system

Abstract

The system according to the embodiment aims to guide a user to an appropriate hospital based on the user's health condition. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit inputs the user's health status. The analysis unit analyzes the information input by the reception unit. The guidance unit provides guidance to nearby hospitals based on the diagnosis results generated by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult for users to find an appropriate hospital based on their own health condition.

[0005] The system according to the embodiment aims to guide a user to an appropriate hospital based on the user's health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit inputs the user's health status. The analysis unit analyzes the information input by the reception unit. The guidance unit provides guidance to nearby hospitals based on the diagnosis results generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can guide the user to an appropriate hospital based on the user's health condition. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health consultation system according to an embodiment of the present invention provides a user with a simple diagnosis and information about nearby hospitals in response to a consultation about their health status. In this health consultation system, the user inputs their health status, and AI analyzes the input information, generates a simple diagnosis, and provides information about nearby hospitals based on the diagnosis. For example, when a user inputs their health status, they input information about their symptoms and physical condition, such as "I have a headache," "I have a fever," or "I have a cough." This information is then input into the AI. The AI ​​then analyzes the input information and generates a simple diagnosis. Based on the input information, the AI ​​identifies possible illnesses and symptoms. For example, it generates a diagnosis such as "Probable cold" or "Suspected influenza." Furthermore, it provides information about nearby hospitals based on the diagnosis. The AI ​​searches for nearby hospitals based on the user's current location and provides information about medical institutions appropriate to the diagnosis. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals." This system allows users to easily understand their health status and quickly find the appropriate medical institution. For example, if a user has cold symptoms, they can be quickly referred to a nearby internal medicine clinic, allowing them to receive treatment quickly. In case of an emergency, the system can provide a prompt response by directing the user to a 24-hour emergency hospital. This allows the health status consultation system to easily grasp the user's health status and quickly find an appropriate medical institution.

[0029] A health consultation system according to an embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit inputs a user's health status. The user's health status may include, but is not limited to, body temperature, blood pressure, and detailed symptoms. The reception unit may accept, for example, text input, voice input, or image input. For example, a user may input "I have a headache" in text. Alternatively, a user may dictate "I have a fever" using voice input. Furthermore, a user may upload a photo of a rash using image input. The analysis unit uses AI to analyze the information input by the reception unit. The analysis may be performed using, for example, a medical database or past diagnostic data. For example, the AI ​​may identify possible illnesses and symptoms based on the input information. For example, the AI ​​may generate a diagnosis such as "high probability of cold" or "suspected influenza." The guidance unit provides guidance to nearby hospitals based on the diagnosis result generated by the analysis unit. The guidance may be performed based, for example, on the user's current location information. For example, the AI ​​may search for nearby hospitals based on the user's current location information and provide guidance to a medical institution that matches the diagnosis result. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals." This allows the health status consultation system according to the embodiment to easily grasp the health status of the user and quickly find an appropriate medical institution.

[0030] The reception unit can accept any one of text input, voice input, and image input. For example, the reception unit can allow a user to input "I have a headache" in text. The reception unit can also dictate "I have a fever" using voice input. Furthermore, the reception unit can also upload a photo of a rash using image input. This allows users to input their health status in a variety of ways. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data acquired using voice input into a generation AI and have the generation AI convert the voice data into text data.

[0031] The analysis unit can perform analysis by utilizing a medical database or past diagnostic data. The analysis unit performs analysis by utilizing, for example, a medical database. For example, the analysis unit refers to an electronic medical record or a medical literature database and analyzes input information. The analysis unit can also perform analysis by utilizing past diagnostic data. For example, the analysis unit performs analysis based on data on the patient's medical history and diagnostic results. This improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a medical database or past diagnostic data into the generation AI and have the generation AI perform the analysis.

[0032] The guidance unit can provide detailed information on medical specialties, consultation hours, and evaluations. The guidance unit, for example, provides detailed information on medical specialties. For example, the guidance unit provides information on medical specialties such as internal medicine, surgery, and dermatology. The guidance unit can also provide detailed information on consultation hours. For example, the guidance unit provides information such as weekday consultation hours and holiday consultation hours. The guidance unit can also provide detailed information on evaluations. For example, the guidance unit provides information such as patient reviews and medical institution certifications. This allows the user to obtain detailed information on medical institutions. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can input information on medical specialties, consultation hours, and evaluations into the generation AI and have the generation AI provide the detailed information.

[0033] The guidance unit can search for nearby hospitals based on the user's current location information and guide the user to a medical institution suitable for the diagnosis results. The guidance unit, for example, searches for nearby hospitals based on the user's current location information. For example, the guidance unit identifies the user's current location using GPS data or an IP address and searches for nearby hospitals. The guidance unit can also guide the user to a medical institution suitable for the diagnosis results. For example, the guidance unit guides the user to a nearby internal medicine clinic based on the cold diagnosis results. This allows the user to quickly find an appropriate medical institution. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the user's current location information into the generation AI and cause the generation AI to search for nearby hospitals.

[0034] The guidance unit can provide voice guidance or map display. The guidance unit, for example, provides voice guidance. For example, the guidance unit uses voice synthesis technology to provide voice guidance to the user. The guidance unit can also display a map. For example, the guidance unit uses map data to provide visual guidance to the user. This allows the user to receive guidance both visually and audibly. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input data for voice guidance or map display into a generation AI and have the generation AI provide the guidance.

[0035] The reception unit can analyze the user's past health condition input history and suggest the optimal input method. For example, the reception unit can automatically display symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, image, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input during a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0036] When inputting the health status, the reception unit can filter the input content based on the user's current living situation and areas of interest. For example, if the user feels that they are not getting enough exercise, the reception unit can cause the user to input exercise-related symptoms preferentially. Furthermore, if the user is interested in their diet, the reception unit can cause the user to input diet-related symptoms preferentially. Furthermore, if the user is feeling stressed, the reception unit can cause the user to input mental health-related symptoms preferentially. This makes it possible to provide input content according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to filter the input content.

[0037] When inputting health status, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives at high altitude, the reception unit can prioritize input of symptoms related to altitude sickness. Furthermore, if the user lives in an urban area, the reception unit can prioritize input of symptoms related to health issues specific to the city. Furthermore, if the user lives by the sea, the reception unit can prioritize input of symptoms related to allergies caused by marine life. This makes it possible to provide input content based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting the health status. For example, the reception unit can automatically input symptoms frequently mentioned by the user on social media. The reception unit can also input related symptoms based on health information shared by the user on social media. Furthermore, the reception unit can also input related symptoms based on information from health-related accounts the user follows on social media. This makes it possible to provide input content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input related information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This makes it possible to provide analysis results according to the importance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies a symptom analysis algorithm to information regarding symptoms. The analysis unit can also apply a physical condition analysis algorithm to information regarding physical conditions. Furthermore, the analysis unit can apply a lifestyle habit analysis algorithm to information regarding lifestyle habits. This makes it possible to provide analysis results according to the category of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. For example, the analysis unit prioritizes analysis of recently input information. The analysis unit can also postpone information input in the past. Furthermore, the analysis unit can prioritize analysis of information input during a specific time period. This makes it possible to provide analysis results according to the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data of the input information to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can also appropriately analyze information with a medium degree of relevance. This makes it possible to provide analysis results according to the relevance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input information to the generation AI and have the generation AI adjust the order of analysis.

[0043] The guidance unit can adjust the level of detail of the guidance based on the importance of the diagnostic result when providing guidance. For example, the guidance unit provides detailed guidance for diagnostic results with high importance. The guidance unit can also provide brief guidance for diagnostic results with low importance. Furthermore, the guidance unit can also provide guidance with an appropriate level of detail for diagnostic results with medium importance. This makes it possible to provide a guidance method according to the importance of the diagnostic result. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input importance data of the diagnostic result to the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0044] The guidance unit can apply different guidance algorithms depending on the category of the diagnostic result when providing guidance. For example, the guidance unit can apply an internal medicine guidance algorithm to diagnostic results related to internal medicine. The guidance unit can also apply a surgery guidance algorithm to diagnostic results related to surgery. The guidance unit can also apply a psychiatry guidance algorithm to diagnostic results related to psychiatry. This makes it possible to provide guidance methods according to the category of the diagnostic result. Some or all of the above-mentioned processing in the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can input category data of the diagnostic result into the generation AI and cause the generation AI to apply different guidance algorithms.

[0045] When providing guidance, the guidance unit can determine the priority of guidance based on the time of submission of the diagnostic results. For example, the guidance unit can prioritize guidance of the most recently generated diagnostic results. The guidance unit can also postpone guidance of diagnostic results generated in the past. Furthermore, the guidance unit can prioritize guidance of diagnostic results generated in a specific time period. This makes it possible to provide a guidance method according to the time of submission of the diagnostic results. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input data on the time of submission of the diagnostic results to the generation AI and have the generation AI determine the priority of guidance.

[0046] The guidance unit can adjust the order of guidance based on the relevance of the diagnostic results when providing guidance. For example, the guidance unit prioritizes guidance of highly relevant diagnostic results. The guidance unit can also postpone guidance of diagnostic results with low relevance. Furthermore, the guidance unit can also appropriately provide guidance of diagnostic results with medium relevance. This makes it possible to provide a guidance method according to the relevance of the diagnostic results. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input relevance data of the diagnostic results to the generation AI and cause the generation AI to adjust the order of guidance.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When the user inputs their health status, the reception unit can automatically acquire the user's past health data and complement the input content. For example, by referencing symptoms and diagnosis results previously input by the user and comparing them with the current input content, more accurate information can be provided. The reception unit can also present predicted symptoms and illnesses based on the user's past health data. Furthermore, the reception unit can prioritize the input content based on the user's past health data, allowing important information to be input first. This makes it possible to utilize the user's past health data to input health status more accurately and efficiently.

[0049] The analysis unit can take into account the user's lifestyle data when analyzing the user's health status. For example, by incorporating data on the user's diet, exercise, sleep, etc. into the analysis, more comprehensive diagnostic results can be provided. The analysis unit can also predict health risks and suggest preventive measures based on the user's lifestyle data. Furthermore, the analysis unit can provide specific advice for improving health based on the user's lifestyle data. This allows for more accurate analysis results that take the user's lifestyle into account.

[0050] The guidance unit can recommend appropriate health management apps and devices based on the user's health condition. For example, if the user inputs a specific symptom, the guidance unit can recommend apps and devices that address that symptom. The guidance unit can also customize and recommend apps and devices that are useful for health management based on the user's health data. Furthermore, the guidance unit can set periodic health checks and reminders according to the user's health condition. This allows the user to manage their own health more effectively.

[0051] The guidance unit can provide appropriate health information and advice based on the user's health condition. For example, if the user inputs a specific symptom, the unit can provide health information and advice related to that symptom. The guidance unit can also suggest preventive measures and ways to improve lifestyle habits based on the user's health data. Furthermore, the guidance unit can set periodic health checks and reminders according to the user's health condition. This allows the user to manage their own health more effectively.

[0052] The analysis unit can take the user's genetic information into account when analyzing the user's health data. For example, the analysis unit can predict the risk of a particular disease or symptom based on the user's genetic information. The analysis unit can also suggest preventive measures or treatments based on the user's genetic information. Furthermore, the analysis unit can provide specific advice for health management based on the user's genetic information. This allows for more accurate analysis results that take the user's genetic information into account.

[0053] When inputting the user's health status, the reception unit can adjust the input content taking into account the user's current activity status. For example, if the user is exercising, the reception unit can prioritize input of exercise-related symptoms. Also, if the user is resting, the reception unit can prioritize input of relaxation-related symptoms. Furthermore, if the user is working, the reception unit can prioritize input of stress-related symptoms. This makes it possible to provide input content that is appropriate for the user's current activity status.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit inputs the user's health status. The user's health status may include, for example, body temperature, blood pressure, and details of symptoms. The reception unit can accept text input, voice input, or image input. For example, the user can input "I have a headache" in text, or dictate "I have a fever" using voice input. Furthermore, the user can upload a photo of a rash using image input. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is carried out using medical databases and past diagnostic data. For example, based on the entered information, the AI ​​identifies possible illnesses and symptoms and generates a diagnosis such as "high probability of having a cold" or "suspected of having the flu." Step 3: The guidance unit provides information about nearby hospitals based on the diagnosis results generated by the analysis unit. Guidance is provided based on the user's current location information. For example, the AI ​​searches for nearby hospitals based on the user's current location information and provides information about medical institutions that are appropriate for the diagnosis results. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals."

[0056] (Example 2) A health consultation system according to an embodiment of the present invention provides a user with a simple diagnosis and information about nearby hospitals in response to a consultation about their health status. In this health consultation system, the user inputs their health status, and AI analyzes the input information, generates a simple diagnosis, and provides information about nearby hospitals based on the diagnosis. For example, when a user inputs their health status, they input information about their symptoms and physical condition, such as "I have a headache," "I have a fever," or "I have a cough." This information is then input into the AI. The AI ​​then analyzes the input information and generates a simple diagnosis. Based on the input information, the AI ​​identifies possible illnesses and symptoms. For example, it generates a diagnosis such as "Probable cold" or "Suspected influenza." Furthermore, it provides information about nearby hospitals based on the diagnosis. The AI ​​searches for nearby hospitals based on the user's current location and provides information about medical institutions appropriate to the diagnosis. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals." This system allows users to easily understand their health status and quickly find the appropriate medical institution. For example, if a user has cold symptoms, they can be quickly referred to a nearby internal medicine clinic, allowing them to receive treatment quickly. In case of an emergency, the system can provide a prompt response by directing the user to a 24-hour emergency hospital. This allows the health status consultation system to easily grasp the user's health status and quickly find an appropriate medical institution.

[0057] A health consultation system according to an embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit inputs a user's health status. The user's health status may include, but is not limited to, body temperature, blood pressure, and detailed symptoms. The reception unit may accept, for example, text input, voice input, or image input. For example, a user may input "I have a headache" in text. Alternatively, a user may dictate "I have a fever" using voice input. Furthermore, a user may upload a photo of a rash using image input. The analysis unit uses AI to analyze the information input by the reception unit. The analysis may be performed using, for example, a medical database or past diagnostic data. For example, the AI ​​may identify possible illnesses and symptoms based on the input information. For example, the AI ​​may generate a diagnosis such as "high probability of cold" or "suspected influenza." The guidance unit provides guidance to nearby hospitals based on the diagnosis result generated by the analysis unit. The guidance may be performed based, for example, on the user's current location information. For example, the AI ​​may search for nearby hospitals based on the user's current location information and provide guidance to a medical institution that matches the diagnosis result. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals." This allows the health status consultation system according to the embodiment to easily grasp the health status of the user and quickly find an appropriate medical institution.

[0058] The reception unit can accept any one of text input, voice input, and image input. For example, the reception unit can allow a user to input "I have a headache" in text. The reception unit can also dictate "I have a fever" using voice input. Furthermore, the reception unit can also upload a photo of a rash using image input. This allows users to input their health status in a variety of ways. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data acquired using voice input into a generation AI and have the generation AI convert the voice data into text data.

[0059] The analysis unit can perform analysis by utilizing a medical database or past diagnostic data. The analysis unit performs analysis by utilizing, for example, a medical database. For example, the analysis unit refers to an electronic medical record or a medical literature database and analyzes input information. The analysis unit can also perform analysis by utilizing past diagnostic data. For example, the analysis unit performs analysis based on data on the patient's medical history and diagnostic results. This improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a medical database or past diagnostic data into the generation AI and have the generation AI perform the analysis.

[0060] The guidance unit can provide detailed information on medical specialties, consultation hours, and evaluations. The guidance unit, for example, provides detailed information on medical specialties. For example, the guidance unit provides information on medical specialties such as internal medicine, surgery, and dermatology. The guidance unit can also provide detailed information on consultation hours. For example, the guidance unit provides information such as weekday consultation hours and holiday consultation hours. The guidance unit can also provide detailed information on evaluations. For example, the guidance unit provides information such as patient reviews and medical institution certifications. This allows the user to obtain detailed information on medical institutions. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can input information on medical specialties, consultation hours, and evaluations into the generation AI and have the generation AI provide the detailed information.

[0061] The guidance unit can search for nearby hospitals based on the user's current location information and guide the user to a medical institution suitable for the diagnosis results. The guidance unit, for example, searches for nearby hospitals based on the user's current location information. For example, the guidance unit identifies the user's current location using GPS data or an IP address and searches for nearby hospitals. The guidance unit can also guide the user to a medical institution suitable for the diagnosis results. For example, the guidance unit guides the user to a nearby internal medicine clinic based on the cold diagnosis results. This allows the user to quickly find an appropriate medical institution. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the user's current location information into the generation AI and cause the generation AI to search for nearby hospitals.

[0062] The guidance unit can provide voice guidance or map display. The guidance unit, for example, provides voice guidance. For example, the guidance unit uses voice synthesis technology to provide voice guidance to the user. The guidance unit can also display a map. For example, the guidance unit uses map data to provide visual guidance to the user. This allows the user to receive guidance both visually and audibly. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input data for voice guidance or map display into a generation AI and have the generation AI provide the guidance.

[0063] The reception unit can estimate the user's emotions and adjust the input method based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize voice input and provide an interface that gives the user a sense of security. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize concise text input and enable the user to quickly enter their health status. This allows the optimal input method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] The reception unit can analyze the user's past health condition input history and suggest the optimal input method. For example, the reception unit can automatically display symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, image, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input during a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0065] When inputting the health status, the reception unit can filter the input content based on the user's current living situation and areas of interest. For example, if the user feels that they are not getting enough exercise, the reception unit can cause the user to input exercise-related symptoms preferentially. Furthermore, if the user is interested in their diet, the reception unit can cause the user to input diet-related symptoms preferentially. Furthermore, if the user is feeling stressed, the reception unit can cause the user to input mental health-related symptoms preferentially. This makes it possible to provide input content according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to filter the input content.

[0066] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize input of symptoms with high urgency. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed symptoms. Furthermore, if the user is in a hurry, the reception unit can also prompt the user to input only major symptoms. This allows the input content to be prioritized according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] When inputting health status, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives at high altitude, the reception unit can prioritize input of symptoms related to altitude sickness. Furthermore, if the user lives in an urban area, the reception unit can prioritize input of symptoms related to health issues specific to the city. Furthermore, if the user lives by the sea, the reception unit can prioritize input of symptoms related to allergies caused by marine life. This makes it possible to provide input content based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0068] The reception unit can analyze the user's social media activity and input related information when inputting the health status. For example, the reception unit can automatically input symptoms frequently mentioned by the user on social media. The reception unit can also input related symptoms based on health information shared by the user on social media. Furthermore, the reception unit can also input related symptoms based on information from health-related accounts the user follows on social media. This makes it possible to provide input content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input related information.

[0069] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a concise expression that gives a sense of security. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This makes it possible to provide analysis results that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This makes it possible to provide analysis results according to the importance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies a symptom analysis algorithm to information regarding symptoms. The analysis unit can also apply a physical condition analysis algorithm to information regarding physical conditions. Furthermore, the analysis unit can apply a lifestyle habit analysis algorithm to information regarding lifestyle habits. This makes it possible to provide analysis results according to the category of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply different analysis algorithms.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a concise analysis result if the user is in a hurry. This allows the length of the analysis result to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. For example, the analysis unit prioritizes analysis of recently input information. The analysis unit can also postpone information input in the past. Furthermore, the analysis unit can prioritize analysis of information input during a specific time period. This makes it possible to provide analysis results according to the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data of the input information to the generation AI and have the generation AI determine the analysis priority.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can also appropriately analyze information with a medium degree of relevance. This makes it possible to provide analysis results according to the relevance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input information to the generation AI and have the generation AI adjust the order of analysis.

[0075] The guidance unit can estimate the user's emotions and adjust the guidance method based on the estimated user's emotions. For example, if the user is feeling anxious, the guidance unit can provide a guidance method that gives the user a sense of security. Furthermore, if the user is relaxed, the guidance unit can provide a detailed guidance method. Furthermore, if the user is in a hurry, the guidance unit can provide a concise guidance method. This makes it possible to provide a guidance method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The guidance unit can adjust the level of detail of the guidance based on the importance of the diagnostic result when providing guidance. For example, the guidance unit provides detailed guidance for diagnostic results with high importance. The guidance unit can also provide brief guidance for diagnostic results with low importance. Furthermore, the guidance unit can also provide guidance with an appropriate level of detail for diagnostic results with medium importance. This makes it possible to provide a guidance method according to the importance of the diagnostic result. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input importance data of the diagnostic result to the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0077] The guidance unit can apply different guidance algorithms depending on the category of the diagnostic result when providing guidance. For example, the guidance unit can apply an internal medicine guidance algorithm to diagnostic results related to internal medicine. The guidance unit can also apply a surgery guidance algorithm to diagnostic results related to surgery. The guidance unit can also apply a psychiatry guidance algorithm to diagnostic results related to psychiatry. This makes it possible to provide guidance methods according to the category of the diagnostic result. Some or all of the above-mentioned processing in the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can input category data of the diagnostic result into the generation AI and cause the generation AI to apply different guidance algorithms.

[0078] The guidance unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. For example, if the user is feeling anxious, the guidance unit can provide short, to-the-point guidance. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance. Furthermore, if the user is in a hurry, the guidance unit can provide concise guidance. This allows the length of guidance to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without AI. For example, the guidance unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] When providing guidance, the guidance unit can determine the priority of guidance based on the time of submission of the diagnostic results. For example, the guidance unit can prioritize guidance of the most recently generated diagnostic results. The guidance unit can also postpone guidance of diagnostic results generated in the past. Furthermore, the guidance unit can prioritize guidance of diagnostic results generated in a specific time period. This makes it possible to provide a guidance method according to the time of submission of the diagnostic results. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input data on the time of submission of the diagnostic results to the generation AI and have the generation AI determine the priority of guidance.

[0080] The guidance unit can adjust the order of guidance based on the relevance of the diagnostic results when providing guidance. For example, the guidance unit prioritizes guidance of highly relevant diagnostic results. The guidance unit can also postpone guidance of diagnostic results with low relevance. Furthermore, the guidance unit can also appropriately provide guidance of diagnostic results with medium relevance. This makes it possible to provide a guidance method according to the relevance of the diagnostic results. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input relevance data of the diagnostic results to the generation AI and cause the generation AI to adjust the order of guidance. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows the user to input health status information by text, voice, or image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI and generates a diagnosis result. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and guides the user to nearby hospitals based on the user's current location information. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and camera 42 of the smart glasses 214, allowing the user to input their health status using voice or images. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI to generate a diagnosis. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and guides the user to nearby hospitals based on the user's current location information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and guidance unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and camera 42 of the headset-type terminal 314, allowing the user to input their health condition using voice or images. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI to generate a diagnosis result. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and guides the user to nearby hospitals based on information about the user's current location. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and camera 42 of the robot 414, allowing the user to input their health condition using voice or images. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using AI to generate a diagnosis result. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and guides the user to nearby hospitals based on information about the user's current location.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When the user inputs their health status, the reception unit can automatically acquire the user's past health data and complement the input content. For example, by referencing symptoms and diagnosis results previously input by the user and comparing them with the current input content, more accurate information can be provided. The reception unit can also present predicted symptoms and illnesses based on the user's past health data. Furthermore, the reception unit can prioritize the input content based on the user's past health data, allowing important information to be input first. This makes it possible to utilize the user's past health data to input health status more accurately and efficiently.

[0083] The analysis unit can take into account the user's lifestyle data when analyzing the user's health status. For example, by incorporating data on the user's diet, exercise, sleep, etc. into the analysis, more comprehensive diagnostic results can be provided. The analysis unit can also predict health risks and suggest preventive measures based on the user's lifestyle data. Furthermore, the analysis unit can provide specific advice for improving health based on the user's lifestyle data. This allows for more accurate analysis results that take the user's lifestyle into account.

[0084] The guidance unit can recommend appropriate health management apps and devices based on the user's health condition. For example, if the user inputs a specific symptom, the guidance unit can recommend apps and devices that address that symptom. The guidance unit can also customize and recommend apps and devices that are useful for health management based on the user's health data. Furthermore, the guidance unit can set periodic health checks and reminders according to the user's health condition. This allows the user to manage their own health more effectively.

[0085] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling anxious, a calm color scheme and a simple design can be used. If the user is relaxed, a colorful and fun design can be provided. Furthermore, if the user is in a hurry, a design that is intuitive and quick to operate can be provided. This makes it possible to provide an optimal input interface according to the user's emotions.

[0086] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is feeling anxious, it can use concise, positive language that gives a sense of security. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that focus on the main points. This makes it possible to present optimal analysis results according to the user's emotions.

[0087] The guidance unit can provide appropriate health information and advice based on the user's health condition. For example, if the user inputs a specific symptom, the unit can provide health information and advice related to that symptom. The guidance unit can also suggest preventive measures and ways to improve lifestyle habits based on the user's health data. Furthermore, the guidance unit can set periodic health checks and reminders according to the user's health condition. This allows the user to manage their own health more effectively.

[0088] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can provide positive feedback on the input content, giving the user a sense of security. If the user is relaxed, the reception unit can provide detailed feedback. Furthermore, if the user is in a hurry, the reception unit can provide concise and quick feedback. This makes it possible to provide optimal feedback according to the user's emotions.

[0089] The analysis unit can take the user's genetic information into account when analyzing the user's health data. For example, the analysis unit can predict the risk of a particular disease or symptom based on the user's genetic information. The analysis unit can also suggest preventive measures or treatments based on the user's genetic information. Furthermore, the analysis unit can provide specific advice for health management based on the user's genetic information. This allows for more accurate analysis results that take the user's genetic information into account.

[0090] The guidance unit can estimate the user's emotions and adjust the timing of guidance based on the estimated user emotions. For example, if the user feels anxious, the guidance unit can provide prompt guidance to give a sense of security. If the user feels relaxed, the guidance unit can provide detailed guidance. Furthermore, if the user is in a hurry, the guidance unit can provide concise and quick guidance. This makes it possible to provide optimal timing of guidance according to the user's emotions.

[0091] When inputting the user's health status, the reception unit can adjust the input content taking into account the user's current activity status. For example, if the user is exercising, the reception unit can prioritize input of exercise-related symptoms. Also, if the user is resting, the reception unit can prioritize input of relaxation-related symptoms. Furthermore, if the user is working, the reception unit can prioritize input of stress-related symptoms. This makes it possible to provide input content that is appropriate for the user's current activity status.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit inputs the user's health status. The user's health status may include, for example, body temperature, blood pressure, and details of symptoms. The reception unit can accept text input, voice input, or image input. For example, the user can input "I have a headache" in text, or dictate "I have a fever" using voice input. Furthermore, the user can upload a photo of a rash using image input. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is carried out using medical databases and past diagnostic data. For example, based on the entered information, the AI ​​identifies possible illnesses and symptoms and generates a diagnosis such as "high probability of having a cold" or "suspected of having the flu." Step 3: The guidance unit provides information about nearby hospitals based on the diagnosis results generated by the analysis unit. Guidance is provided based on the user's current location information. For example, the AI ​​searches for nearby hospitals based on the user's current location information and provides information about medical institutions that are appropriate for the diagnosis results. For example, it provides information such as "nearby internal medicine clinics" and "24-hour emergency hospitals."

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AIs including the generative AI may be replaced with rule-based processes, and rule-based processes may be replaced with processes performed by AIs including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's health status; an analysis unit that analyzes the information input by the reception unit; a guidance unit that guides the patient to nearby hospitals based on the diagnosis results generated by the analysis unit; Equipped with A system characterized by:

2. The reception unit Accepts either text input, voice input, or image input The system of claim 1 .

3. The analysis unit Conduct analysis using medical databases or past diagnostic data The system of claim 1 .

4. The guide unit is Provide detailed information on medical specialties, hours of operation, and evaluations The system of claim 1 .

5. The guide unit is Search for nearby hospitals based on the user's current location and guide them to the medical institution that best suits their diagnosis. The system of claim 1 .

6. The guide unit is Voice guidance or map display The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the input method based on the estimated user emotions The system of claim 1 .

8. The reception unit Analyzes the user's past health status input history and suggests the optimal input method The system of claim 1 .

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