system

Abstract

The system according to this embodiment aims to suggest hot springs and bathing methods tailored to the user's health condition and to introduce bathing methods based on medical evidence. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, an introduction unit, and an interface unit. The collection unit collects information on the water quality of hot springs. The suggestion unit suggests a hot spring and bathing method that suits the user's health condition based on the water quality information collected by the collection unit. The introduction unit introduces a medically-based bathing method based on the hot spring and bathing method suggested by the suggestion unit. The interface unit provides the user with the hot spring and bathing method suggested by the suggestion 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 character of the chatbot, 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 information on selecting hot springs based on medical evidence and effective bathing methods is insufficient, and it is difficult to understand the quality of the spring water and its health effects.

[0005] The system according to the embodiment aims to propose a hot spring and a bathing method suitable for the user's health condition and introduce a bathing method based on medical evidence.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, an introduction unit, and an interface unit. The collection unit collects information on the water quality of hot springs. The suggestion unit suggests a hot spring and bathing method that suits the user's health condition based on the water quality information collected by the collection unit. The introduction unit introduces a medically-based bathing method based on the hot spring and bathing method suggested by the suggestion unit. The interface unit provides the user with the hot spring and bathing method suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest hot springs and bathing methods tailored to the user's health condition and introduce bathing methods based on medical evidence. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters linked 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 reception 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 reception 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 hot spring selection and bathing method suggestion system according to an embodiment of the present invention is a system that uses AI to suggest hot spring selection and bathing methods based on medical evidence. This system solves the problem that conventional hot spring selection is complex and there is a lack of information on effective bathing methods, making it difficult to understand the properties of the spring water and their health effects. The hot spring selection and bathing method suggestion system solves these problems with the following components. First, it collects hot spring data from all over the country. The AI ​​collects and updates hot spring water quality information from the internet in real time. This ensures that the latest hot spring information is always available. Next, it suggests the optimal hot spring and bathing method based on the user's health condition and medical symptoms. The user inputs their health condition, and the AI ​​analyzes this information to suggest the optimal hot spring and bathing method. For example, for a user with joint pain, it suggests a hot spring with properties effective for joint pain and the optimal bathing method at that hot spring. Furthermore, it introduces bathing methods based on medical evidence. The AI ​​introduces traditional therapeutic bathing methods such as timed bathing, alternating hot and cold bathing, and gradual bathing, as well as bathing methods that contribute to health. This allows the user to learn about bathing methods whose effectiveness has been medically proven. Finally, it provides an interactive user interface. Users can easily search for, book, and receive information on hot springs via their smartphones or web applications. This allows users to easily experience hot spring therapy. This system makes it easy to select reliable hot springs based on medical evidence and to experience customized therapies tailored to their health condition. It also allows users to maximize the health benefits of hot springs. Thus, the hot spring selection and bathing method suggestion system suggests the optimal hot spring and bathing method based on the user's health condition and introduces medically-backed bathing methods, thereby maximizing the health benefits of hot springs.

[0029] The hot spring selection and bathing method suggestion system according to this embodiment comprises a collection unit, a suggestion unit, a referral unit, and an interface unit. The collection unit collects information on the water quality of hot springs. The collection unit collects and updates information on the water quality of hot springs in real time, for example, from the internet. The collection unit can collect information such as the temperature, pH value, and contained components of the hot springs. The suggestion unit suggests hot springs and bathing methods that suit the user's health condition based on the water quality information collected by the collection unit. The suggestion unit suggests the most suitable hot springs and bathing methods based on the user's health condition and medical symptoms, for example. The suggestion unit makes suggestions based on information such as the user's medical history, current symptoms, and doctor's diagnosis. The referral unit introduces bathing methods based on medical evidence, based on the hot springs and bathing methods suggested by the suggestion unit. The referral unit introduces traditional therapeutic bathing methods and bathing methods that contribute to health, for example, such as timed bathing, alternating hot and cold bathing, and gradual bathing. The referral unit introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. The interface unit provides users with hot springs and bathing methods suggested by the suggestion unit. The interface unit allows users to search for hot springs, make reservations, and receive therapy information via smartphones or web applications, for example. The interface unit makes it easy for users to experience hot spring therapy. As a result, the hot spring selection and bathing method suggestion system according to the embodiment can maximize the health benefits of hot springs by suggesting the optimal hot spring and bathing method based on the user's health condition and introducing bathing methods based on medical evidence.

[0030] The data collection unit collects information on the water quality of hot springs. For example, the unit collects and updates hot spring water quality information in real time from the internet. Specifically, the unit obtains detailed information such as the temperature, pH value, and contained components of hot springs from the official websites of each hot spring facility, tourist information sites, and databases related to hot springs. Since this information is updated regularly, the unit automatically obtains the latest data and updates the database in the system. The unit also collects feedback and reviews from users of hot spring facilities to enhance the reliability of the water quality information. For example, it collects data from platforms where users post their impressions and experiences with hot springs and evaluates it in comparison with the water quality information. This allows the unit to build a foundation for accurately and comprehensively collecting and providing hot spring water quality information to users. Furthermore, the unit also collects geographical and access information of hot springs, which users can use as a reference when choosing a hot spring. This allows the unit to comprehensively collect and provide users with not only information on the water quality of hot springs, but also overall information about hot spring facilities.

[0031] The Proposal Department proposes hot springs and bathing methods tailored to the user's health condition, based on the spring water quality information collected by the Data Collection Department. For example, the Proposal Department proposes the optimal hot spring and bathing method based on the user's health condition and medical history. Specifically, the Proposal Department analyzes data such as health information, medical history, current symptoms, and doctor's diagnosis results entered by the user to select the optimal hot spring and bathing method. The Proposal Department uses AI to analyze this data and identify the hot spring water quality and bathing method that is most suitable for the user's health condition. For example, for a user with joint pain, it proposes a hot spring with a high temperature and containing specific minerals, and provides specific advice on bathing time and frequency. The Proposal Department also provides detailed suggestions on how to use the hot springs and precautions to take, according to the user's health condition. For example, for a user with high blood pressure, it instructs them to avoid prolonged bathing and proposes appropriate bathing time and how to take breaks. In this way, the Proposal Department can provide individualized advice based on the user's health condition and maximize the health benefits of the hot springs. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the most suitable hot springs and bathing methods, thereby contributing to improved health.

[0032] The Introduction Section introduces medically-based bathing methods based on the hot springs and bathing methods proposed by the Proposal Section. For example, the Introduction Section introduces traditional therapeutic bathing methods and health-benefiting bathing methods such as timed bathing, alternating hot and cold bathing, and gradual bathing. Specifically, the Introduction Section explains in detail the effects and application conditions of each bathing method based on reliable sources such as medical papers and clinical trial results. For example, regarding timed bathing, it introduces a method that maximizes the effects of the hot spring while reducing the burden on the body by repeating bathing and resting at regular intervals. Regarding alternating hot and cold bathing, it explains that using hot springs and cold water alternately promotes blood circulation and is effective for fatigue recovery and improving immunity. Furthermore, regarding gradual bathing, it introduces a method of enjoying the effects of the hot spring while gradually acclimatizing the body by using multiple bathtubs with different temperatures in sequence. The Introduction Section provides specific procedures and precautions for these bathing methods to ensure users can practice them safely and effectively. For example, it provides specific instructions regarding bathing time, temperature, and how to take breaks. The Introduction Section can also customize the optimal bathing method according to the user's health condition and individual needs. This allows the referral department to provide users with reliable bathing methods based on medical evidence, maximizing the health benefits of hot springs.

[0033] The interface unit provides users with hot springs and bathing methods suggested by the suggestion unit. For example, the interface unit allows users to search for and book hot springs and receive therapy guidance via smartphones or web applications. Specifically, the interface unit provides an intuitive interface that users can easily operate, enabling smooth searching and booking of hot springs. Users can access the app from their smartphones or computers to view detailed information about suggested hot springs and complete booking procedures. The interface unit also provides personalized therapy guidance based on the user's health condition and suggested options. For example, upon arrival at a booked hot spring facility, users receive a therapy guidance notification on their smartphone, displaying specific bathing methods and precautions. Furthermore, the interface unit can collect user feedback to improve the system. For instance, users can input their impressions and experiences after using a hot spring, allowing the suggestion and introduction units to use this information to improve the accuracy of their suggestions and recommendations. Additionally, the interface unit provides a multilingual interface to enhance user convenience, creating an environment that is easy for foreign users to use. This allows the interface unit to provide users with an intuitive and user-friendly operating environment, enabling smooth hot spring selection and bathing method suggestions.

[0034] The data collection unit can collect and update hot spring water quality information from the internet in real time. The data collection unit collects hot spring water quality information from, for example, hot spring information websites and databases on the internet. The data collection unit collects information such as the temperature, pH value, and contained components of the hot springs and updates it in real time. The data collection unit can update the information at a frequency such as every minute, every hour, or every day. This allows the data collection unit to always maintain the latest hot spring information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data obtained from a hot spring information website on the internet into a generating AI, which can analyze the data to extract and update the water quality information.

[0035] The suggestion unit can propose the most suitable hot spring and bathing method based on the user's health condition and medical status. For example, the suggestion unit collects and analyzes information about the user's health condition and medical status. Based on information such as the user's medical history, current symptoms, and doctor's diagnosis, the suggestion unit proposes the most suitable hot spring and bathing method. For example, for a user with joint pain, the suggestion unit will propose a hot spring with water properties effective for joint pain and the most suitable bathing method at that hot spring. The suggestion unit can also propose specific bathing methods such as bathing time, temperature, and frequency based on the user's health condition. In this way, the suggestion unit can propose the most suitable hot spring and bathing method tailored to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's health condition into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0036] The introduction section can introduce traditional bathing methods and health-benefiting bathing techniques such as timed bathing, alternating hot and cold baths, and gradual bathing. The introduction section introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. The introduction section introduces specific bathing methods such as timed bathing, alternating hot and cold baths, and gradual bathing. For example, timed bathing is a method of soaking in a hot spring for a set amount of time, alternating hot and cold bathing is a method of bathing alternately in hot and cold water, and gradual bathing is a method of bathing in water of different temperatures in stages. In this way, the introduction section can introduce users to bathing methods that have been medically proven to be effective. Some or all of the above processing in the introduction section may be performed using AI, for example, or not using AI. For example, the introduction section can input medical papers and clinical trial results into a generating AI, and the generating AI can analyze the data and introduce bathing methods.

[0037] The interface unit allows users to search for, book, and receive information about hot springs via smartphones or web applications. The interface unit provides hot spring information via smartphones or web applications, for example. The interface unit makes it easy for users to search for and book hot springs. The interface unit operates on platforms such as iOS apps, Android apps, and web browsers. The interface unit makes it easy for users to experience hot spring therapy. As a result, the interface unit makes it easy for users to experience hot spring therapy. Some or all of the above-described processes in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can input the user's search history and booking history into a generating AI, which can then analyze the data to provide optimal hot spring information.

[0038] The data collection unit can analyze past data collection history to select the optimal data collection method when collecting hot spring water quality information. For example, the data collection unit prioritizes collecting reliable information sources based on past data collection history. The data collection unit optimizes the data collection frequency based on past data collection history. The data collection unit analyzes past data collection history and improves the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0039] The data collection unit can collect hot spring water quality information while considering the geographical conditions and seasonal variations of the hot springs. For example, the data collection unit can prioritize collecting information on easily accessible hot springs, taking geographical conditions into consideration. The data collection unit can collect hot spring information for each season, taking seasonal variations into consideration. The data collection unit can collect optimal hot spring information by combining geographical conditions and seasonal variations. In this way, the data collection unit can collect more relevant hot spring information by considering geographical conditions and seasonal variations. 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 data on geographical conditions and seasonal variations into a generating AI, which can then analyze the data to collect optimal hot spring information.

[0040] The data collection unit can prioritize collecting highly relevant hot spring information by considering the user's geographical location when collecting information on the quality of hot springs. For example, the data collection unit may prioritize collecting information on hot springs close to the user's current location. The data collection unit may also prioritize collecting information on hot springs related to the user's travel destination. The data collection unit collects the most relevant hot spring information based on the user's geographical location. As a result, the data collection unit can provide highly relevant hot spring information by considering the user's geographical location. 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 may input the user's geographical location information into a generating AI, which will analyze the data and collect highly relevant hot spring information.

[0041] The data collection unit can collect information on the water quality of hot springs by analyzing user reviews and ratings of hot springs. For example, the data collection unit can analyze user reviews and prioritize collecting information on highly-rated hot springs. Based on user ratings, the data collection unit collects highly reliable hot spring information. The data collection unit combines user reviews and ratings to collect optimal hot spring information. In this way, the data collection unit can collect highly reliable hot spring information by analyzing user reviews and ratings. 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 user review and rating data into a generating AI, which can then analyze the data to collect highly reliable hot spring information.

[0042] The suggestion unit can improve the accuracy of its suggestions based on detailed information about the user's health condition. For example, the suggestion unit can analyze the user's health condition in detail and suggest the most suitable hot spring and bathing method. The suggestion unit can suggest hot springs with specific water properties according to the user's medical condition. The suggestion unit can update its suggestions in response to changes in the user's health condition. This allows the suggestion unit to make more appropriate suggestions by improving the accuracy of its suggestions based on detailed information about the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input detailed data about the user's health condition into a generating AI, which can then analyze the data to improve the accuracy of its suggestions.

[0043] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's past hot spring usage history. For example, the suggestion unit analyzes the user's past usage history and proposes the most suitable hot spring. Based on the user's past usage history, the suggestion unit proposes an effective bathing method. The suggestion unit customizes the proposed content by referring to the user's past usage history. In this way, the suggestion unit can make more appropriate suggestions by considering the user's past hot spring usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hot spring usage history data into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0044] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's lifestyle and dietary information. For example, the suggestion unit can analyze the user's lifestyle and propose the most suitable hot spring and bathing method. The suggestion unit can propose a hot spring and bathing method that contributes to health based on the user's dietary information. The suggestion unit can combine the user's lifestyle and dietary information to make the best suggestion. In this way, the suggestion unit can make more appropriate suggestions by considering the user's lifestyle and dietary information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and dietary information into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0045] The suggestion unit can customize its suggestions by considering the user's family structure and information about their companions. For example, the suggestion unit can suggest a hot spring that the whole family can enjoy, taking into account the user's family structure. The suggestion unit can suggest a hot spring that the group can enjoy, based on information about the user's companions. The suggestion unit combines the user's family structure and information about their companions to make the most appropriate suggestion. As a result, the suggestion unit can make more appropriate suggestions by considering the user's family structure and information about their companions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's family structure and information about their companions into a generating AI, which can then analyze the data and customize the suggestion.

[0046] The recommendation unit can customize the content of its recommendations for bathing methods according to the user's health condition and medical status. For example, the recommendation unit can analyze the user's health condition in detail and recommend the most suitable bathing method. The recommendation unit can recommend a specific bathing method according to the user's medical condition. The recommendation unit can update the content of its recommendations in response to changes in the user's health condition. In this way, the recommendation unit can recommend the most suitable bathing method according to the user's health condition and medical status. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data about the user's health condition into a generating AI, which can then analyze the data and customize the recommendation content.

[0047] The introduction section can optimize its bathing method recommendations by referencing feedback from past users. For example, the introduction section analyzes feedback from past users to recommend the most suitable bathing method. The introduction section improves its recommendations based on user feedback. The introduction section customizes its recommendations by referring to past feedback. This allows the introduction section to recommend more appropriate bathing methods by referring to feedback from past users. Some or all of the above processes in the introduction section may be performed using AI, for example, or without AI. For example, the introduction section can input past user feedback data into a generating AI, which can then analyze the data to optimize the recommendations.

[0048] The recommendation section can customize the content of its recommendations for bathing methods by considering the user's living environment and climate conditions. For example, the recommendation section can recommend the most suitable bathing method considering the user's living environment. The recommendation section can recommend bathing methods that contribute to health based on the user's climate conditions. The recommendation section can provide the most suitable recommendations by combining the user's living environment and climate conditions. In this way, the recommendation section can recommend more appropriate bathing methods by considering the user's living environment and climate conditions. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input data on the user's living environment and climate conditions into a generating AI, which can then analyze the data and customize the recommendation content.

[0049] The recommendation section can adjust the content of its recommendations when introducing bathing methods, taking into account the user's cultural background and preferences. For example, the recommendation section can introduce the most suitable bathing method considering the user's cultural background. The recommendation section can introduce bathing methods that contribute to health based on the user's preferences. The recommendation section can make the most appropriate recommendations by combining the user's cultural background and preferences. In this way, the recommendation section can introduce more appropriate bathing methods by taking into account the user's cultural background and preferences. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input data on the user's cultural background and preferences into a generating AI, and the generating AI can analyze the data and adjust the content of the recommendations.

[0050] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit analyzes the user's past operation history and provides the optimal display method. Based on the user's operation history, the interface unit selects a display method that improves usability. The interface unit customizes the display content by referring to the user's past operation history. In this way, the interface unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history data into a generating AI, and the generating AI can analyze the data and select the optimal display method.

[0051] The interface unit can optimize the displayed content according to the characteristics of the user's device when displaying the interface. For example, if the user is using a smartphone, the interface unit provides displayed content that matches the screen size. If the user is using a tablet, the interface unit provides displayed content optimized for a larger screen. If the user is using a desktop, the interface unit provides displayed content that includes detailed information. In this way, the interface unit can provide a more appropriate display by optimizing the displayed content according to the characteristics of the user's device. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user device characteristic data into a generating AI, and the generating AI can analyze the data to optimize the displayed content.

[0052] The interface unit can provide optimal display content by considering the user's geographical location information when displaying the interface. For example, the interface unit can display information about nearby hot springs based on the user's current location. The interface unit can display information about hot springs related to the user's travel destination. The interface unit provides optimal display content based on the user's geographical location information. In this way, the interface unit can provide more appropriate display content by considering the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal display content.

[0053] The interface unit can customize the displayed content when displaying the interface, taking into account the user's language settings and cultural background. For example, the interface unit can automatically set the language of the interface based on the user's language settings. The interface unit can provide appropriate expressions and designs, taking into account the user's cultural background. The interface unit can provide optimal displayed content by combining the user's language settings and cultural background. In this way, the interface unit can provide more appropriate displayed content by taking into account the user's language settings and cultural background. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input data on the user's language settings and cultural background into a generating AI, which can then analyze the data and customize the displayed content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect information on the water quality of hot springs by analyzing user reviews and ratings of hot springs. For example, it can analyze user reviews and prioritize collecting information on highly-rated hot springs. Based on user ratings, it can collect highly reliable hot spring information. By combining user reviews and ratings, it can collect optimal hot spring information. In this way, the data collection unit can collect highly reliable hot spring information by analyzing user reviews and ratings. 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 user review and rating data into a generating AI, which can then analyze the data to collect highly reliable hot spring information.

[0056] The data collection unit can collect information on the quality of hot springs while considering the geographical conditions and seasonal variations of the hot springs. For example, it can prioritize collecting information on easily accessible hot springs by considering geographical conditions. It can also collect information on hot springs for each season by considering seasonal variations. By combining geographical conditions and seasonal variations, it can collect the most relevant hot spring information. In this way, the data collection unit can collect more relevant hot spring information by considering geographical conditions and seasonal variations. 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 data on geographical conditions and seasonal variations into a generating AI, which can then analyze the data to collect the most relevant hot spring information.

[0057] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's lifestyle and dietary information. For example, it can analyze the user's lifestyle and propose the most suitable hot spring and bathing method. Based on the user's dietary information, it can propose a hot spring and bathing method that contributes to health. It can combine the user's lifestyle and dietary information to make the most suitable suggestion. In this way, the suggestion unit can make more appropriate suggestions by considering the user's lifestyle and dietary information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and dietary information into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0058] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's past hot spring usage history. For example, it can analyze the user's past usage history and propose the most suitable hot spring. It can propose an effective bathing method based on the user's past usage history. It can customize the proposed content by referring to the user's past usage history. In this way, the suggestion unit can make more appropriate suggestions by considering the user's past hot spring usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hot spring usage history data into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0059] The interface unit can provide optimal display content by considering the user's geographical location information when displaying the interface. For example, it can display information about nearby hot springs based on the user's current location, or display information about hot springs related to the user's travel destination. It provides optimal display content based on the user's geographical location information. In this way, the interface unit can provide more appropriate display content by considering the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal display content.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects information on the water quality of hot springs. The collection unit collects and updates information on the water quality of hot springs in real time, for example, from the internet. The collection unit can collect information such as the temperature, pH value, and contained components of the hot springs. Step 2: The Proposal Department proposes hot springs and bathing methods tailored to the user's health condition based on the spring quality information collected by the Collection Department. For example, the Proposal Department proposes the most suitable hot springs and bathing methods based on the user's health condition and medical history. The Proposal Department makes suggestions based on information such as the user's medical history, current symptoms, and doctor's diagnosis. Step 3: The introduction section introduces bathing methods based on medical evidence, using the hot springs and bathing methods proposed by the suggestion section. The introduction section introduces traditional therapeutic bathing methods and health-benefiting bathing methods, such as timed bathing, alternating hot and cold bathing, and gradual bathing. The introduction section introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. Step 4: The interface unit provides the user with the hot springs and bathing methods proposed by the proposal unit. The interface unit allows users to search for hot springs, make reservations, and receive therapy information via smartphones or web applications, for example. The interface unit makes it easy for users to experience hot spring therapy.

[0062] (Example of form 2) The hot spring selection and bathing method suggestion system according to an embodiment of the present invention is a system that uses AI to suggest hot spring selection and bathing methods based on medical evidence. This system solves the problem that conventional hot spring selection is complex and there is a lack of information on effective bathing methods, making it difficult to understand the properties of the spring water and their health effects. The hot spring selection and bathing method suggestion system solves these problems with the following components. First, it collects hot spring data from all over the country. The AI ​​collects and updates hot spring water quality information from the internet in real time. This ensures that the latest hot spring information is always available. Next, it suggests the optimal hot spring and bathing method based on the user's health condition and medical symptoms. The user inputs their health condition, and the AI ​​analyzes this information to suggest the optimal hot spring and bathing method. For example, for a user with joint pain, it suggests a hot spring with properties effective for joint pain and the optimal bathing method at that hot spring. Furthermore, it introduces bathing methods based on medical evidence. The AI ​​introduces traditional therapeutic bathing methods such as timed bathing, alternating hot and cold bathing, and gradual bathing, as well as bathing methods that contribute to health. This allows the user to learn about bathing methods whose effectiveness has been medically proven. Finally, it provides an interactive user interface. Users can easily search for, book, and receive information on hot springs via their smartphones or web applications. This allows users to easily experience hot spring therapy. This system makes it easy to select reliable hot springs based on medical evidence and to experience customized therapies tailored to their health condition. It also allows users to maximize the health benefits of hot springs. Thus, the hot spring selection and bathing method suggestion system suggests the optimal hot spring and bathing method based on the user's health condition and introduces medically-backed bathing methods, thereby maximizing the health benefits of hot springs.

[0063] The hot spring selection and bathing method suggestion system according to this embodiment comprises a collection unit, a suggestion unit, a referral unit, and an interface unit. The collection unit collects information on the water quality of hot springs. The collection unit collects and updates information on the water quality of hot springs in real time, for example, from the internet. The collection unit can collect information such as the temperature, pH value, and contained components of the hot springs. The suggestion unit suggests hot springs and bathing methods that suit the user's health condition based on the water quality information collected by the collection unit. The suggestion unit suggests the most suitable hot springs and bathing methods based on the user's health condition and medical symptoms, for example. The suggestion unit makes suggestions based on information such as the user's medical history, current symptoms, and doctor's diagnosis. The referral unit introduces bathing methods based on medical evidence, based on the hot springs and bathing methods suggested by the suggestion unit. The referral unit introduces traditional therapeutic bathing methods and bathing methods that contribute to health, for example, such as timed bathing, alternating hot and cold bathing, and gradual bathing. The referral unit introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. The interface unit provides users with hot springs and bathing methods suggested by the suggestion unit. The interface unit allows users to search for hot springs, make reservations, and receive therapy information via smartphones or web applications, for example. The interface unit makes it easy for users to experience hot spring therapy. As a result, the hot spring selection and bathing method suggestion system according to the embodiment can maximize the health benefits of hot springs by suggesting the optimal hot spring and bathing method based on the user's health condition and introducing bathing methods based on medical evidence.

[0064] The data collection unit collects information on the water quality of hot springs. For example, the unit collects and updates hot spring water quality information in real time from the internet. Specifically, the unit obtains detailed information such as the temperature, pH value, and contained components of hot springs from the official websites of each hot spring facility, tourist information sites, and databases related to hot springs. Since this information is updated regularly, the unit automatically obtains the latest data and updates the database in the system. The unit also collects feedback and reviews from users of hot spring facilities to enhance the reliability of the water quality information. For example, it collects data from platforms where users post their impressions and experiences with hot springs and evaluates it in comparison with the water quality information. This allows the unit to build a foundation for accurately and comprehensively collecting and providing hot spring water quality information to users. Furthermore, the unit also collects geographical and access information of hot springs, which users can use as a reference when choosing a hot spring. This allows the unit to comprehensively collect and provide users with not only information on the water quality of hot springs, but also overall information about hot spring facilities.

[0065] The Proposal Department proposes hot springs and bathing methods tailored to the user's health condition, based on the spring water quality information collected by the Data Collection Department. For example, the Proposal Department proposes the optimal hot spring and bathing method based on the user's health condition and medical history. Specifically, the Proposal Department analyzes data such as health information, medical history, current symptoms, and doctor's diagnosis results entered by the user to select the optimal hot spring and bathing method. The Proposal Department uses AI to analyze this data and identify the hot spring water quality and bathing method that is most suitable for the user's health condition. For example, for a user with joint pain, it proposes a hot spring with a high temperature and containing specific minerals, and provides specific advice on bathing time and frequency. The Proposal Department also provides detailed suggestions on how to use the hot springs and precautions to take, according to the user's health condition. For example, for a user with high blood pressure, it instructs them to avoid prolonged bathing and proposes appropriate bathing time and how to take breaks. In this way, the Proposal Department can provide individualized advice based on the user's health condition and maximize the health benefits of the hot springs. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the most suitable hot springs and bathing methods, thereby contributing to improved health.

[0066] The Introduction Section introduces medically-based bathing methods based on the hot springs and bathing methods proposed by the Proposal Section. For example, the Introduction Section introduces traditional therapeutic bathing methods and health-benefiting bathing methods such as timed bathing, alternating hot and cold bathing, and gradual bathing. Specifically, the Introduction Section explains in detail the effects and application conditions of each bathing method based on reliable sources such as medical papers and clinical trial results. For example, regarding timed bathing, it introduces a method that maximizes the effects of the hot spring while reducing the burden on the body by repeating bathing and resting at regular intervals. Regarding alternating hot and cold bathing, it explains that using hot springs and cold water alternately promotes blood circulation and is effective for fatigue recovery and improving immunity. Furthermore, regarding gradual bathing, it introduces a method of enjoying the effects of the hot spring while gradually acclimatizing the body by using multiple bathtubs with different temperatures in sequence. The Introduction Section provides specific procedures and precautions for these bathing methods to ensure users can practice them safely and effectively. For example, it provides specific instructions regarding bathing time, temperature, and how to take breaks. The Introduction Section can also customize the optimal bathing method according to the user's health condition and individual needs. This allows the referral department to provide users with reliable bathing methods based on medical evidence, maximizing the health benefits of hot springs.

[0067] The interface unit provides users with hot springs and bathing methods suggested by the suggestion unit. For example, the interface unit allows users to search for and book hot springs and receive therapy guidance via smartphones or web applications. Specifically, the interface unit provides an intuitive interface that users can easily operate, enabling smooth searching and booking of hot springs. Users can access the app from their smartphones or computers to view detailed information about suggested hot springs and complete booking procedures. The interface unit also provides personalized therapy guidance based on the user's health condition and suggested options. For example, upon arrival at a booked hot spring facility, users receive a therapy guidance notification on their smartphone, displaying specific bathing methods and precautions. Furthermore, the interface unit can collect user feedback to improve the system. For instance, users can input their impressions and experiences after using a hot spring, allowing the suggestion and introduction units to use this information to improve the accuracy of their suggestions and recommendations. Additionally, the interface unit provides a multilingual interface to enhance user convenience, creating an environment that is easy for foreign users to use. This allows the interface unit to provide users with an intuitive and user-friendly operating environment, enabling smooth hot spring selection and bathing method suggestions.

[0068] The data collection unit can collect and update hot spring water quality information from the internet in real time. The data collection unit collects hot spring water quality information from, for example, hot spring information websites and databases on the internet. The data collection unit collects information such as the temperature, pH value, and contained components of the hot springs and updates it in real time. The data collection unit can update the information at a frequency such as every minute, every hour, or every day. This allows the data collection unit to always maintain the latest hot spring information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data obtained from a hot spring information website on the internet into a generating AI, which can analyze the data to extract and update the water quality information.

[0069] The suggestion unit can propose the most suitable hot spring and bathing method based on the user's health condition and medical status. For example, the suggestion unit collects and analyzes information about the user's health condition and medical status. Based on information such as the user's medical history, current symptoms, and doctor's diagnosis, the suggestion unit proposes the most suitable hot spring and bathing method. For example, for a user with joint pain, the suggestion unit will propose a hot spring with water properties effective for joint pain and the most suitable bathing method at that hot spring. The suggestion unit can also propose specific bathing methods such as bathing time, temperature, and frequency based on the user's health condition. In this way, the suggestion unit can propose the most suitable hot spring and bathing method tailored to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's health condition into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0070] The introduction section can introduce traditional bathing methods and health-benefiting bathing techniques such as timed bathing, alternating hot and cold baths, and gradual bathing. The introduction section introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. The introduction section introduces specific bathing methods such as timed bathing, alternating hot and cold baths, and gradual bathing. For example, timed bathing is a method of soaking in a hot spring for a set amount of time, alternating hot and cold bathing is a method of bathing alternately in hot and cold water, and gradual bathing is a method of bathing in water of different temperatures in stages. In this way, the introduction section can introduce users to bathing methods that have been medically proven to be effective. Some or all of the above processing in the introduction section may be performed using AI, for example, or not using AI. For example, the introduction section can input medical papers and clinical trial results into a generating AI, and the generating AI can analyze the data and introduce bathing methods.

[0071] The interface unit allows users to search for, book, and receive information about hot springs via smartphones or web applications. The interface unit provides hot spring information via smartphones or web applications, for example. The interface unit makes it easy for users to search for and book hot springs. The interface unit operates on platforms such as iOS apps, Android apps, and web browsers. The interface unit makes it easy for users to experience hot spring therapy. As a result, the interface unit makes it easy for users to experience hot spring therapy. Some or all of the above-described processes in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can input the user's search history and booking history into a generating AI, which can then analyze the data to provide optimal hot spring information.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of hot spring information collection based on the estimated emotions. The data collection unit estimates emotions using, for example, facial recognition or voice analysis of the user. When the user is relaxed, the data collection unit reduces the collection frequency and collects only the necessary information. When the user is stressed, the data collection unit increases the collection frequency and provides more detailed information. When the user is in a hurry, the data collection unit speeds up the collection timing and provides information quickly. In this way, the data collection unit can provide more appropriate information by adjusting the timing of hot spring information 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 not using AI. For example, the data collection unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust the collection timing.

[0073] The data collection unit can analyze past data collection history to select the optimal data collection method when collecting hot spring water quality information. For example, the data collection unit prioritizes collecting reliable information sources based on past data collection history. The data collection unit optimizes the data collection frequency based on past data collection history. The data collection unit analyzes past data collection history and improves the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0074] The data collection unit can collect hot spring water quality information while considering the geographical conditions and seasonal variations of the hot springs. For example, the data collection unit can prioritize collecting information on easily accessible hot springs, taking geographical conditions into consideration. The data collection unit can collect hot spring information for each season, taking seasonal variations into consideration. The data collection unit can collect optimal hot spring information by combining geographical conditions and seasonal variations. In this way, the data collection unit can collect more relevant hot spring information by considering geographical conditions and seasonal variations. 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 data on geographical conditions and seasonal variations into a generating AI, which can then analyze the data to collect optimal hot spring information.

[0075] The data collection unit can estimate the user's emotions and determine the priority of hot spring information to collect based on the estimated emotions. The data collection unit estimates emotions using, for example, facial recognition or voice analysis of the user. If the user is relaxed, the data collection unit prioritizes collecting information on hot springs with high relaxation effects. If the user is stressed, the data collection unit prioritizes collecting information on hot springs with stress-relieving effects. If the user is in a hurry, the data collection unit prioritizes collecting information on hot springs that can be accessed quickly. In this way, the data collection unit can provide more appropriate information by determining the priority of hot spring information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 user facial data into a generative AI, which can analyze the data to estimate emotions and determine the priority of hot spring information to collect.

[0076] The data collection unit can prioritize collecting highly relevant hot spring information by considering the user's geographical location when collecting information on the quality of hot springs. For example, the data collection unit may prioritize collecting information on hot springs close to the user's current location. The data collection unit may also prioritize collecting information on hot springs related to the user's travel destination. The data collection unit collects the most relevant hot spring information based on the user's geographical location. As a result, the data collection unit can provide highly relevant hot spring information by considering the user's geographical location. 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 may input the user's geographical location information into a generating AI, which will analyze the data and collect highly relevant hot spring information.

[0077] The data collection unit can collect information on the water quality of hot springs by analyzing user reviews and ratings of hot springs. For example, the data collection unit can analyze user reviews and prioritize collecting information on highly-rated hot springs. Based on user ratings, the data collection unit collects highly reliable hot spring information. The data collection unit combines user reviews and ratings to collect optimal hot spring information. In this way, the data collection unit can collect highly reliable hot spring information by analyzing user reviews and ratings. 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 user review and rating data into a generating AI, which can then analyze the data to collect highly reliable hot spring information.

[0078] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can estimate emotions using facial recognition or voice analysis. If the user is relaxed, the suggestion unit will present suggestions in a gentle manner. If the user is stressed, the suggestion unit will present suggestions in a specific and easy-to-understand manner. If the user is in a hurry, the suggestion unit will present suggestions in a concise and rapid manner. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions 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-described processes in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust the way it presents its suggestions.

[0079] The suggestion unit can improve the accuracy of its suggestions based on detailed information about the user's health condition. For example, the suggestion unit can analyze the user's health condition in detail and suggest the most suitable hot spring and bathing method. The suggestion unit can suggest hot springs with specific water properties according to the user's medical condition. The suggestion unit can update its suggestions in response to changes in the user's health condition. This allows the suggestion unit to make more appropriate suggestions by improving the accuracy of its suggestions based on detailed information about the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input detailed data about the user's health condition into a generating AI, which can then analyze the data to improve the accuracy of its suggestions.

[0080] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's past hot spring usage history. For example, the suggestion unit analyzes the user's past usage history and proposes the most suitable hot spring. Based on the user's past usage history, the suggestion unit proposes an effective bathing method. The suggestion unit customizes the proposed content by referring to the user's past usage history. In this way, the suggestion unit can make more appropriate suggestions by considering the user's past hot spring usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hot spring usage history data into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0081] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. The suggestion unit can estimate emotions using, for example, facial recognition or voice analysis of the user. If the user is relaxed, the suggestion unit will prioritize suggestions that have a high relaxation effect. If the user is stressed, the suggestion unit will prioritize suggestions that have a stress-relieving effect. If the user is in a hurry, the suggestion unit will prioritize suggestions that can be executed quickly. In this way, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions 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 suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and determine the priority of suggestions.

[0082] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's lifestyle and dietary information. For example, the suggestion unit can analyze the user's lifestyle and propose the most suitable hot spring and bathing method. The suggestion unit can propose a hot spring and bathing method that contributes to health based on the user's dietary information. The suggestion unit can combine the user's lifestyle and dietary information to make the best suggestion. In this way, the suggestion unit can make more appropriate suggestions by considering the user's lifestyle and dietary information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and dietary information into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0083] The suggestion unit can customize its suggestions by considering the user's family structure and information about their companions. For example, the suggestion unit can suggest a hot spring that the whole family can enjoy, taking into account the user's family structure. The suggestion unit can suggest a hot spring that the group can enjoy, based on information about the user's companions. The suggestion unit combines the user's family structure and information about their companions to make the most appropriate suggestion. As a result, the suggestion unit can make more appropriate suggestions by considering the user's family structure and information about their companions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's family structure and information about their companions into a generating AI, which can then analyze the data and customize the suggestion.

[0084] The introduction unit can estimate the user's emotions and adjust its bathing method introduction based on those emotions. For example, the introduction unit might use facial recognition or voice analysis to estimate emotions. If the user is relaxed, the introduction unit will introduce the bathing method in a calm tone. If the user is stressed, the introduction unit will introduce the bathing method in a specific and easy-to-understand tone. If the user is in a hurry, the introduction unit will introduce the bathing method in a concise and rapid tone. This allows the introduction unit to provide a more appropriate introduction by adjusting its method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the introduction unit may be performed using AI, or not. For example, the introduction unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust its bathing method introduction.

[0085] The recommendation unit can customize the content of its recommendations for bathing methods according to the user's health condition and medical status. For example, the recommendation unit can analyze the user's health condition in detail and recommend the most suitable bathing method. The recommendation unit can recommend a specific bathing method according to the user's medical condition. The recommendation unit can update the content of its recommendations in response to changes in the user's health condition. In this way, the recommendation unit can recommend the most suitable bathing method according to the user's health condition and medical status. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input data about the user's health condition into a generating AI, which can then analyze the data and customize the recommendation content.

[0086] The introduction section can optimize its bathing method recommendations by referencing feedback from past users. For example, the introduction section analyzes feedback from past users to recommend the most suitable bathing method. The introduction section improves its recommendations based on user feedback. The introduction section customizes its recommendations by referring to past feedback. This allows the introduction section to recommend more appropriate bathing methods by referring to feedback from past users. Some or all of the above processes in the introduction section may be performed using AI, for example, or without AI. For example, the introduction section can input past user feedback data into a generating AI, which can then analyze the data to optimize the recommendations.

[0087] The introduction unit can estimate the user's emotions and adjust the order in which bathing methods are introduced based on the estimated emotions. The introduction unit estimates emotions using, for example, facial recognition or voice analysis of the user. If the user is relaxed, the introduction unit will prioritize introducing bathing methods that have a high relaxation effect. If the user is stressed, the introduction unit will prioritize introducing bathing methods that have a stress-relieving effect. If the user is in a hurry, the introduction unit will prioritize introducing bathing methods that can be performed quickly. In this way, the introduction unit can provide more appropriate recommendations by adjusting the order in which bathing methods are introduced 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 introduction unit may be performed using AI, for example, or not using AI. For example, the introduction unit can input the user's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the order in which bathing methods are introduced.

[0088] The recommendation section can customize the content of its recommendations for bathing methods by considering the user's living environment and climate conditions. For example, the recommendation section can recommend the most suitable bathing method considering the user's living environment. The recommendation section can recommend bathing methods that contribute to health based on the user's climate conditions. The recommendation section can provide the most suitable recommendations by combining the user's living environment and climate conditions. In this way, the recommendation section can recommend more appropriate bathing methods by considering the user's living environment and climate conditions. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input data on the user's living environment and climate conditions into a generating AI, which can then analyze the data and customize the recommendation content.

[0089] The recommendation section can adjust the content of its recommendations when introducing bathing methods, taking into account the user's cultural background and preferences. For example, the recommendation section can introduce the most suitable bathing method considering the user's cultural background. The recommendation section can introduce bathing methods that contribute to health based on the user's preferences. The recommendation section can make the most appropriate recommendations by combining the user's cultural background and preferences. In this way, the recommendation section can introduce more appropriate bathing methods by taking into account the user's cultural background and preferences. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation section can input data on the user's cultural background and preferences into a generating AI, and the generating AI can analyze the data and adjust the content of the recommendations.

[0090] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. The interface unit estimates emotions using, for example, facial recognition or voice analysis of the user. When the user is relaxed, the interface unit provides an interface with calm colors. When the user is stressed, the interface unit provides a simple and highly visible interface. When the user is in a hurry, the interface unit provides an interface that can be operated quickly. In this way, the interface unit can provide a more appropriate display by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust the interface display method.

[0091] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit analyzes the user's past operation history and provides the optimal display method. Based on the user's operation history, the interface unit selects a display method that improves usability. The interface unit customizes the display content by referring to the user's past operation history. In this way, the interface unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history data into a generating AI, and the generating AI can analyze the data and select the optimal display method.

[0092] The interface unit can optimize the displayed content according to the characteristics of the user's device when displaying the interface. For example, if the user is using a smartphone, the interface unit provides displayed content that matches the screen size. If the user is using a tablet, the interface unit provides displayed content optimized for a larger screen. If the user is using a desktop, the interface unit provides displayed content that includes detailed information. In this way, the interface unit can provide a more appropriate display by optimizing the displayed content according to the characteristics of the user's device. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user device characteristic data into a generating AI, and the generating AI can analyze the data to optimize the displayed content.

[0093] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, the interface unit estimates emotions using facial recognition or voice analysis. When the user is relaxed, the interface unit provides gentle operation procedures. When the user is stressed, the interface unit provides simple and intuitive operation procedures. When the user is in a hurry, the interface unit provides procedures that allow for quick operation. This allows the interface unit to perform more appropriate operations by adjusting the interface's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using AI, or not. For example, the interface unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust the interface's operation procedures.

[0094] The interface unit can provide optimal display content by considering the user's geographical location information when displaying the interface. For example, the interface unit can display information about nearby hot springs based on the user's current location. The interface unit can display information about hot springs related to the user's travel destination. The interface unit provides optimal display content based on the user's geographical location information. In this way, the interface unit can provide more appropriate display content by considering the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal display content.

[0095] The interface unit can customize the displayed content when displaying the interface, taking into account the user's language settings and cultural background. For example, the interface unit can automatically set the language of the interface based on the user's language settings. The interface unit can provide appropriate expressions and designs, taking into account the user's cultural background. The interface unit can provide optimal displayed content by combining the user's language settings and cultural background. In this way, the interface unit can provide more appropriate displayed content by taking into account the user's language settings and cultural background. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input data on the user's language settings and cultural background into a generating AI, which can then analyze the data and customize the displayed content.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, it can suggest a hot spring and bathing method that is highly relaxing. If the user is stressed, it can suggest a hot spring and bathing method that is stress-relieving. If the user is in a hurry, it can suggest a hot spring and bathing method that provides quick relief. In this way, the suggestion unit can suggest the optimal hot spring and bathing method according to the user's emotions. Emotion estimation is achieved using, for example, 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. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the user's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the content of its suggestions.

[0098] The data collection unit can collect information on the water quality of hot springs by analyzing user reviews and ratings of hot springs. For example, it can analyze user reviews and prioritize collecting information on highly-rated hot springs. Based on user ratings, it can collect highly reliable hot spring information. By combining user reviews and ratings, it can collect optimal hot spring information. In this way, the data collection unit can collect highly reliable hot spring information by analyzing user reviews and ratings. 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 user review and rating data into a generating AI, which can then analyze the data to collect highly reliable hot spring information.

[0099] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it will present suggestions in a gentle manner. If the user is stressed, it will present suggestions in a specific and easy-to-understand manner. If the user is in a hurry, it will present suggestions in a concise and quick manner. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the way it presents suggestions.

[0100] The data collection unit can collect information on the quality of hot springs while considering the geographical conditions and seasonal variations of the hot springs. For example, it can prioritize collecting information on easily accessible hot springs by considering geographical conditions. It can also collect information on hot springs for each season by considering seasonal variations. By combining geographical conditions and seasonal variations, it can collect the most relevant hot spring information. In this way, the data collection unit can collect more relevant hot spring information by considering geographical conditions and seasonal variations. 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 data on geographical conditions and seasonal variations into a generating AI, which can then analyze the data to collect the most relevant hot spring information.

[0101] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's lifestyle and dietary information. For example, it can analyze the user's lifestyle and propose the most suitable hot spring and bathing method. Based on the user's dietary information, it can propose a hot spring and bathing method that contributes to health. It can combine the user's lifestyle and dietary information to make the most suitable suggestion. In this way, the suggestion unit can make more appropriate suggestions by considering the user's lifestyle and dietary information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and dietary information into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of hot spring information collection based on the estimated emotions. For example, emotions can be estimated using facial recognition or voice analysis of the user. If the user is relaxed, the collection frequency is reduced and only necessary information is collected. If the user is stressed, the collection frequency is increased and detailed information is provided. If the user is in a hurry, the collection timing is accelerated to provide information quickly. In this way, the data collection unit can provide more appropriate information by adjusting the timing of hot spring information collection according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust the collection timing.

[0103] The suggestion unit can propose the most suitable hot spring and bathing method by considering the user's past hot spring usage history. For example, it can analyze the user's past usage history and propose the most suitable hot spring. It can propose an effective bathing method based on the user's past usage history. It can customize the proposed content by referring to the user's past usage history. In this way, the suggestion unit can make more appropriate suggestions by considering the user's past hot spring usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hot spring usage history data into a generating AI, which can then analyze the data and propose the most suitable hot spring and bathing method.

[0104] The introduction unit can estimate the user's emotions and adjust its bathing method introduction based on those emotions. For example, it can estimate emotions using facial recognition or voice analysis. If the user is relaxed, it will introduce the bathing method in a calm tone. If the user is stressed, it will introduce the bathing method in a specific and easy-to-understand tone. If the user is in a hurry, it will introduce the bathing method in a concise and quick tone. In this way, the introduction unit can provide a more appropriate introduction by adjusting its method of introducing the bathing method according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the introduction unit may be performed using AI, or not using AI. For example, the introduction unit can input user facial data into a generative AI, which can analyze the data to estimate emotions and adjust its method of introducing the bathing method.

[0105] The interface unit can provide optimal display content by considering the user's geographical location information when displaying the interface. For example, it can display information about nearby hot springs based on the user's current location, or display information about hot springs related to the user's travel destination. It provides optimal display content based on the user's geographical location information. In this way, the interface unit can provide more appropriate display content by considering the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal display content.

[0106] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, emotions can be estimated using facial recognition or voice analysis. If the user is relaxed, an interface with calm colors is provided. If the user is stressed, a simple and highly visible interface is provided. If the user is in a hurry, an interface that can be operated quickly is provided. In this way, the interface unit can provide a more appropriate display by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using, for example, 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. Some or all of the above processing in the interface unit may be performed using AI, or not using AI. For example, the interface unit can input user facial data into a generative AI, the generative AI can analyze the data to estimate emotions, and adjust the interface display method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects information on the water quality of hot springs. The collection unit collects and updates information on the water quality of hot springs in real time, for example, from the internet. The collection unit can collect information such as the temperature, pH value, and contained components of the hot springs. Step 2: The Proposal Department proposes hot springs and bathing methods tailored to the user's health condition based on the spring quality information collected by the Collection Department. For example, the Proposal Department proposes the most suitable hot springs and bathing methods based on the user's health condition and medical history. The Proposal Department makes suggestions based on information such as the user's medical history, current symptoms, and doctor's diagnosis. Step 3: The introduction section introduces bathing methods based on medical evidence, using the hot springs and bathing methods proposed by the suggestion section. The introduction section introduces traditional therapeutic bathing methods and health-benefiting bathing methods, such as timed bathing, alternating hot and cold bathing, and gradual bathing. The introduction section introduces bathing methods based on medical evidence, such as medical papers and clinical trial results. Step 4: The interface unit provides the user with the hot springs and bathing methods proposed by the proposal unit. The interface unit allows users to search for hot springs, make reservations, and receive therapy information via smartphones or web applications, for example. The interface unit makes it easy for users to experience hot spring therapy.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, proposal unit, introduction unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects and updates hot spring water quality information from the internet in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal hot spring and bathing method based on the user's health condition. The introduction unit is implemented by the specific processing unit 290 of the data processing unit 12 and introduces bathing methods based on medical evidence. The interface unit is implemented by the control unit 46A of the smart device 14 and allows users to search for hot springs, make reservations, and receive therapy guidance via smartphones or web applications. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, proposal unit, introduction unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects and updates hot spring water quality information from the internet in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal hot spring and bathing method based on the user's health condition. The introduction unit is implemented by the specific processing unit 290 of the data processing unit 12 and introduces bathing methods based on medical evidence. The interface unit is implemented by the control unit 46A of the smart glasses 214 and allows users to search for hot springs, make reservations, and receive therapy guidance via smartphones or web applications. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, proposal unit, introduction unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects and updates hot spring water quality information from the internet in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal hot spring and bathing method based on the user's health condition. The introduction unit is implemented by the specific processing unit 290 of the data processing unit 12 and introduces bathing methods based on medical evidence. The interface unit is implemented by the control unit 46A of the headset terminal 314 and allows users to search for hot springs, make reservations, and receive therapy guidance via smartphones or web applications. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, proposal unit, introduction unit, and interface unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects and updates hot spring water quality information from the internet in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal hot spring and bathing method based on the user's health condition. The introduction unit is implemented by the specific processing unit 290 of the data processing unit 12 and introduces bathing methods based on medical evidence. The interface unit is implemented by the control unit 46A of the robot 414 and allows users to search for hot springs, make reservations, and receive therapy guidance via smartphones or web applications. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection department that collects information on the water quality of hot springs, Based on the spring water quality information collected by the aforementioned collection unit, the proposal unit proposes a hot spring and bathing method tailored to the user's health condition. Based on the hot springs and bathing methods proposed by the aforementioned proposal section, there is an introduction section that introduces bathing methods based on medical evidence, The system includes an interface unit that provides the user with the hot springs and bathing methods proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect and update real-time information on the water quality of hot springs from the internet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest the most suitable hot spring and bathing method based on the user's health condition and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned introduction section is, This article introduces traditional bathing methods and health-promoting bathing techniques such as chronological bathing, alternating hot and cold baths, and gradual bathing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The interface unit is Search for hot springs, make reservations, and receive information about therapeutic treatments via smartphones or web apps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of hot spring information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information on the water quality of hot springs, we analyze past collection history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information on the water quality of hot springs, we take into consideration the geographical conditions and seasonal variations of the hot springs. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the hot spring information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information on the water quality of hot springs, the system prioritizes collecting highly relevant hot spring information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information on the water quality of hot springs, we analyze and gather reviews and ratings from hot spring users. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making suggestions, improve the accuracy of the suggestions based on detailed information about the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, we will consider the user's past hot spring usage history to suggest the most suitable hot spring and bathing method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we take into account the user's lifestyle and dietary information to suggest the most suitable hot spring and bathing method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we customize the proposal content by taking into account the user's family structure and information about their companions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned introduction section is, The system estimates the user's emotions and adjusts the way bathing methods are introduced based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned introduction section is, When introducing bathing methods, the content of the introduction will be customized according to the user's health condition and medical status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned introduction section is, When introducing bathing methods, we optimize the content by referring to feedback from past users. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned introduction section is, The system estimates the user's emotions and adjusts the order in which bathing methods are introduced based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned introduction section is, When introducing bathing methods, the content is customized to take into account the user's living environment and climate conditions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned introduction section is, When introducing bathing methods, we adjust the content to take into account the user's cultural background and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The interface unit is When displaying the interface, the displayed content is optimized according to the characteristics of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 27) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The interface unit is When displaying the interface, the system provides optimal display content that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The interface unit is When displaying the interface, the displayed content is customized to take into account the user's language settings and cultural background. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 collection department that collects information on the water quality of hot springs, Based on the spring water quality information collected by the aforementioned collection unit, the proposal unit proposes a hot spring and bathing method tailored to the user's health condition. Based on the hot springs and bathing methods proposed by the aforementioned proposal section, there is an introduction section that introduces bathing methods based on medical evidence, The system includes an interface unit that provides the user with the hot springs and bathing methods proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect and update real-time information on the water quality of hot springs from the internet. The system according to feature 1.

3. The aforementioned proposal section is, We suggest the most suitable hot spring and bathing method based on the user's health condition and medical history. The system according to feature 1.

4. The aforementioned introduction section is, This article introduces traditional bathing methods and health-promoting bathing techniques such as chronological bathing, alternating hot and cold baths, and gradual bathing. The system according to feature 1.

5. The interface unit is Search for hot springs, make reservations, and receive information about therapeutic treatments via smartphones or web apps. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of hot spring information collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting information on the water quality of hot springs, we analyze past collection history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting information on the water quality of hot springs, we take into consideration the geographical conditions and seasonal variations of the hot springs. The system according to feature 1.

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