System

Abstract

To provide a system for enabling a senior group to easily perform health medical consultation and to easily access a proper medical institution or welfare facility.SOLUTION: A system includes a reception part, a consultation part, a reservation part, and a notification part. When the senior presses the button, the reception unit starts the health medical consultation in person by the AI. The consultation unit conducts the health care consultation initiated by the reception unit. The reservation part executes medical examination reservation to the nearest medical institution or welfare facility or reservation of a moving means on the basis of the result of the health medical consultation obtained by the consultation part. The notification unit notifies the user of a reservation result executed by the reservation unit.SELECTED DRAWING: Figure 1

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] With conventional technology, it was difficult for seniors to use information devices to seek health and medical advice, and it was difficult for them to access appropriate medical institutions and welfare facilities.

[0005] The system according to the embodiment aims to enable seniors to easily receive health and medical consultations and to easily access appropriate medical institutions and welfare facilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a consultation unit, a reservation unit, and a notification unit. When a senior person presses a button in the reception unit, AI starts a face-to-face health and medical consultation. The consultation unit carries out the health and medical consultation started by the reception unit. The reservation unit makes an appointment for a medical examination at the nearest medical institution or welfare facility or a reservation for transportation based on the results of the health and medical consultation obtained by the consultation unit. The notification unit notifies the user of the results of the reservation made by the reservation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows seniors to easily receive health and medical consultations and easily access appropriate medical institutions and welfare facilities. [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) In a health and medical consultation system according to an embodiment of the present invention, an AI initiates a face-to-face health and medical consultation when a senior presses a button. Based on the results of the health and medical consultation, the AI ​​then schedules an appointment with the nearest medical institution or welfare facility and a transportation reservation, and notifies the user. In this health and medical consultation system, the AI ​​initiates a face-to-face health and medical consultation when a senior presses a button. The senior can easily start the consultation with a single button, without any special operations required. For example, if a senior feels unwell, the AI ​​simply presses a button to initiate a face-to-face consultation and listens to the senior's symptoms. The AI ​​then serves as the senior's daily conversation partner while providing a health and medical consultation. For example, the senior talks about daily events and changes in their health, and the AI ​​records this and keeps track of their health status. This allows the senior to manage their daily health without feeling isolated. Furthermore, based on the results of the health and medical consultation, the AI ​​schedules an appointment with the nearest medical institution or welfare facility as needed. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, the AI ​​schedules the appointment with the nearest medical institution. Furthermore, if the senior needs transportation to the medical institution, the AI ​​reserves a taxi or welfare vehicle. Finally, the AI ​​notifies the user of the results of the appointment and transportation reservation. For example, the date and time of the appointment and details of the transportation method are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind. The health and medical consultation system allows seniors to easily seek health and medical advice and receive the necessary medical services. It also serves as a daily conversation partner for seniors, reducing feelings of loneliness and helping to maintain their physical and mental health. The health and medical consultation system allows seniors to easily seek health and medical advice and receive the necessary medical services. It also serves as a daily conversation partner for seniors, reducing feelings of loneliness and helping to maintain their physical and mental health.

[0029] A health and medical consultation system according to an embodiment includes a reception unit, a consultation unit, a reservation unit, and a notification unit. When a senior person presses a button in the reception unit, AI initiates a face-to-face health and medical consultation. Seniors can easily initiate the consultation with a single button, without any special operations. For example, if a senior feels unwell, AI initiates a face-to-face consultation by simply pressing a button and listens to the senior's symptoms. In the consultation unit, AI serves as a daily conversation partner for the senior. For example, when a senior person talks about daily events and changes in their health, the AI ​​records this and grasps their health status. This allows seniors to manage their daily health without feeling isolated. The consultation unit can also grasp the senior's health status using voice recognition and natural language processing. For example, the AI ​​analyzes the senior's speech and evaluates their health status. Based on the results of the health and medical consultation, the AI ​​schedules appointments and transportation reservations at the nearest medical institution or welfare facility, as needed. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, it schedules an appointment at the nearest medical institution. Furthermore, if a senior needs transportation to a medical institution, the AI ​​will reserve a taxi or welfare vehicle. The notification unit notifies the user of the results of the consultation appointment or transportation reservation. For example, the date and time of the consultation appointment and details of the transportation method are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind. As a result, the health and medical consultation system according to the embodiment allows seniors to easily receive health and medical consultations and receive the necessary medical services. Furthermore, by providing a daily conversation partner for seniors, it can reduce feelings of loneliness and maintain physical and mental health.

[0030] The consultation unit can use AI to provide health and medical consultations to seniors while serving as their daily conversation partner. For example, the consultation unit can understand the senior's health condition by having the AI ​​talk to the senior about daily events and changes in their physical condition. The consultation unit can also have the AI ​​analyze the senior's comments and evaluate their health condition. Furthermore, the consultation unit can have the AI ​​record the senior's health condition and support daily health management. This allows seniors to manage their daily health without feeling lonely. Some or all of the above-mentioned processing in the consultation unit may be performed using, or without, the generation AI. For example, the consultation unit can input the senior's comments into the generation AI, which can then evaluate the senior's health condition.

[0031] The consultation unit can grasp the senior's health condition using voice recognition or natural language processing. The consultation unit can, for example, use voice recognition technology to analyze the senior's utterances and evaluate the health condition. The consultation unit can also use natural language processing technology to analyze the senior's utterances and evaluate the health condition. Furthermore, the consultation unit can combine voice recognition technology and natural language processing technology to more accurately grasp the senior's health condition. This makes it possible to accurately grasp the senior's health condition and provide appropriate health and medical consultations. Some or all of the above-mentioned processing in the consultation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the consultation unit can input the senior's utterances into a generation AI, which can evaluate the senior's health condition.

[0032] The reservation unit can analyze the senior's symptoms and, if it determines that a doctor's examination is necessary, make an appointment at the nearest medical institution. For example, if an AI analyzes the senior's symptoms and determines that a doctor's examination is necessary, the reservation unit can make an appointment at the nearest medical institution. The reservation unit can also analyze the senior's symptoms and select an appropriate medical institution. Furthermore, the reservation unit can analyze the senior's symptoms and determine the priority of appointments according to the urgency of the examination. This allows the senior to be examined at an appropriate medical institution. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input the senior's symptom data into the generation AI, which can select an appropriate medical institution and make an appointment.

[0033] The reservation unit can make reservations for taxis or welfare vehicles. For example, the reservation unit uses AI to analyze the senior's means of transportation and make reservations for taxis or welfare vehicles. The reservation unit can also analyze the senior's means of transportation and select the optimal means of transportation. Furthermore, the reservation unit can analyze the senior's means of transportation and determine reservation priorities according to the urgency of the travel. This allows seniors to secure transportation to medical institutions and welfare facilities. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input the senior's means of transportation data into the generation AI, which can select the optimal means of transportation and make a reservation.

[0034] The notification unit can notify the user of the date and time of the appointment and details of the means of transportation. For example, the notification unit uses AI to notify the user of the date and time of the appointment and details of the means of transportation. The notification unit can also notify the user of information when an appointment is changed or canceled. Furthermore, the notification unit can also notify the user of appointment reminders. This allows seniors to understand details of the appointment and the means of transportation. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input detailed data of the appointment and the means of transportation into the generation AI, and the generation AI can notify the user.

[0035] The reception unit can select the optimal reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit prepares related questions in advance based on the content of consultations the user has frequently made in the past. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past consultation history. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past consultation history data into the generation AI, which can then select the optimal reception method.

[0036] The reception unit can filter consultations based on the user's current health condition and lifestyle at the time of reception. For example, if the user is currently in poor health, the reception unit can prioritize urgent consultations. The reception unit can also suggest appropriate consultation content based on the user's lifestyle. Furthermore, the reception unit can prioritize consultations with specific specialists based on the user's health condition. This makes it possible to provide appropriate consultations based on the user's current health condition and lifestyle. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's health condition data into the generation AI, which can then filter appropriate consultation content.

[0037] The reception unit can select the optimal reception means depending on the user's input method when receiving the data. For example, if the user desires voice input, the reception unit can use voice recognition to receive the data. Furthermore, if the user desires text input, the reception unit can also use chat to receive the data. Furthermore, if the user transmits an image, the reception unit can also use image analysis to receive the data. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input data into the generation AI, which can then select the optimal reception means.

[0038] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information when receiving a consultation. For example, the reception unit can prioritize receiving consultations related to medical institutions close to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize receiving consultations related to that area. Furthermore, the reception unit can suggest optimal consultation content based on the user's geographical location information. This makes it possible to provide optimal consultations based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then suggest optimal consultation content.

[0039] The reception unit can analyze the user's social media activities and receive related consultations at the time of reception. The reception unit can receive related consultations based on, for example, health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related consultations. Furthermore, the reception unit can also receive related consultations by referring to the activities of the user's friends on social media. This makes it possible to provide optimal consultations based on the user's social media activities. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then suggest related consultations.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can also improve the reception method by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI, which can then customize the optimal reception method.

[0041] The consultation unit can adjust the level of detail of the consultation based on the importance of the health condition during the consultation. For example, if the user's health condition is serious, the consultation unit can provide a detailed consultation. Also, if the user's health condition is mild, the consultation unit can provide a brief consultation. Furthermore, the consultation unit can provide a consultation with an appropriate level of detail depending on the user's health condition. This allows the consultation to be provided with an appropriate level of detail depending on the user's health condition. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's health condition data into the generation AI, and the generation AI can adjust the level of detail of the consultation.

[0042] During a consultation, the consultation unit can apply different consultation algorithms depending on the category of the health condition. For example, if the user's health condition is related to the heart, the consultation unit can apply a consultation algorithm specialized in cardiac matters. Furthermore, if the user's health condition is related to the digestive system, the consultation unit can also apply a consultation algorithm specialized in digestive matters. Furthermore, if the user's health condition is related to the respiratory system, the consultation unit can also apply a consultation algorithm specialized in respiratory matters. This makes it possible to apply an appropriate consultation algorithm according to the user's health condition. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's health condition data into a generation AI, which can then apply an appropriate consultation algorithm.

[0043] During a consultation, the consultation unit can improve the accuracy of the consultation by referring to the user's past consultation results. The consultation unit, for example, suggests optimal consultation content based on the user's past consultation results. The consultation unit can also prioritize consultations regarding specific health conditions based on the user's past consultation results. Furthermore, the consultation unit can analyze the user's past consultation results and improve the accuracy of the consultation. This makes it possible to improve the accuracy of the consultation based on the user's past consultation results. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's past consultation result data into the generation AI, which can improve the accuracy of the consultation.

[0044] The consultation unit can determine the priority of the consultation based on the time when the health condition occurred at the time of consultation. For example, if the health condition occurred recently, the consultation unit can prioritize the consultation. In addition, if the health condition has continued for a long period of time, the consultation unit can also provide the consultation with normal priority. Furthermore, the consultation unit can also provide the consultation with appropriate priority depending on the time when the health condition occurred. This allows the consultation to be provided with appropriate priority depending on the time when the user's health condition occurred. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the consultation unit can input data on the time when the user's health condition occurred into the generation AI, and the generation AI can determine the priority of the consultation.

[0045] The consultation unit can adjust the order of consultations based on the relevance of the health conditions during consultations. For example, if the health conditions are highly relevant, the consultation unit can prioritize consultations. Also, if the health conditions are less relevant, the consultation unit can conduct consultations in the normal order. Furthermore, the consultation unit can conduct consultations in an appropriate order depending on the relevance of the health conditions. This allows consultations to be conducted in an appropriate order depending on the relevance of the user's health conditions. Some or all of the above-described processing in the consultation unit may be performed using, or without, a generation AI, for example. For example, the consultation unit can input relevance data of the user's health conditions into the generation AI, which can then adjust the order of consultations.

[0046] During a consultation, the consultation unit can adjust the use of technical terminology in the consultation depending on the user's level of expertise. For example, if the user has technical expertise, the consultation unit can use technical terminology to provide the consultation. Also, if the user does not have technical expertise, the consultation unit can use simple language to provide the consultation. Furthermore, the consultation unit can use appropriate language depending on the user's level of expertise. This allows the consultation to be provided using appropriate language depending on the user's level of expertise. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's level of expertise data into the generation AI, which can adjust the use of technical terminology in the consultation.

[0047] At the time of reservation, the reservation unit can analyze the user's past health condition and select the optimal reservation method. The reservation unit, for example, suggests the optimal medical institution based on the user's past health condition. The reservation unit can also preferentially suggest specific medical institutions based on the user's past health condition. Furthermore, the reservation unit can analyze the user's past health condition and select the optimal reservation method. This makes it possible to provide the optimal reservation method based on the user's past health condition. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's past health condition data into a generation AI, which can select the optimal reservation method.

[0048] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. The reservation unit, for example, suggests the optimal reservation method according to the user's living situation. The reservation unit can also preferentially suggest a specific reservation method taking the user's living situation into consideration. Furthermore, the reservation unit can customize the reservation method based on the user's living situation. This makes it possible to provide the optimal reservation method according to the user's current living situation. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's living situation data into the generation AI, which can then customize the reservation method.

[0049] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on user feedback. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. Furthermore, the reservation unit can improve the reservation method by reflecting user feedback. This makes it possible to provide the optimal reservation method based on user feedback. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input user feedback data into the generation AI, which can then improve the reservation method.

[0050] When making a reservation, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. For example, the reservation unit can prioritize reservations with medical institutions close to the user's current location. Also, if the user is in a specific area, the reservation unit can prioritize reservations with medical institutions related to that area. Furthermore, the reservation unit can suggest the optimal reservation method based on the user's geographical location information. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's geographical location information data into the generation AI, which can select the optimal reservation method.

[0051] At the time of making a reservation, the reservation unit can analyze the user's social media activity and suggest a reservation method. The reservation unit can suggest the optimal reservation method based on, for example, health information shared by the user on social media. The reservation unit can also analyze the user's social media posts and suggest a reservation at a relevant medical institution. Furthermore, the reservation unit can also suggest the optimal reservation method based on the activity of the user's friends on social media. This makes it possible to provide the optimal reservation method based on the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's social media activity data into a generation AI, which can then suggest the optimal reservation method.

[0052] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. The reservation unit can also preferentially suggest a specific reservation method based on the user's past feedback. Furthermore, the reservation unit can improve the reservation method by reflecting the user's feedback. This makes it possible to provide the optimal reservation method based on the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, the generation AI, for example. For example, the reservation unit can input the user's feedback data into the generation AI, which can then customize the reservation method.

[0053] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification unit, for example, preferentially suggests notification methods (audio, text, etc.) that the user has previously preferred. The notification unit can also predict and suggest a notification method to be used during a specific time period based on the user's past notification history. Furthermore, the notification unit can select the optimal notification method based on the user's past notification history. This makes it possible to provide the optimal notification method based on the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's past notification history data into the generation AI, which can then select the optimal notification method.

[0054] The notification unit can customize the notification means based on the user's current living situation at the time of notification. The notification unit, for example, suggests the optimal notification means according to the user's living situation. The notification unit can also preferentially suggest a specific notification means taking the user's living situation into consideration. Furthermore, the notification unit can customize the notification means based on the user's living situation. This makes it possible to provide the optimal notification means according to the user's current living situation. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the user's living situation data into the generation AI, which can then customize the notification means.

[0055] The notification unit can improve the notification method by reflecting user feedback at the time of notification. The notification unit, for example, suggests an optimal notification method based on user feedback. The notification unit can also preferentially suggest a specific notification method based on the user's past feedback. Furthermore, the notification unit can improve the notification method by reflecting user feedback. This makes it possible to provide an optimal notification method based on user feedback. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI, for example. For example, the notification unit can input user feedback data into the generation AI, which can then improve the notification method.

[0056] The notification unit can select the optimal notification method by taking into account the user's geographical location information when making a notification. For example, the notification unit can prioritize notifications to medical institutions close to the user's current location. Furthermore, if the user is in a specific area, the notification unit can also prioritize notifications related to that area. Furthermore, the notification unit can suggest the optimal notification method based on the user's geographical location information. This makes it possible to provide the optimal notification method based on the user's geographical location information. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's geographical location information data into the generation AI, which can then select the optimal notification method.

[0057] At the time of notification, the notification unit can analyze the user's social media activity and suggest a notification method. The notification unit can suggest the optimal notification method based on, for example, health information shared by the user on social media. The notification unit can also analyze the content of the user's social media posts and suggest related notifications. Furthermore, the notification unit can also suggest the optimal notification method based on the activity of the user's friends on social media. This makes it possible to provide the optimal notification method based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the user's social media activity data into a generation AI, which then suggests the optimal notification method.

[0058] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. The notification unit can, for example, suggest an optimal notification method based on the user's past feedback. The notification unit can also preferentially suggest a specific notification method based on the user's past feedback. Furthermore, the notification unit can also improve the notification method by reflecting the user's feedback. This makes it possible to provide an optimal notification method based on the user's past feedback. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input user feedback data into the generation AI, which can then customize the notification method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The health and medical consultation system may further include an analysis unit that collects the user's health data and analyzes long-term health trends. The analysis unit, for example, predicts future health risks based on the user's past health data. The analysis unit may also compare the user's health data with that of other users to understand general health trends. Furthermore, the analysis unit may provide individualized health advice based on the user's health data. This allows the user to understand their own health condition over the long term and take appropriate health management measures.

[0061] The health and medical consultation system may further include a nutritional evaluation unit that collects the user's dietary data and evaluates the nutritional balance. The nutritional evaluation unit may, for example, analyze the nutrients in the meals the user has eaten and suggest a balanced diet. The nutritional evaluation unit may also advise the user to increase the intake of specific nutrients depending on the user's health condition. Furthermore, the nutritional evaluation unit may provide a long-term dietary improvement plan based on the user's dietary data. This allows the user to review their own diet and maintain a healthy eating lifestyle.

[0062] The health and medical consultation system may further include an exercise evaluation unit that collects the user's exercise data and evaluates their exercise habits. The exercise evaluation unit may, for example, record the type and frequency of exercise performed by the user and propose an appropriate exercise plan. The exercise evaluation unit may also recommend specific exercises based on the user's health condition. Furthermore, the exercise evaluation unit may set long-term exercise goals based on the user's exercise data and support the achievement of those goals. This allows the user to review their exercise habits and live a healthier life.

[0063] The health care consultation system may further include a sleep evaluation unit that collects the user's sleep data and evaluates the quality of the user's sleep. The sleep evaluation unit may, for example, record the user's sleep time and sleep quality and suggest areas for improvement. The sleep evaluation unit may also recommend specific sleep habits depending on the user's health condition. Furthermore, the sleep evaluation unit may provide a long-term sleep improvement plan based on the user's sleep data. This allows the user to review their own sleep and ensure high-quality sleep.

[0064] The health care consultation system may further include a preventive medicine unit that provides preventive medicine advice based on the user's health data. The preventive medicine unit, for example, analyzes the user's health data and predicts future health risks. The preventive medicine unit may also suggest specific preventive measures based on the user's health data. Furthermore, the preventive medicine unit may compare the user's health data with that of other users and suggest general preventive measures. This allows the user to understand their own health risks and take appropriate preventive measures.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: At the reception desk, the senior presses a button and the AI ​​starts a face-to-face health and medical consultation. Seniors do not need to perform any special operations; they can easily start a consultation with just one button. For example, if a senior feels unwell, they simply press a button and the AI ​​will start a face-to-face consultation and listen to the senior's symptoms. Step 2: The consultation department provides health and medical consultations to seniors using AI, acting as their daily conversation partner. For example, seniors talk about daily events and changes in their physical condition, and the AI ​​records this and grasps their health status. This allows seniors to manage their health on a daily basis without feeling lonely. The consultation department can also grasp the health status of seniors using voice recognition and natural language processing. For example, the AI ​​analyzes what the seniors say and evaluates their health status. Step 3: In the reservation department, based on the results of the health and medical consultation, the AI ​​will make appointments and transportation reservations at the nearest medical institution or welfare facility as necessary. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, it will make an appointment at the nearest medical institution. Also, if the senior needs transportation to the medical institution, the AI ​​will make a reservation for a taxi or welfare vehicle. Step 4: The notification unit notifies the user of the results of the appointment and transportation reservation. For example, the date and time of the appointment and details of transportation are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind.

[0067] (Example 2) In a health and medical consultation system according to an embodiment of the present invention, an AI initiates a face-to-face health and medical consultation when a senior presses a button. Based on the results of the health and medical consultation, the AI ​​then schedules an appointment with the nearest medical institution or welfare facility and a transportation reservation, and notifies the user. In this health and medical consultation system, the AI ​​initiates a face-to-face health and medical consultation when a senior presses a button. The senior can easily start the consultation with a single button, without any special operations required. For example, if a senior feels unwell, the AI ​​simply presses a button to initiate a face-to-face consultation and listens to the senior's symptoms. The AI ​​then serves as the senior's daily conversation partner while providing a health and medical consultation. For example, the senior talks about daily events and changes in their health, and the AI ​​records this and keeps track of their health status. This allows the senior to manage their daily health without feeling isolated. Furthermore, based on the results of the health and medical consultation, the AI ​​schedules an appointment with the nearest medical institution or welfare facility as needed. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, the AI ​​schedules the appointment with the nearest medical institution. Furthermore, if the senior needs transportation to the medical institution, the AI ​​reserves a taxi or welfare vehicle. Finally, the AI ​​notifies the user of the results of the appointment and transportation reservation. For example, the date and time of the appointment and details of the transportation method are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind. The health and medical consultation system allows seniors to easily seek health and medical advice and receive the necessary medical services. It also serves as a daily conversation partner for seniors, reducing feelings of loneliness and helping to maintain their physical and mental health. The health and medical consultation system allows seniors to easily seek health and medical advice and receive the necessary medical services. It also serves as a daily conversation partner for seniors, reducing feelings of loneliness and helping to maintain their physical and mental health.

[0068] A health and medical consultation system according to an embodiment includes a reception unit, a consultation unit, a reservation unit, and a notification unit. When a senior person presses a button in the reception unit, AI initiates a face-to-face health and medical consultation. Seniors can easily initiate the consultation with a single button, without any special operations. For example, if a senior feels unwell, AI initiates a face-to-face consultation by simply pressing a button and listens to the senior's symptoms. In the consultation unit, AI serves as a daily conversation partner for the senior. For example, when a senior person talks about daily events and changes in their health, the AI ​​records this and grasps their health status. This allows seniors to manage their daily health without feeling isolated. The consultation unit can also grasp the senior's health status using voice recognition and natural language processing. For example, the AI ​​analyzes the senior's speech and evaluates their health status. Based on the results of the health and medical consultation, the AI ​​schedules appointments and transportation reservations at the nearest medical institution or welfare facility, as needed. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, it schedules an appointment at the nearest medical institution. Furthermore, if a senior needs transportation to a medical institution, the AI ​​will reserve a taxi or welfare vehicle. The notification unit notifies the user of the results of the consultation appointment or transportation reservation. For example, the date and time of the consultation appointment and details of the transportation method are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind. As a result, the health and medical consultation system according to the embodiment allows seniors to easily receive health and medical consultations and receive the necessary medical services. Furthermore, by providing a daily conversation partner for seniors, it can reduce feelings of loneliness and maintain physical and mental health.

[0069] The consultation unit can use AI to provide health and medical consultations to seniors while serving as their daily conversation partner. For example, the consultation unit can understand the senior's health condition by having the AI ​​talk to the senior about daily events and changes in their physical condition. The consultation unit can also have the AI ​​analyze the senior's comments and evaluate their health condition. Furthermore, the consultation unit can have the AI ​​record the senior's health condition and support daily health management. This allows seniors to manage their daily health without feeling lonely. Some or all of the above-mentioned processing in the consultation unit may be performed using, or without, the generation AI. For example, the consultation unit can input the senior's comments into the generation AI, which can then evaluate the senior's health condition.

[0070] The consultation unit can grasp the senior's health condition using voice recognition or natural language processing. The consultation unit can, for example, use voice recognition technology to analyze the senior's utterances and evaluate the health condition. The consultation unit can also use natural language processing technology to analyze the senior's utterances and evaluate the health condition. Furthermore, the consultation unit can combine voice recognition technology and natural language processing technology to more accurately grasp the senior's health condition. This makes it possible to accurately grasp the senior's health condition and provide appropriate health and medical consultations. Some or all of the above-mentioned processing in the consultation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the consultation unit can input the senior's utterances into a generation AI, which can evaluate the senior's health condition.

[0071] The reservation unit can analyze the senior's symptoms and, if it determines that a doctor's examination is necessary, make an appointment at the nearest medical institution. For example, if an AI analyzes the senior's symptoms and determines that a doctor's examination is necessary, the reservation unit can make an appointment at the nearest medical institution. The reservation unit can also analyze the senior's symptoms and select an appropriate medical institution. Furthermore, the reservation unit can analyze the senior's symptoms and determine the priority of appointments according to the urgency of the examination. This allows the senior to be examined at an appropriate medical institution. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input the senior's symptom data into the generation AI, which can select an appropriate medical institution and make an appointment.

[0072] The reservation unit can make reservations for taxis or welfare vehicles. For example, the reservation unit uses AI to analyze the senior's means of transportation and make reservations for taxis or welfare vehicles. The reservation unit can also analyze the senior's means of transportation and select the optimal means of transportation. Furthermore, the reservation unit can analyze the senior's means of transportation and determine reservation priorities according to the urgency of the travel. This allows seniors to secure transportation to medical institutions and welfare facilities. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input the senior's means of transportation data into the generation AI, which can select the optimal means of transportation and make a reservation.

[0073] The notification unit can notify the user of the date and time of the appointment and details of the means of transportation. For example, the notification unit uses AI to notify the user of the date and time of the appointment and details of the means of transportation. The notification unit can also notify the user of information when an appointment is changed or canceled. Furthermore, the notification unit can also notify the user of appointment reminders. This allows seniors to understand details of the appointment and the means of transportation. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the notification unit can input detailed data of the appointment and the means of transportation into the generation AI, and the generation AI can notify the user.

[0074] The reception unit can estimate the user's emotions and adjust the timing of reception based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can immediately start reception to provide a sense of security. Furthermore, if the user is relaxed, the reception unit can wait a short time before starting reception, allowing the consultation to begin in a natural flow. Furthermore, if the user is in a hurry, the reception unit can immediately start reception to quickly proceed with the consultation. This allows reception to be performed at an appropriate time 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, 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the timing of reception.

[0075] The reception unit can select the optimal reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit prepares related questions in advance based on the content of consultations the user has frequently made in the past. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past consultation history. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past consultation history data into the generation AI, which can then select the optimal reception method.

[0076] The reception unit can filter consultations based on the user's current health condition and lifestyle at the time of reception. For example, if the user is currently in poor health, the reception unit can prioritize urgent consultations. The reception unit can also suggest appropriate consultation content based on the user's lifestyle. Furthermore, the reception unit can prioritize consultations with specific specialists based on the user's health condition. This makes it possible to provide appropriate consultations based on the user's current health condition and lifestyle. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's health condition data into the generation AI, which can then filter appropriate consultation content.

[0077] The reception unit can select the optimal reception means depending on the user's input method when receiving the data. For example, if the user desires voice input, the reception unit can use voice recognition to receive the data. Furthermore, if the user desires text input, the reception unit can also use chat to receive the data. Furthermore, if the user transmits an image, the reception unit can also use image analysis to receive the data. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input data into the generation AI, which can then select the optimal reception means.

[0078] The reception unit can estimate the user's emotions and determine the priority of consultations to be accepted based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize urgent consultations. Furthermore, if the user is relaxed, the reception unit can also prioritize regular consultations. Furthermore, if the user is in a hurry, the reception unit can also prioritize consultations that require a quick response. This allows consultations to be accepted with appropriate priorities 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 may 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and determines the priority of the consultations.

[0079] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information when receiving a consultation. For example, the reception unit can prioritize receiving consultations related to medical institutions close to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize receiving consultations related to that area. Furthermore, the reception unit can suggest optimal consultation content based on the user's geographical location information. This makes it possible to provide optimal consultations based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then suggest optimal consultation content.

[0080] The reception unit can analyze the user's social media activities and receive related consultations at the time of reception. The reception unit can receive related consultations based on, for example, health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related consultations. Furthermore, the reception unit can also receive related consultations by referring to the activities of the user's friends on social media. This makes it possible to provide optimal consultations based on the user's social media activities. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then suggest related consultations.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can also improve the reception method by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI, which can then customize the optimal reception method.

[0082] The consultation unit can estimate the user's emotions and adjust the way the consultation is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the consultation unit can use gentle language. Furthermore, if the user is relaxed, the consultation unit can provide detailed explanations. Furthermore, if the user is in a hurry, the consultation unit can provide concise, to-the-point advice. This allows the consultation to be provided in an appropriate way according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the consultation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the consultation unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the way the consultation is expressed.

[0083] The consultation unit can adjust the level of detail of the consultation based on the importance of the health condition during the consultation. For example, if the user's health condition is serious, the consultation unit can provide a detailed consultation. Also, if the user's health condition is mild, the consultation unit can provide a brief consultation. Furthermore, the consultation unit can provide a consultation with an appropriate level of detail depending on the user's health condition. This allows the consultation to be provided with an appropriate level of detail depending on the user's health condition. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's health condition data into the generation AI, and the generation AI can adjust the level of detail of the consultation.

[0084] During a consultation, the consultation unit can apply different consultation algorithms depending on the category of the health condition. For example, if the user's health condition is related to the heart, the consultation unit can apply a consultation algorithm specialized in cardiac matters. Furthermore, if the user's health condition is related to the digestive system, the consultation unit can also apply a consultation algorithm specialized in digestive matters. Furthermore, if the user's health condition is related to the respiratory system, the consultation unit can also apply a consultation algorithm specialized in respiratory matters. This makes it possible to apply an appropriate consultation algorithm according to the user's health condition. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's health condition data into a generation AI, which can then apply an appropriate consultation algorithm.

[0085] During a consultation, the consultation unit can improve the accuracy of the consultation by referring to the user's past consultation results. The consultation unit, for example, suggests optimal consultation content based on the user's past consultation results. The consultation unit can also prioritize consultations regarding specific health conditions based on the user's past consultation results. Furthermore, the consultation unit can analyze the user's past consultation results and improve the accuracy of the consultation. This makes it possible to improve the accuracy of the consultation based on the user's past consultation results. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's past consultation result data into the generation AI, which can improve the accuracy of the consultation.

[0086] The consultation unit can estimate the user's emotions and adjust the length of the consultation based on the estimated user emotions. For example, if the user feels anxious, the consultation unit can provide a longer consultation to give the user a sense of security. Furthermore, if the user feels relaxed, the consultation unit can provide a normal length consultation. Furthermore, if the user is in a hurry, the consultation unit can provide a shorter consultation to respond quickly. This allows the consultation to be of an appropriate length 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 may 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 consultation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the consultation unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the length of the consultation.

[0087] The consultation unit can determine the priority of the consultation based on the time when the health condition occurred at the time of consultation. For example, if the health condition occurred recently, the consultation unit can prioritize the consultation. In addition, if the health condition has continued for a long period of time, the consultation unit can also provide the consultation with normal priority. Furthermore, the consultation unit can also provide the consultation with appropriate priority depending on the time when the health condition occurred. This allows the consultation to be provided with appropriate priority depending on the time when the user's health condition occurred. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the consultation unit can input data on the time when the user's health condition occurred into the generation AI, and the generation AI can determine the priority of the consultation.

[0088] The consultation unit can adjust the order of consultations based on the relevance of the health conditions during consultations. For example, if the health conditions are highly relevant, the consultation unit can prioritize consultations. Also, if the health conditions are less relevant, the consultation unit can conduct consultations in the normal order. Furthermore, the consultation unit can conduct consultations in an appropriate order depending on the relevance of the health conditions. This allows consultations to be conducted in an appropriate order depending on the relevance of the user's health conditions. Some or all of the above-described processing in the consultation unit may be performed using, or without, a generation AI, for example. For example, the consultation unit can input relevance data of the user's health conditions into the generation AI, which can then adjust the order of consultations.

[0089] During a consultation, the consultation unit can adjust the use of technical terminology in the consultation depending on the user's level of expertise. For example, if the user has technical expertise, the consultation unit can use technical terminology to provide the consultation. Also, if the user does not have technical expertise, the consultation unit can use simple language to provide the consultation. Furthermore, the consultation unit can use appropriate language depending on the user's level of expertise. This allows the consultation to be provided using appropriate language depending on the user's level of expertise. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the consultation unit can input the user's level of expertise data into the generation AI, which can adjust the use of technical terminology in the consultation.

[0090] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user emotions. For example, if the user is feeling anxious, the reservation unit can make a reservation using simple steps. Furthermore, if the user is relaxed, the reservation unit can also make a reservation using detailed steps. Furthermore, if the user is in a hurry, the reservation unit can also make a reservation quickly. This allows a reservation to be made in an appropriate manner according to the user's emotions. The 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-described processing in the reservation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reservation unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the reservation method.

[0091] At the time of reservation, the reservation unit can analyze the user's past health condition and select the optimal reservation method. The reservation unit, for example, suggests the optimal medical institution based on the user's past health condition. The reservation unit can also preferentially suggest specific medical institutions based on the user's past health condition. Furthermore, the reservation unit can analyze the user's past health condition and select the optimal reservation method. This makes it possible to provide the optimal reservation method based on the user's past health condition. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's past health condition data into a generation AI, which can select the optimal reservation method.

[0092] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. The reservation unit, for example, suggests the optimal reservation method according to the user's living situation. The reservation unit can also preferentially suggest a specific reservation method taking the user's living situation into consideration. Furthermore, the reservation unit can customize the reservation method based on the user's living situation. This makes it possible to provide the optimal reservation method according to the user's current living situation. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's living situation data into the generation AI, which can then customize the reservation method.

[0093] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on user feedback. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. Furthermore, the reservation unit can improve the reservation method by reflecting user feedback. This makes it possible to provide the optimal reservation method based on user feedback. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input user feedback data into the generation AI, which can then improve the reservation method.

[0094] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user emotions. For example, if the user is feeling anxious, the reservation unit can prioritize reservations with high urgency. Furthermore, if the user is relaxed, the reservation unit can also prioritize reservations that require a quick response if the user is in a hurry. This allows reservations to be made with appropriate priority 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 reservation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reservation unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of reservations.

[0095] When making a reservation, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. For example, the reservation unit can prioritize reservations with medical institutions close to the user's current location. Also, if the user is in a specific area, the reservation unit can prioritize reservations with medical institutions related to that area. Furthermore, the reservation unit can suggest the optimal reservation method based on the user's geographical location information. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's geographical location information data into the generation AI, which can select the optimal reservation method.

[0096] At the time of making a reservation, the reservation unit can analyze the user's social media activity and suggest a reservation method. The reservation unit can suggest the optimal reservation method based on, for example, health information shared by the user on social media. The reservation unit can also analyze the user's social media posts and suggest a reservation at a relevant medical institution. Furthermore, the reservation unit can also suggest the optimal reservation method based on the activity of the user's friends on social media. This makes it possible to provide the optimal reservation method based on the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reservation unit can input the user's social media activity data into a generation AI, which can then suggest the optimal reservation method.

[0097] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. The reservation unit can also preferentially suggest a specific reservation method based on the user's past feedback. Furthermore, the reservation unit can improve the reservation method by reflecting the user's feedback. This makes it possible to provide the optimal reservation method based on the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, the generation AI, for example. For example, the reservation unit can input the user's feedback data into the generation AI, which can then customize the reservation method.

[0098] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can provide a notification using gentle language. Furthermore, if the user is relaxed, the notification unit can provide a notification including detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise and to-the-point notification. This allows notifications to be provided in an appropriate manner 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, 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 notification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the notification unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the notification method.

[0099] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification unit, for example, preferentially suggests notification methods (audio, text, etc.) that the user has previously preferred. The notification unit can also predict and suggest a notification method to be used during a specific time period based on the user's past notification history. Furthermore, the notification unit can select the optimal notification method based on the user's past notification history. This makes it possible to provide the optimal notification method based on the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's past notification history data into the generation AI, which can then select the optimal notification method.

[0100] The notification unit can customize the notification means based on the user's current living situation at the time of notification. The notification unit, for example, suggests the optimal notification means according to the user's living situation. The notification unit can also preferentially suggest a specific notification means taking the user's living situation into consideration. Furthermore, the notification unit can customize the notification means based on the user's living situation. This makes it possible to provide the optimal notification means according to the user's current living situation. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the user's living situation data into the generation AI, which can then customize the notification means.

[0101] The notification unit can improve the notification method by reflecting user feedback at the time of notification. The notification unit, for example, suggests an optimal notification method based on user feedback. The notification unit can also preferentially suggest a specific notification method based on the user's past feedback. Furthermore, the notification unit can improve the notification method by reflecting user feedback. This makes it possible to provide an optimal notification method based on user feedback. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI, for example. For example, the notification unit can input user feedback data into the generation AI, which can then improve the notification method.

[0102] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can prioritize notifications with high urgency. Furthermore, if the user is relaxed, the notification unit can also prioritize notifications that require a quick response if the user is in a hurry. This allows notifications to be sent with an appropriate priority 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, 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 notification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the notification unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of notifications.

[0103] The notification unit can select the optimal notification method by taking into account the user's geographical location information when making a notification. For example, the notification unit can prioritize notifications to medical institutions close to the user's current location. Furthermore, if the user is in a specific area, the notification unit can also prioritize notifications related to that area. Furthermore, the notification unit can suggest the optimal notification method based on the user's geographical location information. This makes it possible to provide the optimal notification method based on the user's geographical location information. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's geographical location information data into the generation AI, which can then select the optimal notification method.

[0104] At the time of notification, the notification unit can analyze the user's social media activity and suggest a notification method. The notification unit can suggest the optimal notification method based on, for example, health information shared by the user on social media. The notification unit can also analyze the content of the user's social media posts and suggest related notifications. Furthermore, the notification unit can also suggest the optimal notification method based on the activity of the user's friends on social media. This makes it possible to provide the optimal notification method based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can input the user's social media activity data into a generation AI, which then suggests the optimal notification method.

[0105] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. The notification unit can, for example, suggest an optimal notification method based on the user's past feedback. The notification unit can also preferentially suggest a specific notification method based on the user's past feedback. Furthermore, the notification unit can also improve the notification method by reflecting the user's feedback. This makes it possible to provide an optimal notification method based on the user's past feedback. Some or all of the above-described processing in the notification unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the notification unit can input user feedback data into the generation AI, which can then customize the notification method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, consultation unit, reservation unit, and notification 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 when the senior citizen presses a button, the AI ​​starts a face-to-face health and medical consultation. For example, the consultation unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​provides health and medical consultation while serving as the senior citizen's daily conversation partner. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and makes an appointment for a medical consultation at the nearest medical institution or welfare facility or a reservation for transportation based on the results of the health and medical consultation. For example, the notification unit is realized by the output device 40 of the smart device 14, and notifies the user of the results of the appointment for a medical consultation or a reservation for transportation. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, consultation unit, reservation unit, and notification 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 of the smart glasses 214, and when the senior person presses a button, the AI ​​starts a face-to-face health and medical consultation. For example, the consultation unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​provides health and medical consultation while serving as the senior person's daily conversation partner. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and makes an appointment for a medical consultation at the nearest medical institution or welfare facility or a reservation for transportation based on the results of the health and medical consultation. For example, the notification unit is realized by the speaker 240 of the smart glasses 214, and notifies the user of the results of the appointment for a medical consultation or a reservation for transportation. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, consultation unit, reservation unit, and notification unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and when the senior person presses a button, the AI ​​starts a face-to-face health and medical consultation. For example, the consultation unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​provides health and medical consultation while serving as the senior person's daily conversation partner. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and makes an appointment for a medical consultation at the nearest medical institution or welfare facility or a reservation for transportation based on the results of the health and medical consultation. For example, the notification unit is realized by the display 343 of the headset terminal 314, and notifies the user of the results of the appointment for a medical consultation or the reservation for transportation. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, consultation unit, reservation unit, and notification 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 of the robot 414, and when the senior citizen presses a button, the AI ​​starts a face-to-face health and medical consultation. For example, the consultation unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​provides health and medical consultation while serving as the senior citizen's daily conversation partner. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and makes an appointment for a medical consultation at the nearest medical institution or welfare facility or a reservation for transportation based on the results of the health and medical consultation. For example, the notification unit is realized by the speaker 240 of the robot 414, and notifies the user of the results of the appointment for a medical consultation or a reservation for transportation.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The health and medical consultation system may further include an analysis unit that collects the user's health data and analyzes long-term health trends. The analysis unit, for example, predicts future health risks based on the user's past health data. The analysis unit may also compare the user's health data with that of other users to understand general health trends. Furthermore, the analysis unit may provide individualized health advice based on the user's health data. This allows the user to understand their own health condition over the long term and take appropriate health management measures.

[0108] The health and medical consultation system may further include an emotion advice unit that estimates the user's emotion and provides health advice based on the estimated emotion. For example, if the user is feeling stressed, the emotion advice unit may suggest relaxation methods. If the user is feeling depressed, the emotion advice unit may also suggest words of encouragement or positive activities. If the user is feeling happy, the emotion advice unit may also suggest activities to maintain that emotion. This allows the user to receive appropriate health advice according to their own emotions.

[0109] The health and medical consultation system may further include a nutritional evaluation unit that collects the user's dietary data and evaluates the nutritional balance. The nutritional evaluation unit may, for example, analyze the nutrients in the meals the user has eaten and suggest a balanced diet. The nutritional evaluation unit may also advise the user to increase the intake of specific nutrients depending on the user's health condition. Furthermore, the nutritional evaluation unit may provide a long-term dietary improvement plan based on the user's dietary data. This allows the user to review their own diet and maintain a healthy eating lifestyle.

[0110] The health and medical consultation system may further include an exercise evaluation unit that collects the user's exercise data and evaluates their exercise habits. The exercise evaluation unit may, for example, record the type and frequency of exercise performed by the user and propose an appropriate exercise plan. The exercise evaluation unit may also recommend specific exercises based on the user's health condition. Furthermore, the exercise evaluation unit may set long-term exercise goals based on the user's exercise data and support the achievement of those goals. This allows the user to review their exercise habits and live a healthier life.

[0111] The health care consultation system may further include a sleep evaluation unit that collects the user's sleep data and evaluates the quality of the user's sleep. The sleep evaluation unit may, for example, record the user's sleep time and sleep quality and suggest areas for improvement. The sleep evaluation unit may also recommend specific sleep habits depending on the user's health condition. Furthermore, the sleep evaluation unit may provide a long-term sleep improvement plan based on the user's sleep data. This allows the user to review their own sleep and ensure high-quality sleep.

[0112] The health care consultation system may further include an exercise and emotion adjustment unit that estimates the user's emotions and adjusts the exercise plan based on the estimated emotions. For example, if the user is feeling stressed, the exercise and emotion adjustment unit may suggest an exercise that has a relaxing effect. Also, if the user is feeling depressed, the exercise and emotion adjustment unit may suggest an exercise that will lift the user's spirits. Furthermore, if the user is feeling happy, the exercise and emotion adjustment unit may suggest an exercise that will help maintain that emotion. This allows the user to receive an appropriate exercise plan that suits their emotions.

[0113] The health care consultation system may further include a meal emotion adjustment unit that estimates the user's emotions and adjusts the meal plan based on the estimated emotions. For example, if the user is feeling stressed, the meal emotion adjustment unit may suggest a meal that has a relaxing effect. Also, if the user is feeling depressed, the meal emotion adjustment unit may suggest a meal that will lift the user's spirits. Furthermore, if the user is feeling happy, the meal emotion adjustment unit may suggest a meal that will maintain that emotion. This allows the user to receive an appropriate meal plan that suits their emotions.

[0114] The health care consultation system may further include a sleep emotion adjustment unit that estimates the user's emotions and adjusts the sleep environment based on the estimated emotions. For example, if the user is feeling stressed, the sleep emotion adjustment unit may suggest an environment that has a relaxing effect. If the user is feeling depressed, the sleep emotion adjustment unit may also suggest an environment that will lift the user's spirits. Furthermore, if the user is feeling happy, the sleep emotion adjustment unit may also suggest an environment that will help maintain that emotion. This allows the user to create an appropriate sleep environment according to their emotions.

[0115] The health and medical consultation system may further include an advice emotion adjustment unit that estimates the user's emotion and adjusts the way health advice is expressed based on the estimated emotion. For example, if the user is feeling anxious, the advice emotion adjustment unit may provide advice in gentle language. If the user is relaxed, the advice emotion adjustment unit may also provide advice that includes detailed explanations. If the user is in a hurry, the advice emotion adjustment unit may also provide concise advice that focuses on the main points. This makes it possible to provide health advice in an appropriate way according to the user's emotion.

[0116] The health care consultation system may further include a preventive medicine unit that provides preventive medicine advice based on the user's health data. The preventive medicine unit, for example, analyzes the user's health data and predicts future health risks. The preventive medicine unit may also suggest specific preventive measures based on the user's health data. Furthermore, the preventive medicine unit may compare the user's health data with that of other users and suggest general preventive measures. This allows the user to understand their own health risks and take appropriate preventive measures.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: At the reception desk, the senior presses a button and the AI ​​starts a face-to-face health and medical consultation. Seniors do not need to perform any special operations; they can easily start a consultation with just one button. For example, if a senior feels unwell, they simply press a button and the AI ​​will start a face-to-face consultation and listen to the senior's symptoms. Step 2: The consultation department provides health and medical consultations to seniors using AI, acting as their daily conversation partner. For example, seniors talk about daily events and changes in their physical condition, and the AI ​​records this and grasps their health status. This allows seniors to manage their health on a daily basis without feeling lonely. The consultation department can also grasp the health status of seniors using voice recognition and natural language processing. For example, the AI ​​analyzes what the seniors say and evaluates their health status. Step 3: In the reservation department, based on the results of the health and medical consultation, the AI ​​will make appointments and transportation reservations at the nearest medical institution or welfare facility as necessary. For example, if the AI ​​analyzes the senior's symptoms and determines that a doctor's examination is necessary, it will make an appointment at the nearest medical institution. Also, if the senior needs transportation to the medical institution, the AI ​​will make a reservation for a taxi or welfare vehicle. Step 4: The notification unit notifies the user of the results of the appointment and transportation reservation. For example, the date and time of the appointment and details of transportation are displayed on the senior's tablet. This allows seniors to use medical institutions and welfare facilities with peace of mind.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can 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 AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can 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 AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can 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 AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can 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 AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. The reception desk, where seniors can press a button to initiate a face-to-face health and medical consultation using AI, a consultation unit that performs health and medical consultations initiated by the reception unit; a reservation unit that makes an appointment for a medical examination or a transportation to the nearest medical institution or welfare facility based on the results of the health and medical consultation obtained by the consultation unit; a notification unit that notifies a user of the reservation result executed by the reservation unit; A system characterized by:

2. The consultation department: Using AI to provide daily conversation and health care consultations for seniors 2. The system of claim 1.

3. The consultation department: Understanding senior health using voice recognition or natural language processing 2. The system of claim 1.

4. The reservation unit Analyzes the senior's symptoms and, if it is determined that a doctor's consultation is necessary, makes an appointment at the nearest medical institution.

2. The system of claim 1.

5. The reservation unit Make a reservation for a taxi or welfare vehicle 2. The system of claim 1.

6. The notification unit Notify users of appointment dates and travel details 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of reception based on the estimated user emotions 2. The system of claim 1.

8. The reception unit When accepting a call, the system selects the most appropriate method of acceptance by referring to the user's past consultation history.

2. The system of claim 1.

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