System

Abstract

An object of a system according to an embodiment is to provide quick and appropriate medical service information when the physical condition becomes poor during overseas travel.SOLUTION: A system includes a position information acquisition part, a symptom input part, and a medical information provision part. The position information acquisition unit acquires position information. The symptom input unit inputs a symptom of the user based on the position information acquired by the position information acquisition unit. The medical information providing part analyzes the symptom information inputted by the symptom input part and provides optimum medical service information.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] Conventional technologies have had the problem that it is difficult to obtain prompt and appropriate medical service information when one becomes unwell while traveling abroad.

[0005] The system according to the embodiment aims to provide prompt and appropriate medical service information when a person becomes unwell while traveling abroad. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, a symptom input unit, and a medical information provision unit. The location information acquisition unit acquires location information. The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. The medical information provision unit analyzes the symptom information input by the symptom input unit and provides optimal medical service information. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate medical service information when a person becomes unwell while traveling abroad. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical information provision system according to an embodiment of the present invention is a system that supports users in quickly and appropriately obtaining local medical service information when they become ill while traveling abroad. This system allows users to obtain the necessary information in three simple steps. This allows the medical information provision system to quickly and appropriately obtain local medical service information.

[0029] A medical information provision system according to an embodiment includes a location information acquisition unit, a symptom input unit, and a medical information provision unit. The location information acquisition unit acquires user location information. For example, the location information acquisition unit automatically acquires a user's current location using a smartphone's GPS function. Alternatively, the user can manually input location information into the location information acquisition unit. For example, when a user presses a "Get Current Location" button, the device automatically identifies the user's current location and acquires location information. The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe abdominal pain." The symptom input unit allows the user to input symptoms using text input or multiple-choice options. The medical information provision unit analyzes the symptom information input by the symptom input unit and provides optimal medical service information. For example, the medical information provision unit uses a generation AI to create a list of nearby hospitals, clinics, and pharmacies based on the acquired location information and the analyzed symptom information, and presents the list to the user. The medical information provision unit also provides detailed information such as each medical institution's opening hours, contact information, and language support. As a result, the medical information providing system according to the embodiment allows users to quickly and appropriately obtain local medical service information. For example, if a user suddenly develops a fever while traveling abroad, the user can obtain information about nearby hospitals and clinics immediately by acquiring their current location and entering the symptoms of the fever. Furthermore, since the system can find an appropriate medical institution regardless of language barriers, users can receive medical services with peace of mind.

[0030] The location information acquisition unit can refer to the user's past movement history and prioritize displaying frequently visited places. For example, when the user acquires location information, the location information acquisition unit refers to the user's past movement history and prioritize displaying frequently visited places. For example, it automatically lists places that the user has visited many times in the past and presents them as options. The location information acquisition unit also analyzes the user's movement history data and builds a system that prioritizes displaying frequently visited places. For example, it identifies areas that the user often visits based on past location information data and displays them prioritized. Furthermore, when the user inputs location information, the location information acquisition unit automatically suggests frequently visited places based on the user's past movement history. For example, it prioritizes displaying hospitals and clinics that the user has visited in the past. This prioritizes displaying places that the user frequently visits, making it easier to input location information.

[0031] The location information acquisition unit can acquire surrounding weather information and traffic conditions at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, the location information acquisition unit automatically acquires surrounding weather information when acquiring location information, and evaluates the ease of access to medical institutions. For example, medical institutions that are difficult to access in bad weather are avoided. The location information acquisition unit can also acquire traffic conditions in real time at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, medical institutions in areas where traffic congestion occurs are avoided. The location information acquisition unit can also construct a system that comprehensively evaluates weather information and traffic conditions when acquiring location information, and suggests the most suitable medical institution. For example, medical institutions that are easily accessible depending on the weather and traffic conditions are preferentially displayed. This makes it possible to evaluate the ease of access to medical institutions taking weather information and traffic conditions into consideration.

[0032] The location information acquisition unit can simultaneously acquire the user's health history and allergy information when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the location information acquisition unit can automatically acquire the user's health history when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the location information acquisition unit can suggest the most suitable medical institution based on the user's past medical history and allergy information. The location information acquisition unit can also simultaneously acquire the user's allergy information when acquiring location information, and prioritize displaying medical institutions that can accommodate allergies. For example, it can list hospitals that accommodate specific allergies. The location information acquisition unit can also comprehensively evaluate the user's health history and allergy information when acquiring location information, and build a system that suggests the most suitable medical institution. For example, it can select a medical institution based on the user's past medical history and allergy information. This makes it possible to select the most suitable medical institution taking into account the user's health history and allergy information.

[0033] When acquiring location information, the location information acquisition unit can automatically filter the language support status of local medical institutions based on the user's language setting. For example, when acquiring location information, the location information acquisition unit automatically filters the language support status of local medical institutions based on the user's language setting. For example, if the user speaks English, medical institutions that can provide English support are preferentially displayed. The location information acquisition unit also acquires the user's language setting at the same time as acquiring location information, and builds a system that lists medical institutions that can provide language support. For example, hospitals that can provide the user's native language are displayed. The location information acquisition unit also evaluates the language support status of local medical institutions based on the user's language setting at the same time as acquiring location information, and suggests the most suitable medical institution. For example, if the user speaks Japanese, medical institutions that can provide Japanese support are preferentially displayed. This makes it possible to preferentially display medical institutions that can provide language support based on the user's language setting.

[0034] The symptom input unit can perform more accurate analysis by referring to the user's past health data and medical history when inputting symptoms. The symptom input unit, for example, automatically refers to the user's past health data when inputting symptoms to perform more accurate analysis. For example, it analyzes symptoms based on past medical history and test results. The symptom input unit also builds a system that simultaneously acquires the user's medical history when inputting symptoms and reflects it in the analysis. For example, it analyzes symptoms based on past medical history and treatment history. The symptom input unit also comprehensively evaluates the user's past health data simultaneously with symptom input to perform more accurate analysis. For example, it analyzes symptoms based on past medical history and health data. As a result, by referring to past health data and medical history, more accurate symptom analysis is possible.

[0035] The symptom input unit can acquire the user's vital signs from the smart device at the same time as the symptom input and reflect them in the analysis. For example, the symptom input unit automatically acquires the vital signs from the smart device when the symptom is input and reflects them in the analysis. For example, the heart rate and body temperature are acquired in real time and used for symptom analysis. The symptom input unit also builds a system that acquires the user's vital signs at the same time as the symptom is input and reflects them in the analysis. For example, data is acquired from a smart watch or a thermometer. The symptom input unit also acquires the vital signs from the smart device in an integrated manner at the same time as the symptom is input and reflects them in the analysis. For example, the heart rate and body temperature are acquired in real time and used for symptom analysis. In this way, by acquiring vital signs in real time and reflecting them in the symptom analysis, more accurate analysis is possible.

[0036] The symptom input unit can simultaneously input information about the treatments and prescription medications the user has received in the past when inputting symptoms, and use this information for analysis. For example, the symptom input unit automatically acquires information about the treatments and prescription medications the user has received in the past when inputting symptoms, and uses this information for analysis. For example, the symptom input unit analyzes symptoms based on past prescription medication data. The symptom input unit also constructs a system that simultaneously acquires the user's treatment history and prescription medication information when inputting symptoms, and reflects this information in the analysis. For example, the symptom input unit analyzes symptoms based on past treatment history and prescription medication data. The symptom input unit also comprehensively acquires the user's past treatment and prescription medication information when inputting symptoms, and uses this information for analysis. For example, the symptom input unit analyzes symptoms based on past treatment history and prescription medication data. By utilizing information about past treatments and prescription medications in the analysis, more accurate symptom analysis is possible.

[0037] The symptom input unit uses voice input and image recognition technology to input symptoms, thereby reducing the burden on the user. The symptom input unit, for example, constructs a system that inputs symptoms by voice and reduces the burden on the user. For example, the user describes the symptoms by voice, which is converted into text and analyzed. The symptom input unit also develops a system that inputs symptoms using image recognition technology. For example, the user takes a photo of the affected area, which is analyzed to identify the symptoms. The symptom input unit also constructs a system that inputs symptoms by integrating voice input and image recognition technology. For example, by describing the symptoms by voice and confirming the affected area with an image, a more accurate analysis is performed. In this way, the burden on the user can be reduced by using voice input and image recognition technology.

[0038] The medical information providing unit, when providing medical service information, can refer to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, when providing medical service information, the medical information providing unit automatically refers to the user's past medical institution usage history and preferentially displays highly reliable medical institutions. For example, it preferentially lists medical institutions that the user has given high ratings to in the past. The medical information providing unit also analyzes the user's medical institution usage history and builds a system that preferentially displays highly reliable medical institutions. For example, it identifies highly reliable medical institutions based on the past usage history and displays them preferentially. The medical information providing unit, when providing medical service information, automatically suggests highly reliable medical institutions based on the user's past medical institution usage history. For example, it preferentially displays medical institutions that the user has been satisfied with in the past. In this way, by referring to the past medical institution usage history, highly reliable medical institutions can be preferentially displayed.

[0039] The medical information providing unit can collect evaluations and word-of-mouth information about medical institutions in real time and provide it to users. The medical information providing unit, for example, builds a system that collects evaluations and word-of-mouth information about medical institutions in real time and provides it to users. For example, it collects data from online review sites and rating sites and displays it to users. The medical information providing unit also automatically collects evaluations and word-of-mouth information about medical institutions and provides it to users in real time. For example, it updates the evaluations of medical institutions based on the latest word-of-mouth information and presents it to users. The medical information providing unit also develops a system that collects evaluations and word-of-mouth information about medical institutions in real time and provides it to users. For example, it collects data from social media and review sites and displays it to users. In this way, evaluations and word-of-mouth information about medical institutions can be collected in real time and provided to users, thereby providing highly reliable information.

[0040] The medical information providing unit can refer to the user's insurance information when providing medical service information and prioritize displaying medical institutions that accept insurance. For example, the medical information providing unit automatically acquires the user's insurance information when providing medical service information and prioritizes displaying medical institutions that accept insurance. For example, it lists medical institutions that accept the user's insurance plan. The medical information providing unit also references the user's insurance information and builds a system that prioritizes displaying medical institutions that accept insurance. For example, it links with an insurance company's database to identify medical institutions that accept insurance. The medical information providing unit also automatically suggests medical institutions that accept insurance based on the user's insurance information when providing medical service information. For example, it prioritizes displaying medical institutions that accept the user's insurance plan. In this way, by referencing the user's insurance information, it is possible to prioritize displaying medical institutions that accept insurance.

[0041] The medical information providing unit can acquire the congestion status and waiting times of medical institutions in real time and provide them to the user. The medical information providing unit, for example, builds a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to the user. For example, it works in cooperation with the reservation system or reception system of the medical institution to acquire the congestion status. The medical information providing unit also automatically acquires the congestion status and waiting times of medical institutions and provides them to the user in real time. For example, it acquires data from the reception system of the medical institution and displays it to the user. The medical information providing unit also develops a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to the user. For example, it works in cooperation with the reservation system or reception system of the medical institution to acquire the congestion status. In this way, the congestion status and waiting times of medical institutions can be acquired in real time and provided to the user, allowing the user to select an appropriate medical institution.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The location information acquisition unit acquires the user's location information. For example, the location information acquisition unit automatically acquires the user's current location using the smartphone's GPS function. Alternatively, the location information acquisition unit can manually input the user's location information. For example, when the user presses a "Get current location" button, the device automatically identifies the user's current location and acquires the location information. The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe abdominal pain." The symptom input unit can input symptoms using text input or multiple-choice format. The medical information provision unit analyzes the symptom information input by the symptom input unit and provides optimal medical service information. For example, the medical information provision unit uses a generation AI to list information about nearby hospitals, clinics, and pharmacies based on the acquired location information and the analyzed symptom information, and presents this to the user. The medical information provision unit also provides detailed information such as each medical institution's opening hours, contact information, and language support. This allows the medical information provision system according to the embodiment to quickly and appropriately obtain local medical service information. For example, if a user suddenly develops a fever while traveling abroad, they can obtain information about nearby hospitals and clinics by obtaining their current location and entering their fever symptoms. Furthermore, since the system can find an appropriate medical institution across language barriers, users can receive medical services with peace of mind.

[0044] The location information acquisition unit can refer to the user's past movement history and prioritize displaying frequently visited places. For example, when the user acquires location information, the past movement history is referenced and frequently visited places are prioritized. For example, places that the user has visited many times in the past are automatically listed and presented as options. The location information acquisition unit also analyzes the user's movement history data and builds a system that prioritizes displaying frequently visited places. For example, areas that the user often visits are identified based on past location information data and displayed with priority. Furthermore, when the user inputs location information, the location information acquisition unit automatically suggests frequently visited places based on the past movement history. For example, hospitals and clinics that the user has visited in the past are displayed with priority. This prioritizes displaying places that the user frequently visits, making it easier to input location information.

[0045] The location information acquisition unit can acquire surrounding weather information and traffic conditions at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, when acquiring location information, it automatically acquires surrounding weather information and evaluates the ease of access to medical institutions. For example, in bad weather, medical institutions that are difficult to access are avoided. The location information acquisition unit can also acquire traffic conditions in real time at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, medical institutions in areas where traffic congestion occurs are avoided. The location information acquisition unit can also construct a system that comprehensively evaluates weather information and traffic conditions when acquiring location information, and suggests the most suitable medical institution. For example, it can prioritize and display medical institutions that are easily accessible depending on the weather and traffic conditions. This makes it possible to evaluate the ease of access to medical institutions taking weather information and traffic conditions into consideration.

[0046] The location information acquisition unit can simultaneously acquire the user's health history and allergy information when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the user's health history can be automatically acquired when acquiring location information, and used as a reference for selecting a medical institution. For example, the most suitable medical institution can be suggested based on past medical history and allergy information. The location information acquisition unit can also simultaneously acquire the user's allergy information when acquiring location information, and prioritize displaying medical institutions that can accommodate allergies. For example, it can list hospitals that accommodate specific allergies. The location information acquisition unit can also comprehensively evaluate the user's health history and allergy information when acquiring location information, and build a system that suggests the most suitable medical institution. For example, it can select a medical institution based on past medical history and allergy information. This makes it possible to select the most suitable medical institution taking into account the user's health history and allergy information.

[0047] When acquiring location information, the location information acquisition unit can automatically filter the language support status of local medical institutions based on the user's language setting. For example, when acquiring location information, the location information acquisition unit automatically filters the language support status of local medical institutions based on the user's language setting. For example, if the user speaks English, medical institutions that can provide English support are preferentially displayed. The location information acquisition unit also acquires the user's language setting at the same time as acquiring location information, and builds a system that lists medical institutions that can provide language support. For example, hospitals that can provide the user's native language are displayed. The location information acquisition unit also evaluates the language support status of local medical institutions based on the user's language setting at the same time as acquiring location information, and suggests the most suitable medical institution. For example, if the user speaks Japanese, medical institutions that can provide Japanese support are preferentially displayed. This makes it possible to preferentially display medical institutions that can provide language support based on the user's language setting.

[0048] The symptom input unit can perform more accurate analysis by referring to the user's past health data and medical history when inputting symptoms. For example, the user's past health data can be automatically referenced when inputting symptoms to perform more accurate analysis. For example, symptoms can be analyzed based on past medical history and test results. The symptom input unit can also build a system that simultaneously acquires the user's medical history when inputting symptoms and reflects it in the analysis. For example, symptoms can be analyzed based on past medical history and treatment history. The symptom input unit can also comprehensively evaluate the user's past health data when inputting symptoms to perform more accurate analysis. For example, symptoms can be analyzed based on past medical history and health data. By referring to past health data and medical history, more accurate symptom analysis is possible.

[0049] The symptom input unit can acquire the user's vital signs from the smart device at the same time as the symptoms are input and reflect them in the analysis. For example, when symptoms are input, the vital signs are automatically acquired from the smart device and reflected in the analysis. For example, heart rate and body temperature are acquired in real time and used for symptom analysis. In addition, the symptom input unit can build a system that acquires the user's vital signs at the same time as the symptoms are input and reflects them in the analysis. For example, data is acquired from a smart watch or a thermometer. In addition, the symptom input unit can comprehensively acquire vital signs from the smart device at the same time as the symptoms are input and reflect them in the analysis. For example, heart rate and body temperature are acquired in real time and used for symptom analysis. In this way, by acquiring vital signs in real time and reflecting them in symptom analysis, more accurate analysis is possible.

[0050] The symptom input unit can simultaneously input information about the treatments and prescription medications the user has received in the past when inputting symptoms, and use this information for analysis. For example, when inputting symptoms, the unit automatically acquires information about the treatments and prescription medications the user has received in the past and uses this information for analysis. For example, the unit analyzes symptoms based on past prescription medication data. The symptom input unit also constructs a system that simultaneously acquires the user's treatment history and prescription medication information when inputting symptoms, and reflects this information in the analysis. For example, the unit analyzes symptoms based on past treatment history and prescription medication data. The symptom input unit also simultaneously acquires the user's past treatment and prescription medication information in an integrated manner when inputting symptoms, and uses this information for analysis. For example, the unit analyzes symptoms based on past treatment history and prescription medication data. This enables more accurate symptom analysis by utilizing information about past treatments and prescription medications in the analysis.

[0051] The symptom input unit uses voice input and image recognition technology to input symptoms, thereby reducing the burden on the user. For example, a system is constructed in which symptoms are input by voice, reducing the burden on the user. For example, the user describes the symptoms by voice, which is converted into text and analyzed. Furthermore, a system is developed in which the symptom input unit uses image recognition technology to input symptoms. For example, the user takes a photo of the affected area, which is analyzed to identify the symptoms. Furthermore, a system is constructed in which the symptom input unit uses voice input and image recognition technology in an integrated manner to input symptoms. For example, by describing the symptoms by voice and confirming the affected area with an image, a more accurate analysis is performed. In this way, the burden on the user can be reduced by using voice input and image recognition technology.

[0052] When providing medical service information, the medical information providing unit can refer to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, when providing medical service information, the unit automatically refers to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, medical institutions that the user has given high ratings to in the past are preferentially listed. The medical information providing unit also analyzes the user's medical institution usage history and builds a system that preferentially displays highly reliable medical institutions. For example, based on the past usage history, highly reliable medical institutions are identified and preferentially displayed. When providing medical service information, the medical information providing unit automatically suggests highly reliable medical institutions based on the user's past medical institution usage history. For example, medical institutions that the user has been satisfied with in the past are preferentially displayed. In this way, by referring to the past medical institution usage history, highly reliable medical institutions can be preferentially displayed.

[0053] The medical information providing unit can collect medical institution ratings and word-of-mouth information in real time and provide it to users. For example, a system is constructed that collects medical institution ratings and word-of-mouth information in real time and provides it to users. For example, data is collected from online review sites and rating sites and displayed to users. The medical information providing unit also automatically collects medical institution ratings and word-of-mouth information and provides it to users in real time. For example, the medical institution ratings are updated based on the latest word-of-mouth information and presented to users. The medical information providing unit also develops a system that collects medical institution ratings and word-of-mouth information in real time and provides it to users. For example, data is collected from social media and review sites and displayed to users. In this way, medical institution ratings and word-of-mouth information can be collected in real time and provided to users, thereby providing highly reliable information.

[0054] The medical information providing unit can refer to the user's insurance information when providing medical service information, and prioritize displaying medical institutions that accept insurance. For example, when providing medical service information, the unit automatically obtains the user's insurance information and prioritizes displaying medical institutions that accept insurance. For example, it lists medical institutions that accept the user's insurance plan. The medical information providing unit also references the user's insurance information and builds a system that prioritizes displaying medical institutions that accept insurance. For example, it links with an insurance company's database to identify medical institutions that accept insurance. The medical information providing unit also automatically suggests medical institutions that accept insurance based on the user's insurance information when providing medical service information. For example, it prioritizes displaying medical institutions that accept the user's insurance plan. In this way, by referencing the user's insurance information, it is possible to prioritize displaying medical institutions that accept insurance.

[0055] The medical information providing unit can acquire the congestion status and waiting times of medical institutions in real time and provide them to users. For example, a system is constructed that acquires the congestion status and waiting times of medical institutions in real time and provides them to users. For example, the system cooperates with the reservation system and reception system of the medical institution to acquire the congestion status. The medical information providing unit also automatically acquires the congestion status and waiting times of medical institutions and provides them to users in real time. For example, the system acquires data from the reception system of the medical institution and displays it to the user. The medical information providing unit also develops a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to users. For example, the system cooperates with the reservation system and reception system of the medical institution to acquire the congestion status. In this way, the congestion status and waiting times of medical institutions can be acquired in real time and provided to the user, allowing the user to select an appropriate medical institution.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The location information acquisition unit acquires the user's location information. For example, the location information acquisition unit automatically acquires the current location using the smartphone's GPS function. The user can also manually input the location information. For example, when the user presses the "Get current location" button, the device automatically identifies the current location and acquires the location information. Step 2: The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe stomach pain." The symptom input unit allows the user to input symptoms using text input or multiple-choice format. Step 3: The medical information provider analyzes the symptom information entered by the symptom input unit and provides optimal medical service information. For example, the medical information provider uses a generation AI to create a list of nearby hospitals, clinics, and pharmacies based on the acquired location information and analyzed symptom information, and presents this to the user. The medical information provider also provides detailed information such as each medical institution's opening hours, contact information, and language support available.

[0058] (Example 2) A medical information provision system according to an embodiment of the present invention is a system that supports users in quickly and appropriately obtaining local medical service information when they become ill while traveling abroad. This system allows users to obtain the necessary information in three simple steps. This allows the medical information provision system to quickly and appropriately obtain local medical service information.

[0059] A medical information provision system according to an embodiment includes a location information acquisition unit, a symptom input unit, and a medical information provision unit. The location information acquisition unit acquires user location information. For example, the location information acquisition unit automatically acquires a user's current location using a smartphone's GPS function. Alternatively, the user can manually input location information into the location information acquisition unit. For example, when a user presses a "Get Current Location" button, the device automatically identifies the user's current location and acquires location information. The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe abdominal pain." The symptom input unit allows the user to input symptoms using text input or multiple-choice options. The medical information provision unit analyzes the symptom information input by the symptom input unit and provides optimal medical service information. For example, the medical information provision unit uses a generation AI to create a list of nearby hospitals, clinics, and pharmacies based on the acquired location information and the analyzed symptom information, and presents the list to the user. The medical information provision unit also provides detailed information such as each medical institution's opening hours, contact information, and language support. As a result, the medical information providing system according to the embodiment allows users to quickly and appropriately obtain local medical service information. For example, if a user suddenly develops a fever while traveling abroad, the user can obtain information about nearby hospitals and clinics immediately by acquiring their current location and entering the symptoms of the fever. Furthermore, since the system can find an appropriate medical institution regardless of language barriers, users can receive medical services with peace of mind.

[0060] The location information acquisition unit can refer to the user's past movement history and prioritize displaying frequently visited places. For example, when the user acquires location information, the location information acquisition unit refers to the user's past movement history and prioritize displaying frequently visited places. For example, it automatically lists places that the user has visited many times in the past and presents them as options. The location information acquisition unit also analyzes the user's movement history data and builds a system that prioritizes displaying frequently visited places. For example, it identifies areas that the user often visits based on past location information data and displays them prioritized. Furthermore, when the user inputs location information, the location information acquisition unit automatically suggests frequently visited places based on the user's past movement history. For example, it prioritizes displaying hospitals and clinics that the user has visited in the past. This prioritizes displaying places that the user frequently visits, making it easier to input location information.

[0061] The location information acquisition unit can acquire surrounding weather information and traffic conditions at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, the location information acquisition unit automatically acquires surrounding weather information when acquiring location information, and evaluates the ease of access to medical institutions. For example, medical institutions that are difficult to access in bad weather are avoided. The location information acquisition unit can also acquire traffic conditions in real time at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, medical institutions in areas where traffic congestion occurs are avoided. The location information acquisition unit can also construct a system that comprehensively evaluates weather information and traffic conditions when acquiring location information, and suggests the most suitable medical institution. For example, medical institutions that are easily accessible depending on the weather and traffic conditions are preferentially displayed. This makes it possible to evaluate the ease of access to medical institutions taking weather information and traffic conditions into consideration.

[0062] The location information acquisition unit uses an emotion estimation function to measure a user's stress level when inputting location information, and can simplify the input procedure if the user's stress level is high. The location information acquisition unit, for example, uses the emotion estimation function to measure the user's stress level when inputting location information in real time. For example, the location information acquisition unit calculates the stress level by analyzing facial expressions and voice tones. The location information acquisition unit also builds a system that simplifies the input procedure for location information if the user's stress level is high. For example, it enables location information to be acquired with one click. The location information acquisition unit also uses the emotion estimation function to measure the user's stress level, and automatically simplifies the input procedure if the user's stress level is high. For example, it reduces the number of input fields and requires only the minimum necessary information to be input. This simplifies the input procedure according to the user's stress level, thereby reducing the burden on the user.

[0063] The location information acquisition unit can simultaneously acquire the user's health history and allergy information when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the location information acquisition unit can automatically acquire the user's health history when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the location information acquisition unit can suggest the most suitable medical institution based on the user's past medical history and allergy information. The location information acquisition unit can also simultaneously acquire the user's allergy information when acquiring location information, and prioritize displaying medical institutions that can accommodate allergies. For example, it can list hospitals that accommodate specific allergies. The location information acquisition unit can also comprehensively evaluate the user's health history and allergy information when acquiring location information, and build a system that suggests the most suitable medical institution. For example, it can select a medical institution based on the user's past medical history and allergy information. This makes it possible to select the most suitable medical institution taking into account the user's health history and allergy information.

[0064] When acquiring location information, the location information acquisition unit can automatically filter the language support status of local medical institutions based on the user's language setting. For example, when acquiring location information, the location information acquisition unit automatically filters the language support status of local medical institutions based on the user's language setting. For example, if the user speaks English, medical institutions that can provide English support are preferentially displayed. The location information acquisition unit also acquires the user's language setting at the same time as acquiring location information, and builds a system that lists medical institutions that can provide language support. For example, hospitals that can provide the user's native language are displayed. The location information acquisition unit also evaluates the language support status of local medical institutions based on the user's language setting at the same time as acquiring location information, and suggests the most suitable medical institution. For example, if the user speaks Japanese, medical institutions that can provide Japanese support are preferentially displayed. This makes it possible to preferentially display medical institutions that can provide language support based on the user's language setting.

[0065] The location information acquisition unit can use the emotion estimation function to analyze the emotion of the user when inputting location information in real time and display a guide to elicit positive emotions. The location information acquisition unit, for example, uses the emotion estimation function to analyze the emotion of the user when inputting location information in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The location information acquisition unit also builds a system that analyzes the user's emotion in real time and displays a guide to elicit positive emotions. For example, it displays an encouraging message or positive feedback. The location information acquisition unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically displays a guide to elicit positive emotions. For example, if the user is feeling stressed, it displays advice to relax. This makes it possible to analyze the user's emotion in real time and display a guide to elicit positive emotions.

[0066] The symptom input unit can perform more accurate analysis by referring to the user's past health data and medical history when inputting symptoms. The symptom input unit, for example, automatically refers to the user's past health data when inputting symptoms to perform more accurate analysis. For example, it analyzes symptoms based on past medical history and test results. The symptom input unit also builds a system that simultaneously acquires the user's medical history when inputting symptoms and reflects it in the analysis. For example, it analyzes symptoms based on past medical history and treatment history. The symptom input unit also comprehensively evaluates the user's past health data simultaneously with symptom input to perform more accurate analysis. For example, it analyzes symptoms based on past medical history and health data. As a result, by referring to past health data and medical history, more accurate symptom analysis is possible.

[0067] The symptom input unit can acquire the user's vital signs from the smart device at the same time as the symptom input and reflect them in the analysis. For example, the symptom input unit automatically acquires the vital signs from the smart device when the symptom is input and reflects them in the analysis. For example, the heart rate and body temperature are acquired in real time and used for symptom analysis. The symptom input unit also builds a system that acquires the user's vital signs at the same time as the symptom is input and reflects them in the analysis. For example, data is acquired from a smart watch or a thermometer. The symptom input unit also acquires the vital signs from the smart device in an integrated manner at the same time as the symptom is input and reflects them in the analysis. For example, the heart rate and body temperature are acquired in real time and used for symptom analysis. In this way, by acquiring vital signs in real time and reflecting them in the symptom analysis, more accurate analysis is possible.

[0068] The symptom input unit uses the emotion estimation function to analyze the emotion a user expresses when entering symptoms, and can display a reassuring message if the emotion is strong. The symptom input unit, for example, uses the emotion estimation function to analyze the emotion a user expresses when entering symptoms in real time. For example, it analyzes facial expressions and voice tone to calculate an emotion score. The symptom input unit also builds a system that analyzes the user's emotion in real time and displays a reassuring message if the negative emotion is strong. For example, it displays an encouraging message or positive feedback. The symptom input unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically displays a reassuring message if the negative emotion is strong. For example, if the user is feeling anxious, it displays advice to help them relax. In this way, the user's anxiety can be reduced by analyzing the user's emotion in real time and displaying a reassuring message.

[0069] The symptom input unit can simultaneously input information about the treatments and prescription medications the user has received in the past when inputting symptoms, and use this information for analysis. For example, the symptom input unit automatically acquires information about the treatments and prescription medications the user has received in the past when inputting symptoms, and uses this information for analysis. For example, the symptom input unit analyzes symptoms based on past prescription medication data. The symptom input unit also constructs a system that simultaneously acquires the user's treatment history and prescription medication information when inputting symptoms, and reflects this information in the analysis. For example, the symptom input unit analyzes symptoms based on past treatment history and prescription medication data. The symptom input unit also comprehensively acquires the user's past treatment and prescription medication information when inputting symptoms, and uses this information for analysis. For example, the symptom input unit analyzes symptoms based on past treatment history and prescription medication data. By utilizing information about past treatments and prescription medications in the analysis, more accurate symptom analysis is possible.

[0070] The symptom input unit uses voice input and image recognition technology to input symptoms, thereby reducing the burden on the user. The symptom input unit, for example, constructs a system that inputs symptoms by voice and reduces the burden on the user. For example, the user describes the symptoms by voice, which is converted into text and analyzed. The symptom input unit also develops a system that inputs symptoms using image recognition technology. For example, the user takes a photo of the affected area, which is analyzed to identify the symptoms. The symptom input unit also constructs a system that inputs symptoms by integrating voice input and image recognition technology. For example, by describing the symptoms by voice and confirming the affected area with an image, a more accurate analysis is performed. In this way, the burden on the user can be reduced by using voice input and image recognition technology.

[0071] The symptom input unit uses the emotion estimation function to analyze the emotion of the user when entering symptoms in real time, and can provide emotional support according to the input content. The symptom input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering symptoms in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The symptom input unit also analyzes the user's emotion in real time, and builds a system that provides emotional support according to the input content. For example, it displays encouraging messages and positive feedback. The symptom input unit also uses the emotion estimation function to analyze the user's emotion in real time, and automatically provides emotional support according to the input content. For example, if the user is feeling anxious, it displays advice to help the user relax. In this way, the user's anxiety can be reduced by analyzing the user's emotion in real time and providing emotional support according to the input content.

[0072] The medical information providing unit, when providing medical service information, can refer to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, when providing medical service information, the medical information providing unit automatically refers to the user's past medical institution usage history and preferentially displays highly reliable medical institutions. For example, it preferentially lists medical institutions that the user has given high ratings to in the past. The medical information providing unit also analyzes the user's medical institution usage history and builds a system that preferentially displays highly reliable medical institutions. For example, it identifies highly reliable medical institutions based on the past usage history and displays them preferentially. The medical information providing unit, when providing medical service information, automatically suggests highly reliable medical institutions based on the user's past medical institution usage history. For example, it preferentially displays medical institutions that the user has been satisfied with in the past. In this way, by referring to the past medical institution usage history, highly reliable medical institutions can be preferentially displayed.

[0073] The medical information providing unit can collect evaluations and word-of-mouth information about medical institutions in real time and provide it to users. The medical information providing unit, for example, builds a system that collects evaluations and word-of-mouth information about medical institutions in real time and provides it to users. For example, it collects data from online review sites and rating sites and displays it to users. The medical information providing unit also automatically collects evaluations and word-of-mouth information about medical institutions and provides it to users in real time. For example, it updates the evaluations of medical institutions based on the latest word-of-mouth information and presents it to users. The medical information providing unit also develops a system that collects evaluations and word-of-mouth information about medical institutions in real time and provides it to users. For example, it collects data from social media and review sites and displays it to users. In this way, evaluations and word-of-mouth information about medical institutions can be collected in real time and provided to users, thereby providing highly reliable information.

[0074] The medical information providing unit can use the emotion estimation function to analyze the emotion of the user when receiving medical service information and prioritize displaying information that gives a sense of security. The medical information providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving medical service information in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The medical information providing unit also builds a system that analyzes the user's emotion in real time and prioritizes displaying information that gives a sense of security. For example, it prioritizes displaying medical institutions with positive word-of-mouth reviews and high ratings. The medical information providing unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically display information that gives a sense of security. For example, if the user is feeling anxious, it displays messages or information that give a sense of security. In this way, the user's anxiety can be reduced by analyzing the user's emotion and prioritize displaying information that gives a sense of security.

[0075] The medical information providing unit can refer to the user's insurance information when providing medical service information and prioritize displaying medical institutions that accept insurance. For example, the medical information providing unit automatically acquires the user's insurance information when providing medical service information and prioritizes displaying medical institutions that accept insurance. For example, it lists medical institutions that accept the user's insurance plan. The medical information providing unit also references the user's insurance information and builds a system that prioritizes displaying medical institutions that accept insurance. For example, it links with an insurance company's database to identify medical institutions that accept insurance. The medical information providing unit also automatically suggests medical institutions that accept insurance based on the user's insurance information when providing medical service information. For example, it prioritizes displaying medical institutions that accept the user's insurance plan. In this way, by referencing the user's insurance information, it is possible to prioritize displaying medical institutions that accept insurance.

[0076] The medical information providing unit can acquire the congestion status and waiting times of medical institutions in real time and provide them to the user. The medical information providing unit, for example, builds a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to the user. For example, it works in cooperation with the reservation system or reception system of the medical institution to acquire the congestion status. The medical information providing unit also automatically acquires the congestion status and waiting times of medical institutions and provides them to the user in real time. For example, it acquires data from the reception system of the medical institution and displays it to the user. The medical information providing unit also develops a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to the user. For example, it works in cooperation with the reservation system or reception system of the medical institution to acquire the congestion status. In this way, the congestion status and waiting times of medical institutions can be acquired in real time and provided to the user, allowing the user to select an appropriate medical institution.

[0077] The medical information providing unit can use the emotion estimation function to analyze the emotions of a user when receiving medical service information in real time and provide information to elicit positive emotions. The medical information providing unit, for example, uses the emotion estimation function to analyze the emotions of a user when receiving medical service information in real time. For example, the medical information providing unit analyzes facial expressions and voice tones to calculate an emotion score. The medical information providing unit also builds a system that analyzes the user's emotions in real time and provides information to elicit positive emotions. For example, medical institutions with positive word-of-mouth reviews and high ratings are preferentially displayed. The medical information providing unit also uses the emotion estimation function to analyze the user's emotions in real time and automatically provide information to elicit positive emotions. For example, if the user is feeling anxious, a message or information that provides a sense of security is displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotions in real time and providing information to elicit positive emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The location information acquisition unit acquires the user's location information. For example, the location information acquisition unit automatically acquires the user's current location using the smartphone's GPS function. Alternatively, the location information acquisition unit can manually input the user's location information. For example, when the user presses a "Get current location" button, the device automatically identifies the user's current location and acquires the location information. The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe abdominal pain." The symptom input unit can input symptoms using text input or multiple-choice format. The medical information provision unit analyzes the symptom information input by the symptom input unit and provides optimal medical service information. For example, the medical information provision unit uses a generation AI to list information about nearby hospitals, clinics, and pharmacies based on the acquired location information and the analyzed symptom information, and presents this to the user. The medical information provision unit also provides detailed information such as each medical institution's opening hours, contact information, and language support. This allows the medical information provision system according to the embodiment to quickly and appropriately obtain local medical service information. For example, if a user suddenly develops a fever while traveling abroad, they can obtain information about nearby hospitals and clinics by obtaining their current location and entering their fever symptoms. Furthermore, since the system can find an appropriate medical institution across language barriers, users can receive medical services with peace of mind.

[0080] The location information acquisition unit can refer to the user's past movement history and prioritize displaying frequently visited places. For example, when the user acquires location information, the past movement history is referenced and frequently visited places are prioritized. For example, places that the user has visited many times in the past are automatically listed and presented as options. The location information acquisition unit also analyzes the user's movement history data and builds a system that prioritizes displaying frequently visited places. For example, areas that the user often visits are identified based on past location information data and displayed with priority. Furthermore, when the user inputs location information, the location information acquisition unit automatically suggests frequently visited places based on the past movement history. For example, hospitals and clinics that the user has visited in the past are displayed with priority. This prioritizes displaying places that the user frequently visits, making it easier to input location information.

[0081] The location information acquisition unit can acquire surrounding weather information and traffic conditions at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, when acquiring location information, it automatically acquires surrounding weather information and evaluates the ease of access to medical institutions. For example, in bad weather, medical institutions that are difficult to access are avoided. The location information acquisition unit can also acquire traffic conditions in real time at the same time as acquiring location information, and evaluate the ease of access to medical institutions. For example, medical institutions in areas where traffic congestion occurs are avoided. The location information acquisition unit can also construct a system that comprehensively evaluates weather information and traffic conditions when acquiring location information, and suggests the most suitable medical institution. For example, it can prioritize and display medical institutions that are easily accessible depending on the weather and traffic conditions. This makes it possible to evaluate the ease of access to medical institutions taking weather information and traffic conditions into consideration.

[0082] The location information acquisition unit uses an emotion estimation function to measure a user's stress level when inputting location information, and can simplify the input procedure if the user's stress level is high. For example, the emotion estimation function is used to measure the user's stress level when inputting location information in real time. For example, the stress level is calculated by analyzing facial expressions and voice tone. The location information acquisition unit also builds a system that simplifies the location information input procedure if the user's stress level is high. For example, it makes it possible to acquire location information with one click. The location information acquisition unit also uses the emotion estimation function to measure the user's stress level, and automatically simplifies the input procedure if the user's stress level is high. For example, it reduces the number of input fields and requires only the minimum necessary information to be entered. This simplifies the input procedure according to the user's stress level, thereby reducing the burden on the user.

[0083] The location information acquisition unit can simultaneously acquire the user's health history and allergy information when acquiring location information, and use this information as a reference for selecting a medical institution. For example, the user's health history can be automatically acquired when acquiring location information, and used as a reference for selecting a medical institution. For example, the most suitable medical institution can be suggested based on past medical history and allergy information. The location information acquisition unit can also simultaneously acquire the user's allergy information when acquiring location information, and prioritize displaying medical institutions that can accommodate allergies. For example, it can list hospitals that accommodate specific allergies. The location information acquisition unit can also comprehensively evaluate the user's health history and allergy information when acquiring location information, and build a system that suggests the most suitable medical institution. For example, it can select a medical institution based on past medical history and allergy information. This makes it possible to select the most suitable medical institution taking into account the user's health history and allergy information.

[0084] When acquiring location information, the location information acquisition unit can automatically filter the language support status of local medical institutions based on the user's language setting. For example, when acquiring location information, the location information acquisition unit automatically filters the language support status of local medical institutions based on the user's language setting. For example, if the user speaks English, medical institutions that can provide English support are preferentially displayed. The location information acquisition unit also acquires the user's language setting at the same time as acquiring location information, and builds a system that lists medical institutions that can provide language support. For example, hospitals that can provide the user's native language are displayed. The location information acquisition unit also evaluates the language support status of local medical institutions based on the user's language setting at the same time as acquiring location information, and suggests the most suitable medical institution. For example, if the user speaks Japanese, medical institutions that can provide Japanese support are preferentially displayed. This makes it possible to preferentially display medical institutions that can provide language support based on the user's language setting.

[0085] The location information acquisition unit can use the emotion estimation function to analyze the emotion of the user when entering location information in real time and display a guide to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotion of the user when entering location information in real time. For example, facial expressions and voice tone are analyzed to calculate an emotion score. The location information acquisition unit also builds a system that analyzes the user's emotion in real time and displays a guide to elicit positive emotions. For example, encouraging messages and positive feedback are displayed. The location information acquisition unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically display a guide to elicit positive emotions. For example, if the user is feeling stressed, advice to relax is displayed. This makes it possible to analyze the user's emotion in real time and display a guide to elicit positive emotions.

[0086] The symptom input unit can perform more accurate analysis by referring to the user's past health data and medical history when inputting symptoms. For example, the user's past health data can be automatically referenced when inputting symptoms to perform more accurate analysis. For example, symptoms can be analyzed based on past medical history and test results. The symptom input unit can also build a system that simultaneously acquires the user's medical history when inputting symptoms and reflects it in the analysis. For example, symptoms can be analyzed based on past medical history and treatment history. The symptom input unit can also comprehensively evaluate the user's past health data when inputting symptoms to perform more accurate analysis. For example, symptoms can be analyzed based on past medical history and health data. By referring to past health data and medical history, more accurate symptom analysis is possible.

[0087] The symptom input unit can acquire the user's vital signs from the smart device at the same time as the symptoms are input and reflect them in the analysis. For example, when symptoms are input, the vital signs are automatically acquired from the smart device and reflected in the analysis. For example, heart rate and body temperature are acquired in real time and used for symptom analysis. In addition, the symptom input unit can build a system that acquires the user's vital signs at the same time as the symptoms are input and reflects them in the analysis. For example, data is acquired from a smart watch or a thermometer. In addition, the symptom input unit can comprehensively acquire vital signs from the smart device at the same time as the symptoms are input and reflect them in the analysis. For example, heart rate and body temperature are acquired in real time and used for symptom analysis. In this way, by acquiring vital signs in real time and reflecting them in symptom analysis, more accurate analysis is possible.

[0088] The symptom input unit uses the emotion estimation function to analyze the emotion a user feels when entering symptoms, and can display a reassuring message if the emotion is strong. For example, the emotion estimation function is used to analyze the emotion a user feels when entering symptoms in real time. For example, facial expressions and voice tone are analyzed to calculate an emotion score. The symptom input unit also builds a system that analyzes the user's emotion in real time and displays a reassuring message if the negative emotion is strong. For example, an encouraging message or positive feedback is displayed. The symptom input unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically displays a reassuring message if the negative emotion is strong. For example, if the user is feeling anxious, advice to help them relax is displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotion in real time and displaying a reassuring message.

[0089] The symptom input unit can simultaneously input information about the treatments and prescription medications the user has received in the past when inputting symptoms, and use this information for analysis. For example, when inputting symptoms, the unit automatically acquires information about the treatments and prescription medications the user has received in the past and uses this information for analysis. For example, the unit analyzes symptoms based on past prescription medication data. The symptom input unit also constructs a system that simultaneously acquires the user's treatment history and prescription medication information when inputting symptoms, and reflects this information in the analysis. For example, the unit analyzes symptoms based on past treatment history and prescription medication data. The symptom input unit also simultaneously acquires the user's past treatment and prescription medication information in an integrated manner when inputting symptoms, and uses this information for analysis. For example, the unit analyzes symptoms based on past treatment history and prescription medication data. This enables more accurate symptom analysis by utilizing information about past treatments and prescription medications in the analysis.

[0090] The symptom input unit uses voice input and image recognition technology to input symptoms, thereby reducing the burden on the user. For example, a system is constructed in which symptoms are input by voice, reducing the burden on the user. For example, the user describes the symptoms by voice, which is converted into text and analyzed. Furthermore, a system is developed in which the symptom input unit uses image recognition technology to input symptoms. For example, the user takes a photo of the affected area, which is analyzed to identify the symptoms. Furthermore, a system is constructed in which the symptom input unit uses voice input and image recognition technology in an integrated manner to input symptoms. For example, by describing the symptoms by voice and confirming the affected area with an image, a more accurate analysis is performed. In this way, the burden on the user can be reduced by using voice input and image recognition technology.

[0091] The symptom input unit can use the emotion estimation function to analyze the emotion of the user when entering symptoms in real time and provide emotional support according to the input content. For example, the emotion estimation function is used to analyze the emotion of the user when entering symptoms in real time. For example, facial expressions and voice tone are analyzed to calculate an emotion score. The symptom input unit also analyzes the user's emotion in real time and builds a system that provides emotional support according to the input content. For example, encouraging messages and positive feedback are displayed. The symptom input unit also uses the emotion estimation function to analyze the user's emotion in real time and automatically provide emotional support according to the input content. For example, if the user is feeling anxious, advice to help them relax is displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotion in real time and providing emotional support according to the input content.

[0092] When providing medical service information, the medical information providing unit can refer to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, when providing medical service information, the unit automatically refers to the user's past medical institution usage history and preferentially display highly reliable medical institutions. For example, medical institutions that the user has given high ratings to in the past are preferentially listed. The medical information providing unit also analyzes the user's medical institution usage history and builds a system that preferentially displays highly reliable medical institutions. For example, based on the past usage history, highly reliable medical institutions are identified and preferentially displayed. When providing medical service information, the medical information providing unit automatically suggests highly reliable medical institutions based on the user's past medical institution usage history. For example, medical institutions that the user has been satisfied with in the past are preferentially displayed. In this way, by referring to the past medical institution usage history, highly reliable medical institutions can be preferentially displayed.

[0093] The medical information providing unit can collect medical institution ratings and word-of-mouth information in real time and provide it to users. For example, a system is constructed that collects medical institution ratings and word-of-mouth information in real time and provides it to users. For example, data is collected from online review sites and rating sites and displayed to users. The medical information providing unit also automatically collects medical institution ratings and word-of-mouth information and provides it to users in real time. For example, the medical institution ratings are updated based on the latest word-of-mouth information and presented to users. The medical information providing unit also develops a system that collects medical institution ratings and word-of-mouth information in real time and provides it to users. For example, data is collected from social media and review sites and displayed to users. In this way, medical institution ratings and word-of-mouth information can be collected in real time and provided to users, thereby providing highly reliable information.

[0094] The medical information providing unit can use the emotion estimation function to analyze the emotions of the user when receiving medical service information and prioritize displaying information that gives a sense of security. For example, the emotion estimation function is used to analyze the emotions of the user when receiving medical service information in real time. For example, facial expressions and tone of voice are analyzed to calculate an emotion score. The medical information providing unit also builds a system that analyzes the user's emotions in real time and prioritizes displaying information that gives a sense of security. For example, medical institutions with positive word-of-mouth reviews and high ratings are prioritized. The medical information providing unit also uses the emotion estimation function to analyze the user's emotions in real time and automatically display information that gives a sense of security. For example, if the user is feeling anxious, a message or information that gives a sense of security is displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotions and preferentially displaying information that gives a sense of security.

[0095] The medical information providing unit can refer to the user's insurance information when providing medical service information, and prioritize displaying medical institutions that accept insurance. For example, when providing medical service information, the unit automatically obtains the user's insurance information and prioritizes displaying medical institutions that accept insurance. For example, it lists medical institutions that accept the user's insurance plan. The medical information providing unit also references the user's insurance information and builds a system that prioritizes displaying medical institutions that accept insurance. For example, it links with an insurance company's database to identify medical institutions that accept insurance. The medical information providing unit also automatically suggests medical institutions that accept insurance based on the user's insurance information when providing medical service information. For example, it prioritizes displaying medical institutions that accept the user's insurance plan. In this way, by referencing the user's insurance information, it is possible to prioritize displaying medical institutions that accept insurance.

[0096] The medical information providing unit can acquire the congestion status and waiting times of medical institutions in real time and provide them to users. For example, a system is constructed that acquires the congestion status and waiting times of medical institutions in real time and provides them to users. For example, the system cooperates with the reservation system and reception system of the medical institution to acquire the congestion status. The medical information providing unit also automatically acquires the congestion status and waiting times of medical institutions and provides them to users in real time. For example, the system acquires data from the reception system of the medical institution and displays it to the user. The medical information providing unit also develops a system that acquires the congestion status and waiting times of medical institutions in real time and provides them to users. For example, the system cooperates with the reservation system and reception system of the medical institution to acquire the congestion status. In this way, the congestion status and waiting times of medical institutions can be acquired in real time and provided to the user, allowing the user to select an appropriate medical institution.

[0097] The medical information providing unit can use the emotion estimation function to analyze the emotions of a user when receiving medical service information in real time and provide information to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of a user when receiving medical service information in real time. For example, facial expressions and tone of voice are analyzed to calculate an emotion score. The medical information providing unit also builds a system that analyzes the user's emotions in real time and provides information to elicit positive emotions. For example, medical institutions with positive reviews and high ratings are preferentially displayed. The medical information providing unit also uses the emotion estimation function to analyze the user's emotions in real time and automatically provide information to elicit positive emotions. For example, if the user is feeling anxious, a message or information that provides a sense of security is displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotions in real time and providing information to elicit positive emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The location information acquisition unit acquires the user's location information. For example, the location information acquisition unit automatically acquires the current location using the smartphone's GPS function. The user can also manually input the location information. For example, when the user presses the "Get current location" button, the device automatically identifies the current location and acquires the location information. Step 2: The symptom input unit inputs the user's symptoms based on the location information acquired by the location information acquisition unit. For example, the symptom input unit allows the user to input specific symptoms such as "I have a headache," "I have a fever," or "I have severe stomach pain." The symptom input unit allows the user to input symptoms using text input or multiple-choice format. Step 3: The medical information provider analyzes the symptom information entered by the symptom input unit and provides optimal medical service information. For example, the medical information provider uses a generation AI to create a list of nearby hospitals, clinics, and pharmacies based on the acquired location information and analyzed symptom information, and presents this to the user. The medical information provider also provides detailed information such as each medical institution's opening hours, contact information, and language support available.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, 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 robot 414 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.

[0145] 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.

[0146] 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.

[0147] 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 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a location information acquisition unit that acquires location information; a symptom input unit that inputs a symptom of a user based on the location information acquired by the location information acquisition unit; a medical information providing unit that analyzes the symptom information input by the symptom input unit and provides optimal medical service information. A system characterized by:

2. The location information acquisition unit Refer to the user's past travel history and prioritize frequently visited locations 2. The system of claim 1.

3. The location information acquisition unit At the same time as obtaining location information, weather information and traffic conditions in the surrounding area are also obtained to evaluate the ease of access to medical facilities.

2. The system of claim 1.

4. The location information acquisition unit Measure the stress level of the user when entering location information, and simplify the input procedure if stress is high 2. The system of claim 1.

5. The location information acquisition unit When obtaining location information, the user's health history and allergy information will also be obtained at the same time, and this information will be used as a reference for selecting a medical institution.

2. The system of claim 1.

6. The location information acquisition unit When obtaining location information, automatically filter the language availability of local medical institutions based on the user's language settings.

2. The system of claim 1.

7. The location information acquisition unit Analyzes emotions in real time when users enter location information and displays guidance to elicit positive emotions 2. The system of claim 1.

8. The symptom input unit When entering symptoms, the system references the user's past health data and medical history to perform a more accurate analysis.

2. The system of claim 1.

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