurrences. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the relevance of the health data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The presentation unit can adjust the level of detail in a report based on the importance of the health data during report generation. For example, the presentation unit can generate a detailed report for important health data. For example, the presentation unit can generate a standard report for general health data. For example, the presentation unit can generate a simplified report for supplementary health data. In this way, the presentation unit can provide an appropriate report by adjusting the level of detail in the report according to the importance of the health data. The importance of health data includes, but is not limited to, the comprehensiveness of the information and the level of detail provided. Some or all of the processing described above in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0049] The presentation unit can apply different report generation algorithms depending on the category of health data when generating reports. For example, the presentation unit can apply an exercise report generation algorithm to exercise data. For example, the presentation unit can apply a nutrition report generation algorithm to diet data. For example, the presentation unit can apply a sleep report generation algorithm to sleep data. By doing so, the presentation unit can provide more accurate reports by applying a report generation algorithm according to the category of health data. Categories of health data include, but are not limited to, vital signs and lifestyle data. Some or all of the processing described above in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the categories of health data into a generation AI and have the generation AI execute the application of the report generation algorithm.

[0050] The presentation unit can determine the priority of reports based on when the health data was collected when generating reports. For example, the presentation unit can prioritize the inclusion of the most recent health data in the report. For example, the presentation unit can generate reports while referring to past health data. For example, the presentation unit can focus on inclusion of data collected during a specific period in the report. In this way, the presentation unit can prioritize the inclusion of the most recent data in the report by determining the priority of reports based on when the health data was collected. Report priorities include, but are not limited to, data importance and urgency. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the health data collection period into a generating AI and have the generating AI perform the determination of report priorities.

[0051] The presentation unit can adjust the order of reports based on the relevance of health data during report generation. For example, the presentation unit can prioritize the inclusion of highly relevant data in the report. For example, the presentation unit can postpone the inclusion of less relevant data. For example, the presentation unit can adjust the order of reports according to the relevance of the data. This allows the presentation unit to efficiently generate reports by adjusting the order of reports based on the relevance of health data. The order of reports includes, but is not limited to, data relevance and importance. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the relevance of health data into a generation AI and have the generation AI perform the adjustment of the report order.

[0052] The sharing unit can adjust the level of detail shared based on the importance of the health data during the sharing process. For example, the sharing unit can share detailed information for important health data. For example, it can share standard information for general health data. For example, it can share simplified information for supplementary health data. This allows the sharing unit to share appropriate information by adjusting the level of detail of sharing according to the importance of the health data. The level of detail of sharing includes, but is not limited to, comprehensiveness of information and detailed explanations. Some or all of the processing described above in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0053] The sharing unit can apply different sharing methods depending on the category of health data when sharing. For example, for exercise data, the sharing unit can apply a sharing method that includes exercise advice. For diet data, the sharing unit can apply a sharing method that includes nutrition advice. For sleep data, the sharing unit can apply a sharing method that includes sleep advice. In this way, the sharing unit can share appropriate information by applying a sharing method according to the category of health data. Sharing methods include, but are not limited to, email and cloud services. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the categories of health data into a generating AI and have the generating AI perform the application of the sharing method.

[0054] The sharing unit can determine the sharing priority based on when the health data was collected. For example, the sharing unit may prioritize sharing the most recent health data. The sharing unit may also share data while referring to past health data. For example, the sharing unit may focus on sharing data collected during a specific period. This allows the sharing unit to prioritize sharing the latest information by determining the sharing priority based on when the health data was collected. The sharing priority may include, but is not limited to, the data collection period and the importance of the data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit may input the health data collection period into a generating AI and have the generating AI determine the sharing priority.

[0055] The sharing unit can adjust the order of sharing based on the relevance of the health data during the sharing process. For example, the sharing unit can prioritize sharing highly relevant data. For example, the sharing unit can postpone sharing less relevant data. For example, the sharing unit can adjust the order of sharing according to the relevance of the data. This allows the sharing unit to efficiently share information by adjusting the order of sharing based on the relevance of the health data. The order of sharing includes, but is not limited to, data relevance and importance. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0056] The aggregation unit can adjust the level of anonymization based on the importance of the health data when anonymizing the data. For example, the aggregation unit can perform detailed anonymization for important health data. For example, the aggregation unit can perform standard anonymization for general health data. For example, the aggregation unit can perform simplified anonymization for supplementary health data. In this way, the aggregation unit can perform appropriate anonymization by adjusting the level of anonymization according to the importance of the health data. The level of anonymization includes, but is not limited to, comprehensiveness of information and detailed explanation. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of anonymization.

[0057] The aggregation unit can apply different anonymization algorithms depending on the category of health data when anonymizing data. For example, the aggregation unit can apply an exercise data anonymization algorithm to exercise data. For example, the aggregation unit can apply a diet data anonymization algorithm to diet data. For example, the aggregation unit can apply a sleep data anonymization algorithm to sleep data. In this way, the aggregation unit can perform appropriate anonymization by applying an anonymization algorithm according to the category of health data. Anonymization algorithms include, but are not limited to, data masking and pseudo-data generation. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the categories of health data into a generating AI and have the generating AI perform the application of an anonymization algorithm.

[0058] The aggregation unit can determine the anonymization priority based on when the health data was collected when anonymizing the data. For example, the aggregation unit can anonymize the most recent health data first. For example, the aggregation unit can anonymize while referring to past health data. For example, the aggregation unit can anonymize data collected during a specific period intensively. In this way, the aggregation unit can anonymize the most recent data first by determining the anonymization priority based on when the health data was collected. The anonymization priority includes, but is not limited to, the data collection period and the importance of the data. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the health data collection period into a generating AI and have the generating AI determine the anonymization priority.

[0059] The aggregation unit can adjust the anonymization order based on the relevance of the health data during the anonymization process. For example, the aggregation unit can prioritize anonymizing highly relevant data. For example, the aggregation unit can postpone anonymizing less relevant data. The aggregation unit can adjust the anonymization order according to the relevance of the data. This allows the aggregation unit to perform efficient anonymization by adjusting the anonymization order based on the relevance of the health data. The anonymization order includes, but is not limited to, data relevance and importance. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the anonymization order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can adjust its data collection methods based on the user's lifestyle and daily activities when collecting user health data. For example, if a user leads a sedentary lifestyle, the unit can focus on collecting data during periods of sitting. If a user leads an active lifestyle, it can prioritize collecting data during exercise. If a user is a night owl, it can collect detailed data during the night. In this way, the data collection unit can collect more relevant data by adjusting its data collection methods based on the user's lifestyle and daily activities.

[0062] The presentation unit can adjust the report content based on the user's health goals when generating the report. For example, if the user's goal is weight loss, it can report detailed data on calorie intake and exercise. If the user's goal is muscle gain, it can focus on reporting data related to strength training. If the user wants to improve their sleep quality, it can report detailed sleep data. In this way, the presentation unit can provide more relevant information by adjusting the report content based on the user's health goals.

[0063] The data aggregation unit can adjust the level of anonymization based on the intended use of the user's health data. For example, data used for medical research can be anonymized in detail. Data used for general statistical analysis can be anonymized in a standard manner. Data used for marketing analysis can be anonymized in a simplified manner. In this way, the data aggregation unit can perform appropriate anonymization by adjusting the level of anonymization based on the intended use of the health data.

[0064] The analysis unit can adjust its analysis algorithm based on the user's age and gender when analyzing user health data. For example, it can apply an age-appropriate analysis algorithm to data from elderly individuals, a gender-appropriate analysis algorithm to data from women, and a developmental stage-appropriate analysis algorithm to data from children. By adjusting the analysis algorithm based on the user's age and gender, the analysis unit can provide more accurate analysis results.

[0065] The sharing function can adjust the sharing method based on the user's family structure and relationships when sharing user health data. For example, if a user lives with elderly parents, a sharing method including simple explanations can be applied to the parents. If a user lives with children, a visually easy-to-understand sharing method can be applied to the children. If a user lives with a partner, a sharing method including detailed information can be applied to the partner. In this way, the sharing function can share appropriate information by adjusting the sharing method based on the user's family structure and relationships.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects the user's daily health data. For example, it collects data such as calorie intake, exercise level, and sleep duration entered by the user. The data collection unit can also collect the user's vital signs using sensors. For example, it can measure and collect data on heart rate and blood pressure using wearable devices. Furthermore, the data collection unit can also acquire data from applications used by the user. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms to understand trends in the user's exercise data, evaluate sleep quality, and analyze vital signs to understand their health status. Step 3: The presentation unit presents the analysis results obtained by the analysis unit to the physician. For example, it can generate the analysis results as a text report or a graph report and present them to the physician. It can also send the analysis results to the physician via email or upload them to a cloud service. Step 4: The sharing unit shares the data collected by the collection unit with the family. For example, it can send the user's health data to family members via email, upload it to a cloud service, or notify them through a communication application. Step 5: The aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, personal information is removed or data is masked to anonymize it, and it is used for medical research and medical innovation.

[0068] (Example of form 2) The health management system according to an embodiment of the present invention is a system that supports individual health management by combining a generative AI and a communication application. In this health management system, the user collects and manages daily health data, and the generative AI analyzes this information. The generative AI generates a report to present to a doctor based on the analysis results. Furthermore, the user's health data can be shared with family members through the communication application, enabling early detection of abnormalities and prompt response. Finally, the collected health data is anonymized, accumulated as big data, and utilized for medical research and medical innovation. For example, the health management system allows the user to collect and manage daily health data. In this process, data such as calorie intake, exercise amount, and sleep duration are input into the generative AI. The generative AI analyzes this data and generates a report to present to a doctor. Next, the health management system shares the user's health data with family members through the communication application. Family members can review the data and respond immediately if there are any abnormalities. Finally, the health management system anonymizes the collected health data and accumulates it as big data. This data is utilized for medical research and medical innovation, contributing to the provision of better medical services. As a result, the health management system can support the user's health management more efficiently and effectively.

[0069] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, a sharing unit, and an aggregation unit. The data collection unit collects the user's daily health data. The data collection unit can collect data such as calorie intake, exercise volume, and sleep duration entered by the user. The data collection unit can also collect the user's vital signs using sensors. For example, the data collection unit can measure heart rate and blood pressure using a wearable device and collect the data. Furthermore, the data collection unit can acquire data from applications used by the user. For example, the data collection unit can acquire calorie intake data from a meal management application used by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. For example, the analysis unit can analyze the user's exercise data to understand trends in exercise volume. The analysis unit can also analyze the user's sleep data to evaluate sleep quality. Furthermore, the analysis unit can analyze the user's vital signs to understand their health status. The presentation unit generates a report to present the analysis results obtained by the analysis unit to a physician. The presentation unit can, for example, generate analysis results as text reports or graph reports. For example, the presentation unit can generate a graph showing the user's health status and present it to the doctor. The presentation unit can also send the analysis results to the doctor via email. Furthermore, the presentation unit can upload the analysis results to a cloud service that the doctor can access. The sharing unit shares the data collected by the collection unit with the family. For example, the sharing unit can send the user's health data to the family via email. Furthermore, the sharing unit can upload the user's health data to a cloud service that the family can access. Furthermore, the sharing unit can also share the user's health data with the family through a communication application. For example, the sharing unit notifies the family of the user's health data through a communication application. The aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, the aggregation unit can remove personal information and anonymize the data. Furthermore, the aggregation unit can anonymize the data by masking it.Furthermore, the data aggregation unit can collect anonymized data as big data and utilize it for medical research and medical innovation. As a result, the health management system according to this embodiment can efficiently collect, analyze, present, share, and aggregate users' health data.

[0070] The data collection unit collects the user's daily health data. For example, it can collect data such as calorie intake, exercise levels, and sleep duration entered by the user. Specifically, it collects data entered by the user using a smartphone or computer through a dedicated application. This allows users to easily record details of their daily meals, exercise, and sleep duration. The data collection unit can also collect the user's vital signs using sensors. For example, it can measure heart rate and blood pressure using wearable devices and collect data. These wearable devices are attached to the user's wrist or chest and monitor vital signs 24 hours a day. This allows for real-time monitoring of the user's health status. Furthermore, the data collection unit can acquire data from applications used by the user. For example, it can acquire calorie intake data from a meal management application used by the user. This eliminates the need for the user to manually record their daily meals. The data collection unit centrally manages this data and utilizes it as foundational data for comprehensively understanding the user's health status. The collected data is stored on a secure cloud server, making it accessible to the analysis and presentation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis and machine learning algorithms. Specifically, it analyzes the user's exercise data to understand trends in exercise volume. For example, it analyzes the type, duration, and intensity of exercise performed by the user daily to identify patterns in exercise habits. The analysis unit can also analyze the user's sleep data to evaluate sleep quality. For example, it analyzes the user's sleep duration and the ratio of deep to light sleep to quantify sleep quality. Furthermore, the analysis unit can analyze the user's vital signs to understand their health status. For example, it analyzes fluctuations in heart rate and blood pressure to detect abnormal patterns. The analysis unit comprehensively analyzes this data to evaluate the user's health status. Advanced AI-powered algorithms are used for analysis, enabling accurate identification of data patterns and trends. Additionally, the analysis unit can utilize historical data and statistical information to predict long-term changes in health status. This allows the analysis unit to understand the user's health status in real time and support appropriate health management.

[0072] The presentation unit generates a report to present the analysis results obtained by the analysis unit to the physician. The presentation unit can generate the analysis results as a text report or a graph report, for example. Specifically, it can generate a graph showing the user's health status and present it to the physician. For example, it can graph fluctuations in heart rate and blood pressure to visually indicate abnormal patterns. The presentation unit can also send the analysis results to the physician via email. This allows the physician to quickly understand the user's health status. Furthermore, the presentation unit can upload the analysis results to a cloud service that physicians can access. This allows physicians to access the user's health data anytime, anywhere, and make appropriate diagnoses and treatments. By accurately understanding the user's health status and providing appropriate information to physicians, the presentation unit supports the user's health management.

[0073] The sharing unit shares data collected by the collection unit with the family. For example, the sharing unit can send the user's health data to the family via email. Specifically, it can regularly report the user's health status and daily activity data to the family. The sharing unit can also upload the user's health data to a cloud service that the family can access. This allows the family to stay informed about the user's health status at any time. Furthermore, the sharing unit can also share the user's health data with the family through a communication application. For example, the sharing unit can notify the family of the user's health data through a communication application. This allows the family to understand the user's health status in real time and provide necessary support. By sharing the user's health data with the family, the sharing unit supports the user's health management and enhances the family's sense of security.

[0074] The data aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, the data aggregation unit can anonymize data by deleting personal information. Specifically, it can delete personal information such as users' names and addresses to anonymize the data. The data aggregation unit can also anonymize data by masking it. For example, it can process the data so that specific individuals cannot be identified. Furthermore, the data aggregation unit can aggregate the anonymized data as big data and utilize it for medical research and medical innovation. For example, the data aggregation unit can use the anonymized data to analyze the incidence trends and treatment effects of specific diseases. This allows medical researchers and medical institutions to develop more effective treatments and preventive measures. By ensuring data anonymity and contributing to medical research and medical innovation, the data aggregation unit contributes to improving the health of society as a whole.

[0075] The data collection unit can collect the user's daily health data. For example, the data collection unit can collect data such as calorie intake, exercise level, and sleep duration entered by the user. For example, the data collection unit can collect data such as weight, blood pressure, and heart rate entered by the user daily. The data collection unit can also obtain data from applications used by the user. For example, the data collection unit can obtain calorie intake data from a meal management application used by the user. In this way, the data collection unit can support health management by collecting the user's daily health data. Daily health data includes, but is not limited to, daily weight, blood pressure, and heart rate. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data entered by the user into a generating AI and have the generating AI perform data collection.

[0076] The analysis unit can analyze the collected health data. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can analyze the user's exercise data to understand trends in exercise volume. The analysis unit can analyze the user's sleep data to evaluate sleep quality. The analysis unit can analyze the user's vital signs to understand their health status. Thus, the analysis unit can understand the health status by analyzing the collected health data. The collected health data includes, but is not limited to, data type and data format. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0077] The presentation unit can generate a report to present the analysis results to a physician. For example, the presentation unit can generate the analysis results as a text report or a graph report. For example, the presentation unit can generate a graph showing the user's health status and present it to the physician. For example, the presentation unit can send the analysis results to the physician via email. For example, the presentation unit can upload the analysis results to a cloud service that the physician can access. This allows the presentation unit to present the analysis results to the physician, enabling them to receive appropriate medical care. The report includes, but is not limited to, text reports and graph reports. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the analysis results into a generating AI and have the generating AI perform the report generation.

[0078] The sharing unit can share collected health data with family members. For example, the sharing unit can send the user's health data to family members via email. For example, the sharing unit can upload the user's health data to a cloud service that family members can access. For example, the sharing unit can share the user's health data with family members through a communication application. This allows the sharing unit to detect abnormalities early by sharing health data with family members. Sharing with family members includes, but is not limited to, the types of data to be shared and the frequency of sharing. Some or all of the above-described processes in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the user's health data into a generating AI and have the generating AI perform the data sharing.

[0079] The aggregation unit can anonymize the analyzed data and aggregate it as big data. The aggregation unit can, for example, remove personal information and anonymize the data. The aggregation unit can, for example, mask the data and anonymize it. The aggregation unit can, for example, aggregate the anonymized data as big data and utilize it for medical research and medical innovation. In this way, the aggregation unit can contribute to medical research and medical innovation by anonymizing and aggregating the analyzed data. Big data includes, for example, the type of data and the purpose of data use, but is not limited to such examples. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the analyzed data into a generating AI and have the generating AI perform data anonymization.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is stressed, the data collection unit can collect health data during times when the user is relaxed. For example, if the user is tired, the data collection unit can collect health data after the user has rested. For example, if the user is excited, the data collection unit can collect health data after the user's emotions have calmed down. In this way, the data collection unit can collect more appropriate data by adjusting the timing of health data collection 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can analyze the user's past collection frequency and suggest the optimal collection schedule. For example, the data collection unit can analyze the user's past data collection patterns and suggest the optimal collection timing. In this way, the data collection unit can select the optimal collection method by analyzing the past collection history. Past health data collection history includes, but is not limited to, the types of data collected and the frequency of collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0082] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting data on calorie intake. For example, if the user prioritizes exercise, the data collection unit can prioritize collecting data on exercise volume. For example, if the user wants to improve the quality of their sleep, the data collection unit can prioritize collecting sleep data. In this way, the data collection unit can collect more relevant data by filtering the data based on the user's lifestyle and areas of interest. Lifestyle includes, but is not limited to, lifestyle habits and daily activities. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to stress levels. For example, if the user is tired, the data collection unit may prioritize collecting sleep data. For example, if the user is relaxed, the data collection unit may collect overall health data in a balanced manner. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0084] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of data related to oxygen concentration. For example, if the user is in an urban area, the data collection unit can prioritize the collection of data related to air pollution. For example, if the user is at the beach, the data collection unit can prioritize the collection of data related to humidity. In this way, the data collection unit can obtain data appropriate to the environment by collecting data based on geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the data collection.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit can prioritize collecting exercise data. For example, if a user posts about food, the data collection unit can prioritize collecting data about calorie intake. For example, if a user posts about sleep, the data collection unit can prioritize collecting sleep data. In this way, by analyzing social media activity, the data collection unit can collect data that is relevant to the user's interests. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, the analysis unit can provide easy-to-understand analysis results by adjusting the presentation of the 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. Presentation of the analysis includes, but is not limited to, text, graphs, charts, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a standard analysis on general health data. For example, the analysis unit can perform a simplified analysis on supplementary health data. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the health data. The importance of health data includes, but is not limited to, examples such as physician evaluation and data urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can apply a nutrition analysis algorithm to diet data. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. By applying analysis algorithms according to the category of health data, the analysis unit can provide more accurate analysis results. Categories of health data include, but are not limited to, vital signs and lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, the analysis unit can provide appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The length of the analysis includes, but is not limited to, the time of the analysis and the level of detail of the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent health data. For example, the analysis unit may perform analysis while referring to past health data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the timing of health data collection. The timing of health data collection includes, but is not limited to, the date of data collection and the frequency of collection. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the timing of health data collection into a generating AI and have the generating AI perform the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the health data. The relevance of health data includes, but is not limited to, data correlations and co-occurrences. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the relevance of the health data into a generating AI and have the generating AI adjust the order of analysis.

[0092] The presentation unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is stressed, the presentation unit can provide a simple and visually easy-to-understand report. For example, if the user is relaxed, the presentation unit can provide a detailed report. For example, if the user is excited, the presentation unit can provide a visually stimulating report. In this way, the presentation unit can provide an easy-to-understand report by adjusting the presentation of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The presentation of the report includes, but is not limited to, text, graphs, charts, etc. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the report.

[0093] The presentation unit can adjust the level of detail in a report based on the importance of the health data during report generation. For example, the presentation unit can generate a detailed report for important health data. For example, the presentation unit can generate a standard report for general health data. For example, the presentation unit can generate a simplified report for supplementary health data. In this way, the presentation unit can provide an appropriate report by adjusting the level of detail in the report according to the importance of the health data. The importance of health data includes, but is not limited to, the comprehensiveness of the information and the level of detail provided. Some or all of the processing described above in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0094] The presentation unit can apply different report generation algorithms depending on the category of health data when generating reports. For example, the presentation unit can apply an exercise report generation algorithm to exercise data. For example, the presentation unit can apply a nutrition report generation algorithm to diet data. For example, the presentation unit can apply a sleep report generation algorithm to sleep data. By doing so, the presentation unit can provide more accurate reports by applying a report generation algorithm according to the category of health data. Categories of health data include, but are not limited to, vital signs and lifestyle data. Some or all of the processing described above in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the categories of health data into a generation AI and have the generation AI execute the application of the report generation algorithm.

[0095] The presentation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the presentation unit can provide a short, concise report. For example, if the user is relaxed, the presentation unit can provide a detailed report. For example, if the user is excited, the presentation unit can provide a visually stimulating report. In this way, the presentation unit can provide an appropriate report by adjusting the length of the report 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. The length of the report includes, but is not limited to, the number of pages or characters. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the report.

[0096] The presentation unit can determine the priority of reports based on when the health data was collected when generating reports. For example, the presentation unit can prioritize the inclusion of the most recent health data in the report. For example, the presentation unit can generate reports while referring to past health data. For example, the presentation unit can focus on inclusion of data collected during a specific period in the report. In this way, the presentation unit can prioritize the inclusion of the most recent data in the report by determining the priority of reports based on when the health data was collected. Report priorities include, but are not limited to, data importance and urgency. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the health data collection period into a generating AI and have the generating AI perform the determination of report priorities.

[0097] The presentation unit can adjust the order of reports based on the relevance of health data during report generation. For example, the presentation unit can prioritize the inclusion of highly relevant data in the report. For example, the presentation unit can postpone the inclusion of less relevant data. For example, the presentation unit can adjust the order of reports according to the relevance of the data. This allows the presentation unit to efficiently generate reports by adjusting the order of reports based on the relevance of health data. The order of reports includes, but is not limited to, data relevance and importance. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the relevance of health data into a generation AI and have the generation AI perform the adjustment of the report order.

[0098] The sharing unit can estimate the user's emotions and adjust the timing of sharing based on the estimated emotions. For example, if the user is stressed, the sharing unit can share health data during a time when the user is relaxed. For example, if the user is tired, the sharing unit can share health data after the user has rested. For example, if the user is excited, the sharing unit can share health data after the user's emotions have calmed down. In this way, the sharing unit can share health data at the appropriate time by adjusting the timing of sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The timing of sharing includes, for example, the time of data collection and the user's situation, but is not limited to such examples. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of sharing.

[0099] The sharing unit can adjust the level of detail shared based on the importance of the health data during the sharing process. For example, the sharing unit can share detailed information for important health data. For example, it can share standard information for general health data. For example, it can share simplified information for supplementary health data. This allows the sharing unit to share appropriate information by adjusting the level of detail of sharing according to the importance of the health data. The level of detail of sharing includes, but is not limited to, comprehensiveness of information and detailed explanations. Some or all of the processing described above in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0100] The sharing unit can apply different sharing methods depending on the category of health data when sharing. For example, for exercise data, the sharing unit can apply a sharing method that includes exercise advice. For diet data, the sharing unit can apply a sharing method that includes nutrition advice. For sleep data, the sharing unit can apply a sharing method that includes sleep advice. In this way, the sharing unit can share appropriate information by applying a sharing method according to the category of health data. Sharing methods include, but are not limited to, email and cloud services. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the categories of health data into a generating AI and have the generating AI perform the application of the sharing method.

[0101] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated emotions. For example, if the user is stressed, the sharing unit may prioritize sharing stress-related data. For example, if the user is tired, the sharing unit may prioritize sharing sleep data. For example, if the user is relaxed, the sharing unit may share overall health data in a balanced manner. In this way, the sharing unit can prioritize sharing important information by determining sharing priorities 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. Sharing priorities may include, but are not limited to, data importance and urgency. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI determine the sharing priorities.

[0102] The sharing unit can determine the sharing priority based on when the health data was collected. For example, the sharing unit may prioritize sharing the most recent health data. The sharing unit may also share data while referring to past health data. For example, the sharing unit may focus on sharing data collected during a specific period. This allows the sharing unit to prioritize sharing the latest information by determining the sharing priority based on when the health data was collected. The sharing priority may include, but is not limited to, the data collection period and the importance of the data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit may input the health data collection period into a generating AI and have the generating AI determine the sharing priority.

[0103] The sharing unit can adjust the order of sharing based on the relevance of the health data during the sharing process. For example, the sharing unit can prioritize sharing highly relevant data. For example, the sharing unit can postpone sharing less relevant data. For example, the sharing unit can adjust the order of sharing according to the relevance of the data. This allows the sharing unit to efficiently share information by adjusting the order of sharing based on the relevance of the health data. The order of sharing includes, but is not limited to, data relevance and importance. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0104] The aggregation unit can estimate the user's emotions and adjust the data anonymization method based on the estimated user emotions. For example, if the user values ​​privacy, the aggregation unit can perform detailed anonymization. For example, if the user is willing to share data, the aggregation unit can perform simplified anonymization. For example, if the user is neutral, the aggregation unit can perform standard anonymization. In this way, the aggregation unit can perform appropriate anonymization by adjusting the data 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. Data anonymization methods include, but are not limited to, the deletion of personal information or data masking. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI adjust the anonymization method.

[0105] The aggregation unit can adjust the level of anonymization based on the importance of the health data when anonymizing the data. For example, the aggregation unit can perform detailed anonymization for important health data. For example, the aggregation unit can perform standard anonymization for general health data. For example, the aggregation unit can perform simplified anonymization for supplementary health data. In this way, the aggregation unit can perform appropriate anonymization by adjusting the level of anonymization according to the importance of the health data. The level of anonymization includes, but is not limited to, comprehensiveness of information and detailed explanation. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of anonymization.

[0106] The aggregation unit can apply different anonymization algorithms depending on the category of health data when anonymizing data. For example, the aggregation unit can apply an exercise data anonymization algorithm to exercise data. For example, the aggregation unit can apply a diet data anonymization algorithm to diet data. For example, the aggregation unit can apply a sleep data anonymization algorithm to sleep data. In this way, the aggregation unit can perform appropriate anonymization by applying an anonymization algorithm according to the category of health data. Anonymization algorithms include, but are not limited to, data masking and pseudo-data generation. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the categories of health data into a generating AI and have the generating AI perform the application of an anonymization algorithm.

[0107] The aggregation unit can estimate the user's emotions and determine the priority of data anonymization based on the estimated user emotions. For example, if the user values ​​privacy, the aggregation unit can prioritize anonymizing important data. For example, if the user is willing to share data, the aggregation unit can anonymize the overall data in a balanced manner. For example, if the user is neutral, the aggregation unit can anonymize data with a standard priority. In this way, the aggregation unit can prioritize anonymizing important data by determining the priority of data 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. Anonymization priorities include, but are not limited to, data importance and urgency. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI perform the determination of anonymization priorities.

[0108] The aggregation unit can determine the anonymization priority based on when the health data was collected when anonymizing the data. For example, the aggregation unit can anonymize the most recent health data first. For example, the aggregation unit can anonymize while referring to past health data. For example, the aggregation unit can anonymize data collected during a specific period intensively. In this way, the aggregation unit can anonymize the most recent data first by determining the anonymization priority based on when the health data was collected. The anonymization priority includes, but is not limited to, the data collection period and the importance of the data. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the health data collection period into a generating AI and have the generating AI determine the anonymization priority.

[0109] The aggregation unit can adjust the anonymization order based on the relevance of the health data during the anonymization process. For example, the aggregation unit can prioritize anonymizing highly relevant data. For example, the aggregation unit can postpone anonymizing less relevant data. The aggregation unit can adjust the anonymization order according to the relevance of the data. This allows the aggregation unit to perform efficient anonymization by adjusting the anonymization order based on the relevance of the health data. The anonymization order includes, but is not limited to, data relevance and importance. Some or all of the above processing in the aggregation unit may be performed using, for example, AI, or not using AI. For example, the aggregation unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the anonymization order.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] 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 stressed, it can prioritize the analysis of stress-related data. If the user is relaxed, it can analyze overall health data in a balanced manner. If the user is excited, it can focus on analyzing specific health data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0112] The data collection unit can adjust its data collection methods based on the user's lifestyle and daily activities when collecting user health data. For example, if a user leads a sedentary lifestyle, the unit can focus on collecting data during periods of sitting. If a user leads an active lifestyle, it can prioritize collecting data during exercise. If a user is a night owl, it can collect detailed data during the night. In this way, the data collection unit can collect more relevant data by adjusting its data collection methods based on the user's lifestyle and daily activities.

[0113] The analysis unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is stressed, it can provide simple, positive feedback. If the user is relaxed, it can provide detailed feedback. If the user is excited, it can provide visually stimulating feedback. In this way, the analysis unit can provide easily understandable feedback by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0114] The presentation unit can adjust the report content based on the user's health goals when generating the report. For example, if the user's goal is weight loss, it can report detailed data on calorie intake and exercise. If the user's goal is muscle gain, it can focus on reporting data related to strength training. If the user wants to improve their sleep quality, it can report detailed sleep data. In this way, the presentation unit can provide more relevant information by adjusting the report content based on the user's health goals.

[0115] The sharing function can estimate the user's emotions and adjust the content of the information it shares based on those emotions. For example, if the user is stressed, it can prioritize sharing information related to stress reduction. If the user is relaxed, it can share overall health information in a balanced way. If the user is excited, it can focus on sharing specific health information. In this way, the sharing function can prioritize sharing important information by adjusting the content of the information it shares according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0116] The data aggregation unit can adjust the level of anonymization based on the intended use of the user's health data. For example, data used for medical research can be anonymized in detail. Data used for general statistical analysis can be anonymized in a standard manner. Data used for marketing analysis can be anonymized in a simplified manner. In this way, the data aggregation unit can perform appropriate anonymization by adjusting the level of anonymization based on the intended use of the health data.

[0117] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, it can prioritize collecting data related to stress levels. If the user is relaxed, it can collect overall health data in a balanced manner. If the user is excited, it can focus on collecting specific health data. This allows the data collection unit to prioritize the collection of important data by adjusting the types of data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0118] The analysis unit can adjust its analysis algorithm based on the user's age and gender when analyzing user health data. For example, it can apply an age-appropriate analysis algorithm to data from elderly individuals, a gender-appropriate analysis algorithm to data from women, and a developmental stage-appropriate analysis algorithm to data from children. By adjusting the analysis algorithm based on the user's age and gender, the analysis unit can provide more accurate analysis results.

[0119] The presentation unit can estimate the user's emotions and adjust the timing of report delivery based on the estimated emotions. For example, if the user is stressed, the report can be delivered during a time when they are relaxed. If the user is tired, the report can be delivered after they have rested. If the user is excited, the report can be delivered after their emotions have calmed down. In this way, the presentation unit can provide reports at the appropriate time by adjusting the timing of report delivery according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0120] The sharing function can adjust the sharing method based on the user's family structure and relationships when sharing user health data. For example, if a user lives with elderly parents, a sharing method including simple explanations can be applied to the parents. If a user lives with children, a visually easy-to-understand sharing method can be applied to the children. If a user lives with a partner, a sharing method including detailed information can be applied to the partner. In this way, the sharing function can share appropriate information by adjusting the sharing method based on the user's family structure and relationships.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the user's daily health data. For example, it collects data such as calorie intake, exercise level, and sleep duration entered by the user. The data collection unit can also collect the user's vital signs using sensors. For example, it can measure and collect data on heart rate and blood pressure using wearable devices. Furthermore, the data collection unit can also acquire data from applications used by the user. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms to understand trends in the user's exercise data, evaluate sleep quality, and analyze vital signs to understand their health status. Step 3: The presentation unit presents the analysis results obtained by the analysis unit to the physician. For example, it can generate the analysis results as a text report or a graph report and present them to the physician. It can also send the analysis results to the physician via email or upload them to a cloud service. Step 4: The sharing unit shares the data collected by the collection unit with the family. For example, it can send the user's health data to family members via email, upload it to a cloud service, or notify them through a communication application. Step 5: The aggregation unit anonymizes the data analyzed by the analysis unit and aggregates it as big data. For example, personal information is removed or data is masked to anonymize it, and it is used for medical research and medical innovation.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, sharing unit, and aggregation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's health data using the sensors and applications of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a report to present the analysis results to a doctor. The sharing unit shares the user's health data with family members using the communication application of the smart device 14. The aggregation unit is implemented in the specific processing unit 290 of the data processing unit 12 and anonymizes the analyzed data and aggregates it as big data. 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] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, sharing unit, and aggregation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's health data using the sensors and applications of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a report to present the analysis results to a doctor. The sharing unit shares the user's health data with family members using the communication application of the smart glasses 214. The aggregation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and anonymizes the analyzed data and aggregates it as big data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[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 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.

[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 (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).

[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] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, sharing unit, and aggregation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user health data using the sensors and applications of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a report to present the analysis results to a doctor. The sharing unit shares the user's health data with family members using the communication application of the headset terminal 314. The aggregation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and anonymizes the analyzed data and aggregates it as big data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, presentation unit, sharing unit, and aggregation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user health data using the sensors and applications of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a report to present the analysis results to a doctor. The sharing unit shares the user's health data with family members using the communication application of the robot 414. The aggregation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and anonymizes the analyzed data and aggregates it as big data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A presentation unit that presents the analysis results obtained by the aforementioned analysis unit to the physician, A sharing unit that shares the data collected by the aforementioned collection unit with the family, The system comprises an aggregation unit that anonymizes and aggregates the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect users' daily health data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected health data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Generate a report to present the analysis results to the doctor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Share collected health data with your family. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned integration unit is The analyzed data is anonymized and collected as big data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When generating reports, adjust the level of detail in the report based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When generating reports, different report generation algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When generating reports, prioritize reports based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When generating reports, the order of reports is adjusted based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, It estimates the user's emotions and adjusts the timing of sharing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing, different sharing methods are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing, prioritize sharing based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing, adjust the sharing order based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned integration unit is We estimate the user's sentiment and adjust the data anonymization method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned integration unit is When anonymizing data, adjust the level of anonymization based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned integration unit is When anonymizing data, different anonymization algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned integration unit is We estimate user sentiment and determine the priority for anonymizing data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned integration unit is When anonymizing data, the priority of anonymization is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned integration unit is When anonymizing data, the anonymization order is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A presentation unit that presents the analysis results obtained by the aforementioned analysis unit to the physician, A sharing unit that shares the data collected by the aforementioned collection unit with the family, The system comprises an aggregation unit that anonymizes and aggregates the data analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect users' daily health data. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected health data. The system according to feature 1.

4. The aforementioned display unit is, Generate a report to present the analysis results to the doctor. The system according to feature 1.

5. The aforementioned shared portion is, Share collected health data with your family. The system according to feature 1.

6. The aforementioned integration unit is The analyzed data is anonymized and collected as big data. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system according to feature 1.

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