system

Abstract

The system according to the embodiment aims to enable patients to quickly find an appropriate medical institution and communicate with them in real time. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a communication unit. The reception unit inputs symptoms. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes medical facilities based on the analysis results generated by the analysis unit. The communication unit communicates in real time with the medical facilities proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, patients may have difficulty finding the right medical facility and may be passed around from hospital to hospital.

[0005] The system according to the embodiment aims to enable patients to quickly find an appropriate medical institution and communicate with them in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a communication unit. The reception unit inputs symptoms. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes medical facilities based on the analysis results generated by the analysis unit. The communication unit communicates in real time with the medical facilities proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows patients to quickly find the appropriate medical institution and communicate with them in real time. [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 institution referral system according to an embodiment of the present invention helps patients accurately find the medical institution they need. This system allows patients to input their health problems and symptoms, and a generating AI analyzes the input information to recommend the most appropriate medical institution and specialist. Furthermore, real-time communication with the suggested medical institution prevents patients from being passed around from hospital to hospital. For example, if a patient inputs "I have a persistent headache," the generating AI analyzes the information and recommends specialists such as neurologists and neurosurgeons. Furthermore, patients can consult directly with the suggested medical institution via chat, streamlining the process for appointments and initial consultations. This system enables patients to receive the necessary medical care quickly and accurately and receive appropriate advice for their health problems. This document clearly states that patients can input their symptoms using text or voice input. It also describes that the generating AI uses data such as past medical records and medical literature to analyze. Chat and video call functions are provided as means of real-time communication. This allows the medical institution referral system to accurately find the medical institution they need and communicate with them in real time.

[0029] The medical facility referral system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a communication unit. The reception unit is a unit through which a patient inputs symptoms. When inputting symptoms, the patient can input them by text or voice. For example, the patient can input symptoms using a keyboard. Alternatively, the patient can input symptoms by voice using a microphone. The analysis unit is a unit that analyzes the information received by the reception unit. The analysis unit analyzes the input information using a generation AI. For example, the analysis unit analyzes the information based on past medical data and medical literature. The proposal unit is a unit that proposes medical facilities based on the analysis results generated by the analysis unit. The proposal unit proposes the most suitable medical facility using a generation AI. For example, the proposal unit proposes specialists such as neurologists and neurosurgeons based on the analysis results. The communication unit is a unit that communicates in real time with the medical facilities proposed by the proposal unit. The communication unit provides chat and video call functions. For example, the communication unit allows the patient to directly chat with the proposed medical facility. The communication unit can also communicate in real time with the medical facility via video call. As a result, the medical facility referral system according to the embodiment allows the patient to accurately find the medical facility they need and communicate with them in real time.

[0030] The reception unit can receive text input or voice input. For example, the patient can input symptoms into the reception unit using a keyboard. The reception unit can also allow the patient to input symptoms by voice using a microphone. For example, the patient can input "My headache continues" using a keyboard. The patient can also input "My headache continues" by voice using a microphone. This allows the patient to input symptoms using text input or voice input.

[0031] The analysis unit can analyze information based on past medical data or medical literature. The analysis unit, for example, analyzes information based on past medical data. For example, the analysis unit analyzes information based on electronic medical records or medical records. The analysis unit can also analyze information based on medical literature. For example, the analysis unit analyzes information based on academic papers or medical books. In this way, by performing analysis based on past medical data or medical literature, it becomes possible to suggest more accurate medical institutions.

[0032] The suggestion unit can suggest an appropriate medical institution based on the analysis results. For example, the suggestion unit suggests a specialist such as a neurologist or a neurosurgeon based on the analysis results. For example, the suggestion unit suggests the most appropriate medical institution based on the analysis results. This allows the patient to receive appropriate medical care by suggesting the most appropriate medical institution based on the analysis results.

[0033] The communication unit may provide a chat function or a video call function. For example, the communication unit may allow a patient to directly chat with a proposed medical institution. The communication unit may also communicate with the medical institution in real time through a video call. For example, the communication unit may allow a patient to chat with a proposed medical institution and smoothly complete procedures for making an appointment or an initial consultation. The communication unit may also communicate with the medical institution in real time through a video call and smoothly complete procedures for making an appointment or an initial consultation. This allows a patient to communicate with the medical institution in real time.

[0034] The reception unit can analyze the patient's past symptom input history and suggest an appropriate input method. For example, the reception unit can automatically display symptoms that the patient has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used during a specific time period based on the patient's past input history. This reduces the patient's input burden by suggesting the optimal input method based on the past input history. Analysis of the past symptom input history is performed using, for example, a generation AI. For example, the reception unit can input the patient's past symptom input history data into the generation AI, which can then analyze the data and suggest the optimal input method.

[0035] When inputting symptoms, the reception unit can filter the input content based on the patient's current health condition and lifestyle habits. For example, the reception unit filters out less relevant symptoms based on the patient's current health condition. The reception unit can also preferentially display more relevant symptoms based on the patient's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter out specific symptoms based on the patient's medical history. In this way, by filtering the input content based on the patient's health condition and lifestyle habits, more relevant symptoms can be input. The filtering process is performed, for example, using a generation AI. For example, the reception unit can input the patient's health condition and lifestyle habit data into the generation AI, which can then analyze the data and filter the input content.

[0036] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms based on the patient's geographical location information. For example, if the patient is in a specific area, the reception unit can prioritize inputting symptoms related to diseases prevalent in that area. Also, if the patient is traveling, the reception unit can prioritize inputting symptoms related to health risks at the patient's destination. Furthermore, if the patient is in a specific environment (such as a factory or farm), the reception unit can prioritize inputting symptoms related to that environment. In this way, by taking geographical location information into consideration, symptoms specific to the area can be prioritized. Geographical location information can be acquired using, for example, GPS data or address information. For example, the reception unit can input GPS data acquired from the patient's smartphone into the generation AI, and the generation AI can analyze the geographical location information and prioritize inputting highly relevant symptoms.

[0037] When entering symptoms, the reception unit can analyze the patient's social media activity and enter related symptoms. The reception unit can, for example, enter related symptoms based on health information shared by the patient on social media. The reception unit can also enter related symptoms based on information obtained from health-related accounts the patient follows on social media. The reception unit can also enter related symptoms based on information obtained from health-related groups the patient participates in on social media. This allows for more accurate symptom entry by entering related symptoms based on social media activity. Analysis of social media activity can be performed, for example, using a generation AI. For example, the reception unit can enter the patient's social media activity data into a generation AI, which can then analyze the data and enter related symptoms.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, the analysis unit performs a detailed analysis for serious symptoms. The analysis unit can also perform a simplified analysis for minor symptoms. Furthermore, the analysis unit can determine the priority of the analysis according to the severity of the symptoms. This allows for efficient analysis by adjusting the level of detail of the analysis according to the severity of the symptoms. The severity of the symptoms is evaluated, for example, using a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which analyzes the data to evaluate the severity of the symptoms and adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit can apply a respiratory-specific analysis algorithm to respiratory symptoms. The analysis unit can also apply a digestive-specific analysis algorithm to digestive symptoms. The analysis unit can also apply a neurological-specific analysis algorithm to nervous system symptoms. This allows for more specialized analysis by applying an analysis algorithm depending on the symptom category. The application of the analysis algorithm is performed using, for example, a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which then analyzes the data and applies an analysis algorithm depending on the symptom category.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom onset. For example, the analysis unit prioritizes analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have continued for a long period of time. Furthermore, the analysis unit can determine the priority of analysis according to the time of symptom onset. This enables a prompt response by determining the priority of analysis based on the time of symptom onset. The time of symptom onset is obtained, for example, using self-reported data or medical data. For example, the analysis unit can input the patient's self-reported data or medical data into the generation AI, which can then analyze the data and determine the priority of analysis based on the time of symptom onset.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms. This enables efficient analysis by adjusting the order of analysis based on the relevance of symptoms. The evaluation of the relevance of symptoms is performed, for example, using a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which analyzes the data to evaluate the relevance of symptoms and adjust the order of analysis.

[0042] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the medical institution. For example, the suggestion unit provides detailed information to important medical institutions. The suggestion unit can also provide simplified information to general medical institutions. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the medical institution. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the medical institution. The importance of the medical institution is evaluated, for example, using a generation AI. For example, the suggestion unit can input data of the medical institution into the generation AI, which analyzes the data to evaluate the importance of the medical institution and adjust the level of detail of the proposal.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the medical institution. For example, the proposal unit applies a specialized proposal algorithm to a specialized hospital. The proposal unit can also apply a general proposal algorithm to a general hospital. Furthermore, the proposal unit can also apply a proposal algorithm for clinics to a clinic. This makes it possible to make optimal proposals depending on the category of the medical institution. The proposal algorithm is applied, for example, using a generation AI. For example, the proposal unit can input data of the medical institution into the generation AI, which can analyze the data and apply a proposal algorithm depending on the category of the medical institution.

[0044] When making a proposal, the suggestion unit can determine the priority of the proposal based on the location information of the medical institution. For example, the suggestion unit can prioritize proposing medical institutions close to the patient's current location. The suggestion unit can also prioritize proposing medical institutions along the patient's commute route. Furthermore, the suggestion unit can also prioritize proposing medical institutions close to the patient's home. In this way, by determining the priority of the proposal based on the location information of the medical institution, it is possible to propose the most suitable medical institution for the patient. The location information of the medical institution is obtained using, for example, GPS data or address information. For example, the suggestion unit can input GPS data obtained from the patient's smartphone into the generation AI, and the generation AI can analyze the location information and propose the most suitable medical institution.

[0045] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the medical institutions. For example, the suggestion unit prioritizes proposing medical institutions that are most relevant to the patient's symptoms. The suggestion unit can also suggest medical institutions that are highly relevant based on the patient's medical history. Furthermore, the suggestion unit can also suggest medical institutions that are highly relevant based on the patient's current health condition. In this way, by adjusting the order of proposals based on the relevance of the medical institutions, more appropriate medical institutions can be proposed. The relevance of the medical institutions is evaluated, for example, using a generation AI. For example, the suggestion unit can input the patient's symptom data and medical history data into the generation AI, which then analyzes the data to evaluate the relevance of the medical institutions and adjust the order of proposals.

[0046] When communicating, the communication unit can select an appropriate method by referring to the patient's past communication history. For example, the communication unit can prioritize and suggest communication methods (chat, video call, etc.) that the patient has used favorably in the past. The communication unit can also communicate at the optimal timing based on the patient's past communication history. Furthermore, the communication unit can analyze the patient's past communication history and select the most effective method. This enables more effective communication by selecting the optimal method based on the past communication history. Analysis of the past communication history is performed, for example, using a generation AI. For example, the communication unit can input the patient's past communication history data into the generation AI, which can then analyze the data and select the optimal communication method.

[0047] The communication unit can customize the means of communication based on the patient's current health condition when communicating. For example, if the patient is feeling unwell, the communication unit prioritizes text chat to reduce the burden on the patient. If the patient is healthy, the communication unit can also suggest video calls for more detailed communication. Furthermore, if the patient is tired, the communication unit can also suggest communication via short messages. This enables more appropriate communication by customizing the means of communication according to the patient's health condition. The current health condition can be evaluated, for example, using self-reported data or medical data. For example, the communication unit can input the patient's self-reported data or medical data into the generation AI, which can then analyze the data to evaluate the patient's health condition and suggest the optimal means of communication.

[0048] When communicating, the communication unit can select an appropriate communication method based on the patient's geographical location information. For example, if the patient is in a remote location, the communication unit can suggest a video call. The communication unit can also suggest face-to-face communication if the patient is nearby. Furthermore, the communication unit can also suggest a voice call if the patient is on the move. This makes it possible to select the optimal communication method for the patient by taking geographical location information into consideration. Geographical location information is acquired using, for example, GPS data or address information. For example, the communication unit can input GPS data acquired from the patient's smartphone into the generation AI, which can then analyze the location information and suggest the optimal communication method.

[0049] When communicating, the communication unit can analyze the patient's social media activity and suggest communication methods. For example, if the patient is active on social media, the communication unit can suggest a chat function. Also, if the patient shares health information on social media, the communication unit can suggest a video call. Furthermore, the communication unit can communicate in collaboration with medical professionals the patient follows on social media. This enables more effective communication by suggesting the optimal communication method based on social media activity. Analysis of social media activity is performed, for example, using generative AI. For example, the communication unit can input the patient's social media activity data into generative AI, which can analyze the data and suggest the optimal communication method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can automatically generate related questions when symptoms are entered based on the patient's past medical history. For example, if a patient has complained of headaches in the past, the reception unit can automatically generate questions such as "How often do you have headaches?" or "When did your headaches start?" and present them to the patient. In addition, if the patient has used a specific medication in the past, questions about the effects and side effects of that medication can also be automatically generated. Furthermore, the reception unit can automatically generate questions about specific test or diagnostic results based on the patient's past medical history. This makes it possible to collect more detailed information by utilizing the patient's past medical history.

[0052] The analysis unit can estimate the cause of symptoms based on the patient's lifestyle data. For example, if the patient is a smoker, the analysis unit can prioritize analysis of symptoms that may be caused by smoking. Also, if the patient is not exercising enough, the analysis unit can analyze symptoms that may be caused by a lack of exercise. Furthermore, if the patient has specific eating habits, the analysis unit can analyze symptoms that may be caused by those eating habits. This makes it possible to more accurately estimate the cause of symptoms by utilizing the patient's lifestyle data.

[0053] The reception unit can take into account relevant environmental factors when entering symptoms based on the patient's geographical location information. For example, if the patient lives at high altitude, the reception unit can prompt the patient to enter symptoms specific to high altitudes first. If the patient lives in an urban area, the reception unit can prompt the patient to enter symptoms related to environmental factors specific to urban areas (such as air pollution). Furthermore, if the patient lives in a rural area, the reception unit can prompt the patient to enter symptoms related to environmental factors specific to rural areas (such as pesticides). In this way, by taking geographical location information into consideration, more relevant symptoms can be entered.

[0054] The suggestion unit can customize the medical institution suggestions based on the patient's past medical history. For example, if the patient has visited a specific medical department in the past, it can preferentially suggest medical institutions related to that department. Also, if the patient has visited a specific doctor in the past, it can suggest medical institutions to which that doctor belongs. Furthermore, if the patient has received a specific treatment in the past, it can suggest medical institutions that provide that treatment. In this way, it is possible to suggest more appropriate medical institutions by utilizing the patient's past medical history.

[0055] The reception unit can provide relevant information when entering symptoms based on the patient's social media activity. For example, the reception unit can prompt the patient to enter relevant symptoms based on health information the patient has shared on social media. The reception unit can also prompt the patient to enter relevant symptoms based on information obtained from health-related accounts the patient follows on social media. The reception unit can also prompt the patient to enter relevant symptoms based on information obtained from health-related groups the patient participates in on social media. This allows for more accurate symptom entry by providing relevant information based on social media activity.

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

[0057] Step 1: The reception unit is a section where the patient inputs their symptoms. The patient can input their symptoms using text input or voice input. For example, they can input their symptoms using a keyboard or by voice using a microphone. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses generative AI to analyze the input information and analyzes the information based on past medical data and medical literature. Step 3: The proposal unit proposes medical institutions based on the analysis results generated by the analysis unit. Using generative AI, the proposal unit proposes the most suitable medical institution and, for example, a specialist in neurology or neurosurgery. Step 4: The communication department communicates in real time with the medical institutions proposed by the proposal department. The communication department provides chat and video call functions, allowing patients to consult directly with the proposed medical institutions via chat or communicate in real time via video call.

[0058] (Example 2) A medical institution referral system according to an embodiment of the present invention helps patients accurately find the medical institution they need. This system allows patients to input their health problems and symptoms, and a generating AI analyzes the input information to recommend the most appropriate medical institution and specialist. Furthermore, real-time communication with the suggested medical institution prevents patients from being passed around from hospital to hospital. For example, if a patient inputs "I have a persistent headache," the generating AI analyzes the information and recommends specialists such as neurologists and neurosurgeons. Furthermore, patients can consult directly with the suggested medical institution via chat, streamlining the process for appointments and initial consultations. This system enables patients to receive the necessary medical care quickly and accurately and receive appropriate advice for their health problems. This document clearly states that patients can input their symptoms using text or voice input. It also describes that the generating AI uses data such as past medical records and medical literature to analyze. Chat and video call functions are provided as means of real-time communication. This allows the medical institution referral system to accurately find the medical institution they need and communicate with them in real time.

[0059] The medical facility referral system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a communication unit. The reception unit is a unit through which a patient inputs symptoms. When inputting symptoms, the patient can input them by text or voice. For example, the patient can input symptoms using a keyboard. Alternatively, the patient can input symptoms by voice using a microphone. The analysis unit is a unit that analyzes the information received by the reception unit. The analysis unit analyzes the input information using a generation AI. For example, the analysis unit analyzes the information based on past medical data and medical literature. The proposal unit is a unit that proposes medical facilities based on the analysis results generated by the analysis unit. The proposal unit proposes the most suitable medical facility using a generation AI. For example, the proposal unit proposes specialists such as neurologists and neurosurgeons based on the analysis results. The communication unit is a unit that communicates in real time with the medical facilities proposed by the proposal unit. The communication unit provides chat and video call functions. For example, the communication unit allows the patient to directly chat with the proposed medical facility. The communication unit can also communicate in real time with the medical facility via video call. As a result, the medical facility referral system according to the embodiment allows the patient to accurately find the medical facility they need and communicate with them in real time.

[0060] The reception unit can receive text input or voice input. For example, the patient can input symptoms into the reception unit using a keyboard. The reception unit can also allow the patient to input symptoms by voice using a microphone. For example, the patient can input "My headache continues" using a keyboard. The patient can also input "My headache continues" by voice using a microphone. This allows the patient to input symptoms using text input or voice input.

[0061] The analysis unit can analyze information based on past medical data or medical literature. The analysis unit, for example, analyzes information based on past medical data. For example, the analysis unit analyzes information based on electronic medical records or medical records. The analysis unit can also analyze information based on medical literature. For example, the analysis unit analyzes information based on academic papers or medical books. In this way, by performing analysis based on past medical data or medical literature, it becomes possible to suggest more accurate medical institutions.

[0062] The suggestion unit can suggest an appropriate medical institution based on the analysis results. For example, the suggestion unit suggests a specialist such as a neurologist or a neurosurgeon based on the analysis results. For example, the suggestion unit suggests the most appropriate medical institution based on the analysis results. This allows the patient to receive appropriate medical care by suggesting the most appropriate medical institution based on the analysis results.

[0063] The communication unit may provide a chat function or a video call function. For example, the communication unit may allow a patient to directly chat with a proposed medical institution. The communication unit may also communicate with the medical institution in real time through a video call. For example, the communication unit may allow a patient to chat with a proposed medical institution and smoothly complete procedures for making an appointment or an initial consultation. The communication unit may also communicate with the medical institution in real time through a video call and smoothly complete procedures for making an appointment or an initial consultation. This allows a patient to communicate with the medical institution in real time.

[0064] The reception unit can estimate the patient's emotions and adjust the symptom input interface based on the estimated patient emotions. For example, if the patient is feeling anxious, the reception unit can provide a simple and intuitive interface to minimize input steps. Alternatively, if the patient is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the patient is in a hurry, the reception unit can prioritize voice input to enable quick symptom input. This allows for more appropriate symptom input by adjusting the interface according to the patient's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input facial expression data of the patient captured with a camera into a generation AI, which can then analyze the facial expression data to estimate the patient's emotions. The reception unit can also input voice data of the patient captured with a microphone into a generation AI, which can then analyze the voice data to estimate the patient's emotions.

[0065] The reception unit can analyze the patient's past symptom input history and suggest an appropriate input method. For example, the reception unit can automatically display symptoms that the patient has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used during a specific time period based on the patient's past input history. This reduces the patient's input burden by suggesting the optimal input method based on the past input history. Analysis of the past symptom input history is performed using, for example, a generation AI. For example, the reception unit can input the patient's past symptom input history data into the generation AI, which can then analyze the data and suggest the optimal input method.

[0066] When inputting symptoms, the reception unit can filter the input content based on the patient's current health condition and lifestyle habits. For example, the reception unit filters out less relevant symptoms based on the patient's current health condition. The reception unit can also preferentially display more relevant symptoms based on the patient's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter out specific symptoms based on the patient's medical history. In this way, by filtering the input content based on the patient's health condition and lifestyle habits, more relevant symptoms can be input. The filtering process is performed, for example, using a generation AI. For example, the reception unit can input the patient's health condition and lifestyle habit data into the generation AI, which can then analyze the data and filter the input content.

[0067] The reception unit can estimate the patient's emotions and determine the priority of symptoms to be entered based on the estimated patient emotions. For example, if the patient is feeling anxious, the reception unit can prompt the patient to enter serious symptoms first. The reception unit can also prompt the patient to enter detailed symptoms first if the patient is relaxed. Furthermore, if the patient is in a hurry, the reception unit can prompt the patient to enter major symptoms first. This allows the priority of symptoms to be determined according to the patient's emotions, allowing important symptoms to be entered first. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The reception unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0068] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms based on the patient's geographical location information. For example, if the patient is in a specific area, the reception unit can prioritize inputting symptoms related to diseases prevalent in that area. Also, if the patient is traveling, the reception unit can prioritize inputting symptoms related to health risks at the patient's destination. Furthermore, if the patient is in a specific environment (such as a factory or farm), the reception unit can prioritize inputting symptoms related to that environment. In this way, by taking geographical location information into consideration, symptoms specific to the area can be prioritized. Geographical location information can be acquired using, for example, GPS data or address information. For example, the reception unit can input GPS data acquired from the patient's smartphone into the generation AI, and the generation AI can analyze the geographical location information and prioritize inputting highly relevant symptoms.

[0069] When entering symptoms, the reception unit can analyze the patient's social media activity and enter related symptoms. The reception unit can, for example, enter related symptoms based on health information shared by the patient on social media. The reception unit can also enter related symptoms based on information obtained from health-related accounts the patient follows on social media. The reception unit can also enter related symptoms based on information obtained from health-related groups the patient participates in on social media. This allows for more accurate symptom entry by entering related symptoms based on social media activity. Analysis of social media activity can be performed, for example, using a generation AI. For example, the reception unit can enter the patient's social media activity data into a generation AI, which can then analyze the data and enter related symptoms.

[0070] The analysis unit can estimate the patient's emotions and adjust the analysis algorithm based on the estimated patient emotions. For example, if the patient is feeling anxious, the analysis unit can quickly perform an analysis and provide results. Alternatively, if the patient is relaxed, the analysis unit can perform a detailed analysis and provide results. Furthermore, if the patient is in a hurry, the analysis unit can perform an analysis focusing on the patient's main symptoms and provide results. This allows for more appropriate analysis results to be provided by adjusting the analysis algorithm according to the patient's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input facial expression data of the patient captured with a camera into the generation AI, which can then analyze the facial expression data to estimate the patient's emotions. The analysis unit can also input voice data of the patient captured with a microphone into the generation AI, which can then analyze the voice data to estimate the patient's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, the analysis unit performs a detailed analysis for serious symptoms. The analysis unit can also perform a simplified analysis for minor symptoms. Furthermore, the analysis unit can determine the priority of the analysis according to the severity of the symptoms. This allows for efficient analysis by adjusting the level of detail of the analysis according to the severity of the symptoms. The severity of the symptoms is evaluated, for example, using a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which analyzes the data to evaluate the severity of the symptoms and adjust the level of detail of the analysis.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit can apply a respiratory-specific analysis algorithm to respiratory symptoms. The analysis unit can also apply a digestive-specific analysis algorithm to digestive symptoms. The analysis unit can also apply a neurological-specific analysis algorithm to nervous system symptoms. This allows for more specialized analysis by applying an analysis algorithm depending on the symptom category. The application of the analysis algorithm is performed using, for example, a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which then analyzes the data and applies an analysis algorithm depending on the symptom category.

[0073] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the patient is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results according to the patient's emotions, it is possible to provide results that are easier to understand. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The analysis unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom onset. For example, the analysis unit prioritizes analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have continued for a long period of time. Furthermore, the analysis unit can determine the priority of analysis according to the time of symptom onset. This enables a prompt response by determining the priority of analysis based on the time of symptom onset. The time of symptom onset is obtained, for example, using self-reported data or medical data. For example, the analysis unit can input the patient's self-reported data or medical data into the generation AI, which can then analyze the data and determine the priority of analysis based on the time of symptom onset.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms. This enables efficient analysis by adjusting the order of analysis based on the relevance of symptoms. The evaluation of the relevance of symptoms is performed, for example, using a generation AI. For example, the analysis unit can input the patient's symptom data into the generation AI, which analyzes the data to evaluate the relevance of symptoms and adjust the order of analysis.

[0076] The suggestion unit can estimate the patient's emotions and adjust the way the suggestion is expressed based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit can make a simple, highly visible suggestion. If the patient is relaxed, the suggestion unit can also make a suggestion that includes detailed information. Furthermore, if the patient is in a hurry, the suggestion unit can also make a suggestion that focuses on the main points. This enables more appropriate suggestions to be made by adjusting the way the suggestion is expressed depending on the patient's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the suggestion unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The suggestion unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0077] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the medical institution. For example, the suggestion unit provides detailed information to important medical institutions. The suggestion unit can also provide simplified information to general medical institutions. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the medical institution. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the medical institution. The importance of the medical institution is evaluated, for example, using a generation AI. For example, the suggestion unit can input data of the medical institution into the generation AI, which analyzes the data to evaluate the importance of the medical institution and adjust the level of detail of the proposal.

[0078] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the medical institution. For example, the proposal unit applies a specialized proposal algorithm to a specialized hospital. The proposal unit can also apply a general proposal algorithm to a general hospital. Furthermore, the proposal unit can also apply a proposal algorithm for clinics to a clinic. This makes it possible to make optimal proposals depending on the category of the medical institution. The proposal algorithm is applied, for example, using a generation AI. For example, the proposal unit can input data of the medical institution into the generation AI, which can analyze the data and apply a proposal algorithm depending on the category of the medical institution.

[0079] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestion based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit can make short, to-the-point suggestions. If the patient is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. If the patient is in a hurry, the suggestion unit can also make quick, concise suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the patient's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the suggestion unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The suggestion unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0080] When making a proposal, the suggestion unit can determine the priority of the proposal based on the location information of the medical institution. For example, the suggestion unit can prioritize proposing medical institutions close to the patient's current location. The suggestion unit can also prioritize proposing medical institutions along the patient's commute route. Furthermore, the suggestion unit can also prioritize proposing medical institutions close to the patient's home. In this way, by determining the priority of the proposal based on the location information of the medical institution, it is possible to propose the most suitable medical institution for the patient. The location information of the medical institution is obtained using, for example, GPS data or address information. For example, the suggestion unit can input GPS data obtained from the patient's smartphone into the generation AI, and the generation AI can analyze the location information and propose the most suitable medical institution.

[0081] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the medical institutions. For example, the suggestion unit prioritizes proposing medical institutions that are most relevant to the patient's symptoms. The suggestion unit can also suggest medical institutions that are highly relevant based on the patient's medical history. Furthermore, the suggestion unit can also suggest medical institutions that are highly relevant based on the patient's current health condition. In this way, by adjusting the order of proposals based on the relevance of the medical institutions, more appropriate medical institutions can be proposed. The relevance of the medical institutions is evaluated, for example, using a generation AI. For example, the suggestion unit can input the patient's symptom data and medical history data into the generation AI, which then analyzes the data to evaluate the relevance of the medical institutions and adjust the order of proposals.

[0082] The communication unit can estimate the patient's emotions and adjust the communication method based on the estimated patient's emotions. For example, if the patient is feeling anxious, the communication unit can communicate in a calm tone. If the patient is relaxed, the communication unit can also communicate in a friendly tone. Furthermore, if the patient is in a hurry, the communication unit can communicate quickly and concisely. This enables more appropriate communication by adjusting the communication method according to the patient's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the communication unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The communication unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0083] When communicating, the communication unit can select an appropriate method by referring to the patient's past communication history. For example, the communication unit can prioritize and suggest communication methods (chat, video call, etc.) that the patient has used favorably in the past. The communication unit can also communicate at the optimal timing based on the patient's past communication history. Furthermore, the communication unit can analyze the patient's past communication history and select the most effective method. This enables more effective communication by selecting the optimal method based on the past communication history. Analysis of the past communication history is performed, for example, using a generation AI. For example, the communication unit can input the patient's past communication history data into the generation AI, which can then analyze the data and select the optimal communication method.

[0084] The communication unit can customize the means of communication based on the patient's current health condition when communicating. For example, if the patient is feeling unwell, the communication unit prioritizes text chat to reduce the burden on the patient. If the patient is healthy, the communication unit can also suggest video calls for more detailed communication. Furthermore, if the patient is tired, the communication unit can also suggest communication via short messages. This enables more appropriate communication by customizing the means of communication according to the patient's health condition. The current health condition can be evaluated, for example, using self-reported data or medical data. For example, the communication unit can input the patient's self-reported data or medical data into the generation AI, which can then analyze the data to evaluate the patient's health condition and suggest the optimal means of communication.

[0085] The communication unit can estimate the patient's emotions and determine communication priorities based on the estimated patient emotions. For example, if the patient is feeling anxious, the communication unit can respond quickly. If the patient is relaxed, the communication unit can also respond within the normal response time. Furthermore, if the patient is in a hurry, the communication unit can also respond with top priority. This enables prompt and appropriate responses by determining communication priorities according to the patient's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the communication unit can input facial expression data of the patient captured with a camera into the generation AI, which can analyze the facial expression data to estimate the patient's emotions. The communication unit can also input voice data of the patient captured with a microphone into the generation AI, which can analyze the voice data to estimate the patient's emotions.

[0086] When communicating, the communication unit can select an appropriate communication method based on the patient's geographical location information. For example, if the patient is in a remote location, the communication unit can suggest a video call. The communication unit can also suggest face-to-face communication if the patient is nearby. Furthermore, the communication unit can also suggest a voice call if the patient is on the move. This makes it possible to select the optimal communication method for the patient by taking geographical location information into consideration. Geographical location information is acquired using, for example, GPS data or address information. For example, the communication unit can input GPS data acquired from the patient's smartphone into the generation AI, which can then analyze the location information and suggest the optimal communication method.

[0087] When communicating, the communication unit can analyze the patient's social media activity and suggest communication methods. For example, if the patient is active on social media, the communication unit can suggest a chat function. Also, if the patient shares health information on social media, the communication unit can suggest a video call. Furthermore, the communication unit can communicate in collaboration with medical professionals the patient follows on social media. This enables more effective communication by suggesting the optimal communication method based on social media activity. Analysis of social media activity is performed, for example, using generative AI. For example, the communication unit can input the patient's social media activity data into generative AI, which can analyze the data and suggest the optimal communication method. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and communication unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the patient's symptoms using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the most appropriate medical institution based on the analysis results. The communication unit is realized by the control unit 46A of the smart device 14 and provides a chat function and a video call function. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and communication unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the patient's symptoms by voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the most appropriate medical institution based on the analysis results. The communication unit is realized by the control unit 46A of the smart glasses 214 and provides a chat function and a video call function. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and communication unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the patient's symptoms by voice using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the most suitable medical institution based on the analysis results. The communication unit is realized by the control unit 46A of the headset-type terminal 314 and provides a chat function and a video call function. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and communication unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the patient's symptoms by voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the most suitable medical institution based on the analysis results. The communication unit is realized by the control unit 46A of the robot 414 and provides a chat function and a video call function.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can automatically generate related questions when symptoms are entered based on the patient's past medical history. For example, if a patient has complained of headaches in the past, the reception unit can automatically generate questions such as "How often do you have headaches?" or "When did your headaches start?" and present them to the patient. In addition, if the patient has used a specific medication in the past, questions about the effects and side effects of that medication can also be automatically generated. Furthermore, the reception unit can automatically generate questions about specific test or diagnostic results based on the patient's past medical history. This makes it possible to collect more detailed information by utilizing the patient's past medical history.

[0090] The analysis unit can estimate the cause of symptoms based on the patient's lifestyle data. For example, if the patient is a smoker, the analysis unit can prioritize analysis of symptoms that may be caused by smoking. Also, if the patient is not exercising enough, the analysis unit can analyze symptoms that may be caused by a lack of exercise. Furthermore, if the patient has specific eating habits, the analysis unit can analyze symptoms that may be caused by those eating habits. This makes it possible to more accurately estimate the cause of symptoms by utilizing the patient's lifestyle data.

[0091] The suggestion unit can estimate the patient's emotions and customize the medical institution suggestions based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit can prioritize suggesting medical institutions that will give the patient a sense of security. Also, if the patient is relaxed, the suggestion unit can suggest medical institutions that include detailed information. Furthermore, if the patient is in a hurry, the suggestion unit can prioritize suggesting medical institutions that can respond quickly. This allows for more appropriate suggestions to be made by customizing the medical institution suggestions according to the patient's emotions.

[0092] The communication unit can estimate the patient's emotions and adjust the tone of communication based on the estimated patient's emotions. For example, if the patient is feeling anxious, the communication unit can respond in a calm tone to reassure the patient. If the patient is relaxed, the communication unit can respond in a friendly tone. Furthermore, if the patient is in a hurry, the communication unit can respond in a quick and concise tone. This allows for more appropriate communication by adjusting the tone of communication according to the patient's emotions.

[0093] The reception unit can take into account relevant environmental factors when entering symptoms based on the patient's geographical location information. For example, if the patient lives at high altitude, the reception unit can prompt the patient to enter symptoms specific to high altitudes first. If the patient lives in an urban area, the reception unit can prompt the patient to enter symptoms related to environmental factors specific to urban areas (such as air pollution). Furthermore, if the patient lives in a rural area, the reception unit can prompt the patient to enter symptoms related to environmental factors specific to rural areas (such as pesticides). In this way, by taking geographical location information into consideration, more relevant symptoms can be entered.

[0094] The analysis unit can estimate the patient's emotions and adjust the analysis priority based on the estimated patient emotions. For example, if the patient is feeling anxious, the analysis unit can quickly perform an analysis and provide the results. If the patient is relaxed, the analysis unit can perform a detailed analysis and provide the results. Furthermore, if the patient is in a hurry, the analysis unit can perform an analysis focusing on the patient's main symptoms and provide the results. This allows the system to provide more appropriate analysis results by adjusting the analysis priority according to the patient's emotions.

[0095] The suggestion unit can customize the medical institution suggestions based on the patient's past medical history. For example, if the patient has visited a specific medical department in the past, it can preferentially suggest medical institutions related to that department. Also, if the patient has visited a specific doctor in the past, it can suggest medical institutions to which that doctor belongs. Furthermore, if the patient has received a specific treatment in the past, it can suggest medical institutions that provide that treatment. In this way, it is possible to suggest more appropriate medical institutions by utilizing the patient's past medical history.

[0096] The communication unit can estimate the patient's emotions and adjust the frequency of communication based on the estimated patient emotions. For example, if the patient is feeling anxious, the communication unit can contact the patient frequently to reassure them. If the patient is relaxed, the communication unit can contact the patient at a normal frequency. Furthermore, if the patient is in a hurry, the communication unit can contact the patient quickly to provide the necessary information. This allows for more appropriate responses by adjusting the frequency of communication according to the patient's emotions.

[0097] The reception unit can provide relevant information when entering symptoms based on the patient's social media activity. For example, the reception unit can prompt the patient to enter relevant symptoms based on health information the patient has shared on social media. The reception unit can also prompt the patient to enter relevant symptoms based on information obtained from health-related accounts the patient follows on social media. The reception unit can also prompt the patient to enter relevant symptoms based on information obtained from health-related groups the patient participates in on social media. This allows for more accurate symptom entry by providing relevant information based on social media activity.

[0098] The suggestion unit can estimate the patient's emotions and adjust the timing of the suggestion based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit can quickly suggest medical facilities to reassure the patient. If the patient is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the patient is in a hurry, the suggestion unit can make quick and concise suggestions. This allows for more appropriate suggestions to be made by adjusting the timing of the suggestion according to the patient's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit is a section where the patient inputs their symptoms. The patient can input their symptoms using text input or voice input. For example, they can input their symptoms using a keyboard or by voice using a microphone. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses generative AI to analyze the input information and analyzes the information based on past medical data and medical literature. Step 3: The proposal unit proposes medical institutions based on the analysis results generated by the analysis unit. Using generative AI, the proposal unit proposes the most suitable medical institution and, for example, a specialist in neurology or neurosurgery. Step 4: The communication department communicates in real time with the medical institutions proposed by the proposal department. The communication department provides chat and video call functions, allowing patients to consult directly with the proposed medical institutions via chat or communicate in real time via video call.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting symptoms; an analysis unit that analyzes the information received by the reception unit; a suggestion unit that suggests medical institutions based on the analysis results generated by the analysis unit; a communication unit that communicates in real time with the medical institution recommended by the recommendation unit. A system characterized by:

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

3. The analysis unit Analyze information based on past medical data or medical literature The system of claim 1 .

4. The proposal unit Recommend appropriate medical institutions based on analysis results The system of claim 1 .

5. The communication unit Offering chat or video calling features The system of claim 1 .

6. The reception unit Estimates the patient's emotions and adjusts the symptom input interface based on the estimated patient emotions. The system of claim 1 .

7. The reception unit Analyzes the patient's past symptom input history and suggests the appropriate input method The system of claim 1 .

8. The reception unit When entering symptoms, filter the input based on the patient's current health status and lifestyle habits The system of claim 1 .

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