system

Abstract

The system according to the embodiment aims to quickly identify the illness when a user feels unwell, and to suggest and book an appropriate hospital. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and a reservation unit. The reception unit inputs the user's physical condition. The analysis unit analyzes the information input by the reception unit. The diagnosis unit identifies an illness based on the information analyzed by the analysis unit. The suggestion unit suggests a recommended hospital based on the illness identified by the diagnosis unit. The reservation unit makes a reservation at the hospital suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to quickly identify the appropriate illness or select a hospital when they feel unwell.

[0005] The system according to the embodiment aims to quickly identify the illness when a user feels unwell, and to suggest and book an appropriate hospital. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and a reservation unit. The reception unit inputs the user's physical condition. The analysis unit analyzes the information input by the reception unit. The diagnosis unit identifies an illness based on the information analyzed by the analysis unit. The suggestion unit suggests a recommended hospital based on the illness identified by the diagnosis unit. The reservation unit makes a reservation at the hospital suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly identify the illness when a user feels unwell, and suggest and make a reservation at an appropriate hospital. [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 support system according to an embodiment of the present invention allows a user to input their physical condition and show a photo of the affected area to an AI doctor, and the AI ​​doctor then advises on possible illnesses, recommended medications, and treatment methods. The medical support system provides prompt and appropriate medical services by allowing the user to input their physical condition, analyze, diagnose, suggest, and schedule an appointment. For example, the user can input symptoms such as headache or fever into the medical support system. The medical support system can also show a photo of the affected area to the AI ​​doctor. This allows the AI ​​doctor to analyze the user's symptoms and photos and identify possible illnesses. For example, the AI ​​doctor can identify the possibility of a cold or influenza based on symptoms such as headache and fever. The medical support system then recommends medications and treatment methods based on the identified illness. For example, in the case of a cold, the system can recommend over-the-counter cold medicine and sufficient rest. In the case of influenza, the system can recommend a doctor's appointment. Furthermore, the medical support system recommends hospitals based on information such as consultation hours, availability, accessibility, and reviews. For example, the system can recommend hospitals close to the user's current location or hospitals with high reviews. This allows the user to find the best hospital for them. Finally, the medical support system can even make hospital reservations if necessary. For example, if a user wishes to make an appointment at a suggested hospital, the medical support system can handle the reservation procedure on their behalf. This allows the user to make a hospital appointment without any hassle. This allows the medical support system to receive prompt and appropriate advice when the user is feeling unwell, and to find the most suitable hospital and make an appointment. For example, if a user has cold or flu symptoms, the AI ​​doctor can advise on appropriate medication and treatment methods, suggest a nearby hospital, and even make an appointment, allowing the user to receive prompt and appropriate medical care.

[0029] The medical support system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and a reservation unit. The reception unit inputs the user's physical condition. The user's physical condition includes, but is not limited to, symptoms such as headache and fever. The reception unit, for example, allows the user to input symptoms such as headache and fever. The reception unit can also allow the user to show a photo of the affected area to the AI ​​doctor. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, analyzes the user's symptoms and photos. The analysis unit, for example, analyzes the user's symptoms and identifies possible illnesses. The analysis unit can also analyze the user's photos and evaluate the condition of the affected area. The diagnosis unit identifies illnesses based on the information analyzed by the analysis unit. The diagnosis unit, for example, identifies illnesses based on the analysis results. The diagnosis unit can, for example, identify the possibility of a cold or influenza based on symptoms such as headache and fever. The suggestion unit recommends hospitals based on the illness identified by the diagnosis unit. The suggestion unit recommends hospitals based on information such as consultation hours, availability of reservations, access, and reviews. The suggestion unit can suggest, for example, hospitals close to the user's current location or hospitals with high word-of-mouth reviews. The reservation unit makes a reservation at the hospital suggested by the suggestion unit. For example, if the user wishes to make a reservation at a suggested hospital, the reservation unit can handle the reservation procedure on the user's behalf. As a result, the medical support system according to the embodiment can provide prompt and appropriate medical services by inputting the user's physical condition and performing analysis, diagnosis, suggestions, and reservations.

[0030] The analysis unit can analyze the user's symptoms or photographs. The analysis unit, for example, analyzes the user's symptoms. For example, the analysis unit analyzes the user's symptoms, such as headache or fever, and identifies possible illnesses. The analysis unit can also analyze the user's photographs to evaluate the condition of the affected area. For example, the analysis unit can analyze the user's skin photographs to evaluate the skin condition. The analysis unit can also analyze the user's X-ray images to evaluate the condition of the bones. This enables a more accurate diagnosis by analyzing the user's symptoms and photographs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's symptom data and photograph data into the generation AI and have the generation AI execute the analysis results.

[0031] The diagnosis unit can identify a disease based on the analysis results. The diagnosis unit identifies a disease based on, for example, the analysis results. For example, the diagnosis unit can identify the possibility of a cold or influenza based on symptoms such as headache and fever. The diagnosis unit can also suggest an appropriate treatment to the user based on the analysis results. For example, in the case of a cold, the diagnosis unit can advise the user to take over-the-counter cold medicine and get plenty of rest. In the case of influenza, the diagnosis unit can also advise the user to see a doctor. In this way, by identifying a disease based on the analysis results, an appropriate treatment can be suggested. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the analysis results to a generation AI and cause the generation AI to identify the disease.

[0032] The suggestion unit can suggest hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. The suggestion unit can suggest recommended hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. For example, the suggestion unit can suggest hospitals close to the user's current location or hospitals with high reviews. The suggestion unit can also suggest hospitals that provide specialized treatment based on the user's symptoms. For example, if the user has a skin problem, the suggestion unit can suggest a dermatology specialty hospital. If the user has a bone problem, the suggestion unit can suggest an orthopedic specialty hospital. This allows the user to find the most suitable hospital by suggesting hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information such as consultation times, availability of reservations, accessibility, and reviews into the generation AI and cause the generation AI to suggest hospitals.

[0033] The reservation unit can make a reservation at the suggested hospital. For example, if a user wishes to make a reservation at the suggested hospital, the reservation unit can handle the reservation procedure on behalf of the user. For example, the reservation unit makes a hospital reservation through an online reservation system. The reservation unit can also handle telephone reservations on behalf of the user. For example, the reservation unit makes a reservation by calling the hospital on behalf of the user. The reservation unit can also confirm and change reservations. For example, the reservation unit checks the user's reservation status and changes the reservation as necessary. In this way, by making a reservation at the suggested hospital, the user can make a hospital reservation without any hassle. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's reservation information into the generation AI and have the generation AI execute the reservation procedure.

[0034] The reception unit can analyze the user's past health history and select an input method. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms to be input during a specific time period based on the user's past health history. For example, the reception unit can analyze the user's past health history and predict symptoms to be input during a specific time period. In this way, the optimal input method can be provided by analyzing the user's past health history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health history data into the generation AI and have the generation AI select an input method.

[0035] When inputting the user's physical condition, the reception unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is at work, the reception unit provides a question format that can be input in a short time. For example, if the user is relaxed, the reception unit provides detailed input options. Furthermore, if the user has a specific area of ​​interest (e.g., health management), the reception unit preferentially displays questions related to that area. For example, if the user is interested in health management, the reception unit preferentially displays questions related to health. This allows for filtering based on the user's living situation and areas of interest, thereby providing more appropriate input options. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0036] When inputting the user's physical condition, the reception unit can select an input means according to the user's input method. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. For example, if the user prefers text input, the reception unit can provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially. For example, if the user prefers image input, the reception unit can provide an option to upload an image. This improves user convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the input means.

[0037] When inputting the user's physical condition, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting physical condition information related to that area. For example, when the user is traveling, the reception unit prioritizes inputting physical condition information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting physical condition information related to the user's home. For example, when the user is at home, the reception unit prioritizes inputting physical condition information related to the user's home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to input highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting the user's physical condition. The reception unit, for example, automatically inputs physical condition information shared by the user on social media. For example, the reception unit analyzes the content of the user's social media posts and inputs related physical condition information. The reception unit can also input related physical condition information by referring to the activities of the user's friends on social media. For example, the reception unit inputs related physical condition information by referring to the activities of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting the user's physical condition. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the reception unit preferentially provides a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. For example, the reception unit analyzes the user's past feedback and improves the input method. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptom. For example, the analysis unit performs a detailed analysis when the symptom is severe. For example, the analysis unit performs a simplified analysis when the symptom is mild. The analysis unit can also gradually adjust the level of detail of the analysis according to the severity of the symptom. For example, the analysis unit gradually adjusts between a detailed analysis and a simplified analysis according to the severity of the symptom. This enables efficient analysis by adjusting the level of detail of the analysis based on the severity of the symptom. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit applies a dedicated analysis algorithm to respiratory system symptoms. For example, the analysis unit applies a dedicated analysis algorithm to digestive system symptoms. The analysis unit can also apply a dedicated analysis algorithm to nervous system symptoms. For example, the analysis unit applies a dedicated analysis algorithm to nervous system symptoms. In this way, by applying different analysis algorithms depending on the symptom category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit extracts specific patterns from the user's past analysis results and reflects them in the current analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] 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. For example, the analysis unit prioritizes analysis of symptoms that have continued for a long period of time. The analysis unit can also gradually adjust the priority of analysis according to the time of symptom onset. For example, the analysis unit determines which symptoms to prioritize analysis according to the time of symptom onset. This enables efficient analysis by determining the priority of analysis based on the time of symptom onset. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom onset data to the generation AI and have the generation AI determine the priority of analysis.

[0044] 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. For example, the analysis unit postpones analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis in stages according to the relevance of symptoms. For example, the analysis unit determines the order of analysis to be prioritized according to the relevance of symptoms. This enables efficient analysis by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terminology. For example, if the user is a layperson, the analysis unit provides analysis results that avoid technical terminology. The analysis unit can also gradually adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology according to the user's level of expertise. This allows for providing analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0046] The diagnostic unit can improve the accuracy of diagnosis by taking into account the interrelationships between symptoms during diagnosis. For example, when multiple symptoms are related, the diagnostic unit performs a comprehensive diagnosis of the symptoms. For example, the diagnostic unit analyzes the interrelationships between symptoms and corrects the diagnostic result. The diagnostic unit can also optimize the diagnostic algorithm based on the interrelationships between symptoms. For example, the diagnostic unit optimizes the diagnostic algorithm based on the interrelationships between symptoms. This improves the accuracy of diagnosis by taking the interrelationships between symptoms into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input symptom interrelation data into the generation AI and cause the generation AI to improve the accuracy of diagnosis.

[0047] The diagnostic unit can make a diagnosis taking into account the user's attribute information. The diagnostic unit makes a diagnosis taking into account, for example, the user's age and gender. For example, the diagnostic unit makes a diagnosis taking into account the user's lifestyle and occupation. The diagnostic unit can also make a diagnosis taking into account the user's medical history and family history. For example, the diagnostic unit makes a diagnosis taking into account the user's medical history and family history. This makes it possible to make a more personalized diagnosis by taking into account the user's attribute information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's attribute information data into the generation AI and have the generation AI perform the diagnosis.

[0048] The diagnostic unit can weight the diagnosis based on the frequency of symptom occurrence during diagnosis. For example, the diagnostic unit assigns a higher weight to a symptom that occurs frequently. For example, the diagnostic unit assigns a lower weight to a symptom that occurs rarely. The diagnostic unit can also adjust the diagnostic weight in stages according to the frequency of symptom occurrence. For example, the diagnostic unit adjusts the weight according to the frequency of symptom occurrence. This enables efficient diagnosis by weighting the diagnosis based on the frequency of symptom occurrence. Some or all of the above-described processing in the diagnostic unit may be performed using AI, for example, or may be performed without using AI. For example, the diagnostic unit can input symptom occurrence frequency data to a generation AI and have the generation AI perform diagnostic weighting.

[0049] The diagnostic unit can make a diagnosis taking into account the geographical distribution of symptoms. The diagnostic unit makes a diagnosis, for example, taking into account diseases that are prevalent in a particular region. For example, the diagnostic unit makes a diagnosis taking into account diseases specific to a region based on the user's place of residence. The diagnostic unit can also optimize a diagnostic algorithm based on the geographical distribution. For example, the diagnostic unit optimizes a diagnostic algorithm based on the geographical distribution. This makes it possible to make a diagnosis taking into account diseases specific to a region by taking into account the geographical distribution of symptoms. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input geographical distribution data of symptoms into a generation AI and have the generation AI perform a diagnosis.

[0050] The diagnostic unit can improve the accuracy of the diagnosis by referring to literature related to the symptoms during diagnosis. The diagnostic unit, for example, makes a diagnosis by referring to the latest medical literature. For example, the diagnostic unit makes a diagnosis by referring to past research results related to the symptoms. The diagnostic unit can also refer to related literature to reinforce the diagnostic results. For example, the diagnostic unit refers to related literature to reinforce the diagnostic results. By referring to literature related to the symptoms, the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input literature data related to the symptoms into the generating AI and cause the generating AI to improve the accuracy of the diagnosis.

[0051] The diagnostic unit can make a diagnosis taking into account the market value of the symptom. The diagnostic unit, for example, increases the accuracy of the diagnosis for symptoms that require expensive treatment. For example, the diagnostic unit makes a simplified diagnosis for symptoms with low market value. The diagnostic unit can also adjust the diagnostic algorithm according to the market value of the symptom. For example, the diagnostic unit adjusts the diagnostic algorithm according to the market value of the symptom. This enables efficient diagnosis by taking the market value of the symptom into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input market value data of the symptom into the generation AI and have the generation AI perform the diagnosis.

[0052] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital when making a proposal. For example, the suggestion unit provides detailed information for an important hospital. For example, the suggestion unit provides simplified information for a general hospital. The suggestion unit can also gradually adjust the level of detail of the proposal depending on the importance of the hospital. For example, the suggestion unit gradually adjusts between a detailed proposal and a simplified proposal depending on the importance of the hospital. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the hospital. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0053] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the hospital. For example, the proposal unit applies a dedicated proposal algorithm to a general hospital. For example, the proposal unit applies a dedicated proposal algorithm to a specialized hospital. The proposal unit can also apply a dedicated proposal algorithm to a clinic. For example, the proposal unit applies a dedicated proposal algorithm to a clinic. In this way, by applying different proposal algorithms depending on the category of the hospital, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input hospital category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0054] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. For example, the suggestion unit extracts a specific pattern from the user's past proposal results and reflects it in the current proposal. The suggestion unit can also analyze the user's past proposal results and optimize the proposal algorithm. For example, the suggestion unit analyzes the user's past proposal results and optimizes the proposal algorithm. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0055] When making a proposal, the proposal unit can determine the priority of the proposal based on the hospital's consultation schedule. For example, the proposal unit prioritizes the proposal when an examination is required urgently. For example, the proposal unit postpones the proposal if a delay in the examination is not a problem. The proposal unit can also gradually adjust the priority of the proposal according to the examination schedule. For example, the proposal unit determines which hospital to prioritize based on the examination schedule. This enables efficient proposals by determining the priority of the proposal based on the hospital's consultation schedule. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input hospital consultation schedule data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0056] The suggestion unit can adjust the order of suggestions based on the relevance of the hospitals when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant hospitals. For example, the suggestion unit postpones suggesting less relevant hospitals. The suggestion unit can also gradually adjust the order of suggestions based on the relevance of the hospitals. For example, the suggestion unit determines the order in which suggestions are prioritized based on the relevance of the hospitals. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the hospitals. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input hospital relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0057] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a medical professional, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user is a general public, the suggestion unit can provide a proposal that avoids technical terminology. The suggestion unit can also gradually adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit adjusts the use of technical terminology according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0058] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's past reservation history. For example, the reservation unit extracts specific patterns from the user's past reservation history and reflects them in the current reservation. The reservation unit can also analyze the user's past reservation history and optimize the reservation algorithm. For example, the reservation unit analyzes the user's past reservation history and optimizes the reservation algorithm. In this way, the optimal reservation method can be provided by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into a generation AI and have the generation AI select a reservation method.

[0059] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. For example, if the user is busy, the reservation unit provides a simple reservation method. For example, if the user is relaxed, the reservation unit provides a detailed reservation method. The reservation unit can also gradually adjust the reservation method according to the user's living situation. For example, the reservation unit provides the optimal reservation method according to the user's living situation. In this way, by customizing the reservation method based on the user's current living situation, the optimal reservation method for the user can be provided. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into a generation AI and have the generation AI customize the reservation method.

[0060] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. For example, the reservation unit preferentially provides a specific reservation method based on the user's past feedback. The reservation unit can also analyze the user's past feedback and improve the reservation method. For example, the reservation unit analyzes the user's past feedback and improves the reservation method. In this way, the reservation method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input user feedback data into a generation AI and have the generation AI improve the reservation method.

[0061] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's current location. For example, the reservation unit prioritizes reserving nearby hospitals based on the user's geographical location information. The reservation unit can also analyze the user's geographical location information and select the optimal reservation method. For example, the reservation unit analyzes the user's geographical location information and selects the optimal reservation method. This makes it possible to provide the optimal reservation method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information data into the generation AI and have the generation AI select the reservation method.

[0062] At the time of reservation, the reservation unit can analyze the user's social media activity and suggest a reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's social media activity. For example, the reservation unit analyzes the content of the user's social media posts and suggests a related reservation method. The reservation unit can also suggest the optimal reservation method by referring to the activity of the user's friends on social media. For example, the reservation unit suggests the optimal reservation method by referring to the activity of the user's friends on social media. In this way, the optimal reservation method can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of a reservation method.

[0063] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. For example, the reservation unit prioritizes providing a specific reservation method based on the user's past feedback. The reservation unit can also analyze the user's past feedback and customize the reservation method. For example, the reservation unit analyzes the user's past feedback and customizes the reservation method. In this way, the reservation method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's feedback data into a generation AI and have the generation AI customize the reservation method.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit not only inputs the user's physical condition, but also automatically acquires the user's past medical history and provides it to the analysis unit. For example, the reception unit acquires information about the user's past diagnostic results and prescribed medications. The reception unit can also prioritize displaying information related to the user's current symptoms based on the user's past medical history. This allows for more accurate analysis and diagnosis by taking the user's past medical history into consideration. Furthermore, the reception unit can evaluate the user's risk of a specific disease based on the user's past medical history and suggest necessary tests and preventive measures.

[0066] The analysis unit not only analyzes the user's symptoms or photos, but also acquires the user's lifestyle data and reflects it in the analysis. For example, the analysis unit acquires the user's diet and exercise data and identifies the cause of the symptoms based on this data. The analysis unit can also analyze the user's sleep data and evaluate the impact of lack of sleep on the symptoms. This allows for a more comprehensive analysis by taking the user's lifestyle data into consideration. Furthermore, the analysis unit can analyze the user's stress level and evaluate the impact of stress on the symptoms.

[0067] The diagnostic unit not only identifies diseases based on the analysis results, but also acquires the user's genetic information and reflects it in the diagnosis. For example, the diagnostic unit evaluates specific genetic risks based on the user's genetic information. The diagnostic unit can also take into account the user's family history and evaluate the risk of diseases common to the family. This allows for more personalized diagnosis by taking into account the user's genetic information. Furthermore, the diagnostic unit can predict the effectiveness of specific treatments and suggest optimal treatments based on the user's genetic information.

[0068] The suggestion unit not only suggests hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews, but can also make suggestions taking into account the user's insurance information. For example, the suggestion unit may preferentially suggest hospitals that are covered by the user's insurance. The suggestion unit can also suggest optimal treatments based on the user's insurance plan. This makes it possible to make suggestions that reduce the user's financial burden by taking into account the user's insurance information. Furthermore, the suggestion unit can also provide information on treatments not covered by insurance based on the user's insurance information, allowing the user to understand their options.

[0069] The reservation unit not only makes the suggested hospital appointment, but also acquires the user's calendar information and can suggest the optimal appointment date and time. For example, the reservation unit automatically detects available time slots based on the user's calendar information. The reservation unit can also suggest the optimal appointment date and time based on the user's schedule. This improves the convenience of appointments by taking the user's calendar information into consideration. Furthermore, the reservation unit can set reminders based on the user's calendar information to notify the user so that they do not forget the appointment date and time.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit inputs the user's physical condition. The user's physical condition may include, but is not limited to, symptoms such as headache and fever. The user can input symptoms such as headache and fever, and can also show the AI ​​doctor a photo of the affected area. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the user's symptoms and photos to identify possible illnesses. It can also evaluate the condition of the affected area. Step 3: The diagnosis unit identifies illnesses based on the information analyzed by the analysis unit. For example, it can identify the possibility of a cold or flu based on symptoms such as headache and fever. Step 4: The suggestion unit suggests recommended hospitals based on the illness identified by the diagnosis unit. Based on information such as consultation times, availability of reservations, access, and reviews, it can suggest hospitals close to the user's current location or hospitals with high reviews. Step 5: The reservation unit makes a reservation at the hospital suggested by the suggestion unit. If the user wishes to make a reservation at the suggested hospital, the reservation procedure can be carried out on behalf of the user.

[0072] (Example 2) A medical support system according to an embodiment of the present invention allows a user to input their physical condition and show a photo of the affected area to an AI doctor, and the AI ​​doctor then advises on possible illnesses, recommended medications, and treatment methods. The medical support system provides prompt and appropriate medical services by allowing the user to input their physical condition, analyze, diagnose, suggest, and schedule an appointment. For example, the user can input symptoms such as headache or fever into the medical support system. The medical support system can also show a photo of the affected area to the AI ​​doctor. This allows the AI ​​doctor to analyze the user's symptoms and photos and identify possible illnesses. For example, the AI ​​doctor can identify the possibility of a cold or influenza based on symptoms such as headache and fever. The medical support system then recommends medications and treatment methods based on the identified illness. For example, in the case of a cold, the system can recommend over-the-counter cold medicine and sufficient rest. In the case of influenza, the system can recommend a doctor's appointment. Furthermore, the medical support system recommends hospitals based on information such as consultation hours, availability, accessibility, and reviews. For example, the system can recommend hospitals close to the user's current location or hospitals with high reviews. This allows the user to find the best hospital for them. Finally, the medical support system can even make hospital reservations if necessary. For example, if a user wishes to make an appointment at a suggested hospital, the medical support system can handle the reservation procedure on their behalf. This allows the user to make a hospital appointment without any hassle. This allows the medical support system to receive prompt and appropriate advice when the user is feeling unwell, and to find the most suitable hospital and make an appointment. For example, if a user has cold or flu symptoms, the AI ​​doctor can advise on appropriate medication and treatment methods, suggest a nearby hospital, and even make an appointment, allowing the user to receive prompt and appropriate medical care.

[0073] The medical support system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and a reservation unit. The reception unit inputs the user's physical condition. The user's physical condition includes, but is not limited to, symptoms such as headache and fever. The reception unit, for example, allows the user to input symptoms such as headache and fever. The reception unit can also allow the user to show a photo of the affected area to the AI ​​doctor. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, analyzes the user's symptoms and photos. The analysis unit, for example, analyzes the user's symptoms and identifies possible illnesses. The analysis unit can also analyze the user's photos and evaluate the condition of the affected area. The diagnosis unit identifies illnesses based on the information analyzed by the analysis unit. The diagnosis unit, for example, identifies illnesses based on the analysis results. The diagnosis unit can, for example, identify the possibility of a cold or influenza based on symptoms such as headache and fever. The suggestion unit recommends hospitals based on the illness identified by the diagnosis unit. The suggestion unit recommends hospitals based on information such as consultation hours, availability of reservations, access, and reviews. The suggestion unit can suggest, for example, hospitals close to the user's current location or hospitals with high word-of-mouth reviews. The reservation unit makes a reservation at the hospital suggested by the suggestion unit. For example, if the user wishes to make a reservation at a suggested hospital, the reservation unit can handle the reservation procedure on the user's behalf. As a result, the medical support system according to the embodiment can provide prompt and appropriate medical services by inputting the user's physical condition and performing analysis, diagnosis, suggestions, and reservations.

[0074] The analysis unit can analyze the user's symptoms or photographs. The analysis unit, for example, analyzes the user's symptoms. For example, the analysis unit analyzes the user's symptoms, such as headache or fever, and identifies possible illnesses. The analysis unit can also analyze the user's photographs to evaluate the condition of the affected area. For example, the analysis unit can analyze the user's skin photographs to evaluate the skin condition. The analysis unit can also analyze the user's X-ray images to evaluate the condition of the bones. This enables a more accurate diagnosis by analyzing the user's symptoms and photographs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's symptom data and photograph data into the generation AI and have the generation AI execute the analysis results.

[0075] The diagnosis unit can identify a disease based on the analysis results. The diagnosis unit identifies a disease based on, for example, the analysis results. For example, the diagnosis unit can identify the possibility of a cold or influenza based on symptoms such as headache and fever. The diagnosis unit can also suggest an appropriate treatment to the user based on the analysis results. For example, in the case of a cold, the diagnosis unit can advise the user to take over-the-counter cold medicine and get plenty of rest. In the case of influenza, the diagnosis unit can also advise the user to see a doctor. In this way, by identifying a disease based on the analysis results, an appropriate treatment can be suggested. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the analysis results to a generation AI and cause the generation AI to identify the disease.

[0076] The suggestion unit can suggest hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. The suggestion unit can suggest recommended hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. For example, the suggestion unit can suggest hospitals close to the user's current location or hospitals with high reviews. The suggestion unit can also suggest hospitals that provide specialized treatment based on the user's symptoms. For example, if the user has a skin problem, the suggestion unit can suggest a dermatology specialty hospital. If the user has a bone problem, the suggestion unit can suggest an orthopedic specialty hospital. This allows the user to find the most suitable hospital by suggesting hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information such as consultation times, availability of reservations, accessibility, and reviews into the generation AI and cause the generation AI to suggest hospitals.

[0077] The reservation unit can make a reservation at the suggested hospital. For example, if a user wishes to make a reservation at the suggested hospital, the reservation unit can handle the reservation procedure on behalf of the user. For example, the reservation unit makes a hospital reservation through an online reservation system. The reservation unit can also handle telephone reservations on behalf of the user. For example, the reservation unit makes a reservation by calling the hospital on behalf of the user. The reservation unit can also confirm and change reservations. For example, the reservation unit checks the user's reservation status and changes the reservation as necessary. In this way, by making a reservation at the suggested hospital, the user can make a hospital reservation without any hassle. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's reservation information into the generation AI and have the generation AI execute the reservation procedure.

[0078] The reception unit can estimate the user's emotions and adjust the timing of inputting the user's physical condition information based on the emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input the user's physical condition information during a time when the user can relax. For example, if the user is relaxed, the reception unit prompts the user to input the user's physical condition information immediately. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input the user's physical condition information in the form of a simple question. For example, if the user is in a hurry, the reception unit provides a question format that can be input in a short time. This allows the user to adjust the timing of inputting the user's physical condition information according to the user's emotions, thereby prompting the user to input the information at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0079] The reception unit can analyze the user's past health history and select an input method. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms to be input during a specific time period based on the user's past health history. For example, the reception unit can analyze the user's past health history and predict symptoms to be input during a specific time period. In this way, the optimal input method can be provided by analyzing the user's past health history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health history data into the generation AI and have the generation AI select an input method.

[0080] When inputting the user's physical condition, the reception unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is at work, the reception unit provides a question format that can be input in a short time. For example, if the user is relaxed, the reception unit provides detailed input options. Furthermore, if the user has a specific area of ​​interest (e.g., health management), the reception unit preferentially displays questions related to that area. For example, if the user is interested in health management, the reception unit preferentially displays questions related to health. This allows for filtering based on the user's living situation and areas of interest, thereby providing more appropriate input options. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0081] When inputting the user's physical condition, the reception unit can select an input means according to the user's input method. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. For example, if the user prefers text input, the reception unit can provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially. For example, if the user prefers image input, the reception unit can provide an option to upload an image. This improves user convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the input means.

[0082] The reception unit can estimate the user's emotions and determine the priority of the physical condition information to be input based on the emotions. For example, when the user is stressed, the reception unit prioritizes input of important physical condition information. For example, when the user is relaxed, the reception unit inputs detailed physical condition information. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of simple physical condition information. For example, when the user is in a hurry, the reception unit prioritizes input of physical condition information that can be input in a short time. In this way, by determining the priority of physical condition information according to the user's emotions, important information can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0083] When inputting the user's physical condition, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting physical condition information related to that area. For example, when the user is traveling, the reception unit prioritizes inputting physical condition information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting physical condition information related to the user's home. For example, when the user is at home, the reception unit prioritizes inputting physical condition information related to the user's home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to input highly relevant information.

[0084] The reception unit can analyze the user's social media activity and input related information when inputting the user's physical condition. The reception unit, for example, automatically inputs physical condition information shared by the user on social media. For example, the reception unit analyzes the content of the user's social media posts and inputs related physical condition information. The reception unit can also input related physical condition information by referring to the activities of the user's friends on social media. For example, the reception unit inputs related physical condition information by referring to the activities of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0085] The reception unit can customize the input method by reflecting the user's past feedback when inputting the user's physical condition. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the reception unit preferentially provides a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. For example, the reception unit analyzes the user's past feedback and improves the input method. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, if the user is in a hurry, the analysis unit provides analysis results that can be understood in a short amount of time. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the severity of the symptom. For example, the analysis unit performs a detailed analysis when the symptom is severe. For example, the analysis unit performs a simplified analysis when the symptom is mild. The analysis unit can also gradually adjust the level of detail of the analysis according to the severity of the symptom. For example, the analysis unit gradually adjusts between a detailed analysis and a simplified analysis according to the severity of the symptom. This enables efficient analysis by adjusting the level of detail of the analysis based on the severity of the symptom. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit applies a dedicated analysis algorithm to respiratory system symptoms. For example, the analysis unit applies a dedicated analysis algorithm to digestive system symptoms. The analysis unit can also apply a dedicated analysis algorithm to nervous system symptoms. For example, the analysis unit applies a dedicated analysis algorithm to nervous system symptoms. In this way, by applying different analysis algorithms depending on the symptom category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit extracts specific patterns from the user's past analysis results and reflects them in the current analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides the analysis result in a visually easy-to-understand format. This allows the analysis length to be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] 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. For example, the analysis unit prioritizes analysis of symptoms that have continued for a long period of time. The analysis unit can also gradually adjust the priority of analysis according to the time of symptom onset. For example, the analysis unit determines which symptoms to prioritize analysis according to the time of symptom onset. This enables efficient analysis by determining the priority of analysis based on the time of symptom onset. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom onset data to the generation AI and have the generation AI determine the priority of analysis.

[0092] 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. For example, the analysis unit postpones analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis in stages according to the relevance of symptoms. For example, the analysis unit determines the order of analysis to be prioritized according to the relevance of symptoms. This enables efficient analysis by adjusting the order of analysis based on the relevance of symptoms. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terminology. For example, if the user is a layperson, the analysis unit provides analysis results that avoid technical terminology. The analysis unit can also gradually adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology according to the user's level of expertise. This allows for providing analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0094] The diagnostic unit can estimate the user's emotions and adjust the diagnostic criteria based on the emotions. For example, if the user is nervous, the diagnostic unit provides simple, highly visible diagnostic criteria. For example, if the user is relaxed, the diagnostic unit provides detailed diagnostic criteria. Furthermore, if the user is in a hurry, the diagnostic unit can provide diagnostic criteria that focus on the key points. For example, if the user is in a hurry, the diagnostic unit provides diagnostic criteria that can be understood in a short time. This enables a more appropriate diagnosis by adjusting the diagnostic criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's emotion data into the generative AI and cause the generative AI to estimate the emotion.

[0095] The diagnostic unit can improve the accuracy of diagnosis by taking into account the interrelationships between symptoms during diagnosis. For example, when multiple symptoms are related, the diagnostic unit performs a comprehensive diagnosis of the symptoms. For example, the diagnostic unit analyzes the interrelationships between symptoms and corrects the diagnostic result. The diagnostic unit can also optimize the diagnostic algorithm based on the interrelationships between symptoms. For example, the diagnostic unit optimizes the diagnostic algorithm based on the interrelationships between symptoms. This improves the accuracy of diagnosis by taking the interrelationships between symptoms into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input symptom interrelation data into the generation AI and cause the generation AI to improve the accuracy of diagnosis.

[0096] The diagnostic unit can make a diagnosis taking into account the user's attribute information. The diagnostic unit makes a diagnosis taking into account, for example, the user's age and gender. For example, the diagnostic unit makes a diagnosis taking into account the user's lifestyle and occupation. The diagnostic unit can also make a diagnosis taking into account the user's medical history and family history. For example, the diagnostic unit makes a diagnosis taking into account the user's medical history and family history. This makes it possible to make a more personalized diagnosis by taking into account the user's attribute information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's attribute information data into the generation AI and have the generation AI perform the diagnosis.

[0097] The diagnostic unit can weight the diagnosis based on the frequency of symptom occurrence during diagnosis. For example, the diagnostic unit assigns a higher weight to a symptom that occurs frequently. For example, the diagnostic unit assigns a lower weight to a symptom that occurs rarely. The diagnostic unit can also adjust the diagnostic weight in stages according to the frequency of symptom occurrence. For example, the diagnostic unit adjusts the weight according to the frequency of symptom occurrence. This enables efficient diagnosis by weighting the diagnosis based on the frequency of symptom occurrence. Some or all of the above-described processing in the diagnostic unit may be performed using AI, for example, or may be performed without using AI. For example, the diagnostic unit can input symptom occurrence frequency data to a generation AI and have the generation AI perform diagnostic weighting.

[0098] The diagnostic unit can estimate the user's emotions and adjust the order in which diagnostic results are displayed based on the emotions. For example, if the user is nervous, the diagnostic unit prioritizes displaying important diagnostic results. For example, if the user is relaxed, the diagnostic unit displays detailed diagnostic results in an orderly manner. Furthermore, if the user is in a hurry, the diagnostic unit can prioritize displaying diagnostic results that focus on the key points. For example, if the user is in a hurry, the diagnostic unit prioritizes displaying diagnostic results that can be understood in a short time. This allows for adjusting the order in which diagnostic results are displayed according to the user's emotions, thereby providing diagnostic results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0099] The diagnostic unit can make a diagnosis taking into account the geographical distribution of symptoms. The diagnostic unit makes a diagnosis, for example, taking into account diseases that are prevalent in a particular region. For example, the diagnostic unit makes a diagnosis taking into account diseases specific to a region based on the user's place of residence. The diagnostic unit can also optimize a diagnostic algorithm based on the geographical distribution. For example, the diagnostic unit optimizes a diagnostic algorithm based on the geographical distribution. This makes it possible to make a diagnosis taking into account diseases specific to a region by taking into account the geographical distribution of symptoms. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input geographical distribution data of symptoms into a generation AI and have the generation AI perform a diagnosis.

[0100] The diagnostic unit can improve the accuracy of the diagnosis by referring to literature related to the symptoms during diagnosis. The diagnostic unit, for example, makes a diagnosis by referring to the latest medical literature. For example, the diagnostic unit makes a diagnosis by referring to past research results related to the symptoms. The diagnostic unit can also refer to related literature to reinforce the diagnostic results. For example, the diagnostic unit refers to related literature to reinforce the diagnostic results. By referring to literature related to the symptoms, the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input literature data related to the symptoms into the generating AI and cause the generating AI to improve the accuracy of the diagnosis.

[0101] The diagnostic unit can make a diagnosis taking into account the market value of the symptom. The diagnostic unit, for example, increases the accuracy of the diagnosis for symptoms that require expensive treatment. For example, the diagnostic unit makes a simplified diagnosis for symptoms with low market value. The diagnostic unit can also adjust the diagnostic algorithm according to the market value of the symptom. For example, the diagnostic unit adjusts the diagnostic algorithm according to the market value of the symptom. This enables efficient diagnosis by taking the market value of the symptom into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input market value data of the symptom into the generation AI and have the generation AI perform the diagnosis.

[0102] The suggestion unit can estimate the user's emotions and adjust the display method of the suggestions based on the emotions. For example, if the user is nervous, the suggestion unit provides a simple, highly visible display method. For example, if the user is relaxed, the suggestion unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the suggestion unit provides a display method that can be understood in a short time. This allows the suggestion display method to be adjusted according to the user's emotions, thereby providing suggestions that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, for example, or without an AI. For example, the suggestion unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0103] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital when making a proposal. For example, the suggestion unit provides detailed information for an important hospital. For example, the suggestion unit provides simplified information for a general hospital. The suggestion unit can also gradually adjust the level of detail of the proposal depending on the importance of the hospital. For example, the suggestion unit gradually adjusts between a detailed proposal and a simplified proposal depending on the importance of the hospital. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the hospital. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0104] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the hospital. For example, the proposal unit applies a dedicated proposal algorithm to a general hospital. For example, the proposal unit applies a dedicated proposal algorithm to a specialized hospital. The proposal unit can also apply a dedicated proposal algorithm to a clinic. For example, the proposal unit applies a dedicated proposal algorithm to a clinic. In this way, by applying different proposal algorithms depending on the category of the hospital, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input hospital category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0105] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. For example, the suggestion unit extracts a specific pattern from the user's past proposal results and reflects it in the current proposal. The suggestion unit can also analyze the user's past proposal results and optimize the proposal algorithm. For example, the suggestion unit analyzes the user's past proposal results and optimizes the proposal algorithm. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0106] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the emotions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. The suggestion unit can also provide visually stimulating suggestions if the user is excited. For example, if the user is excited, the suggestion unit provides suggestions in a visually easy-to-understand format. This allows the length of the suggestions to be adjusted according to the user's emotions, thereby providing optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0107] When making a proposal, the proposal unit can determine the priority of the proposal based on the hospital's consultation schedule. For example, the proposal unit prioritizes the proposal when an examination is required urgently. For example, the proposal unit postpones the proposal if a delay in the examination is not a problem. The proposal unit can also gradually adjust the priority of the proposal according to the examination schedule. For example, the proposal unit determines which hospital to prioritize based on the examination schedule. This enables efficient proposals by determining the priority of the proposal based on the hospital's consultation schedule. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input hospital consultation schedule data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0108] The suggestion unit can adjust the order of suggestions based on the relevance of the hospitals when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant hospitals. For example, the suggestion unit postpones suggesting less relevant hospitals. The suggestion unit can also gradually adjust the order of suggestions based on the relevance of the hospitals. For example, the suggestion unit determines the order in which suggestions are prioritized based on the relevance of the hospitals. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the hospitals. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input hospital relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0109] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a medical professional, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user is a general public, the suggestion unit can provide a proposal that avoids technical terminology. The suggestion unit can also gradually adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit adjusts the use of technical terminology according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0110] The reservation unit can estimate the user's emotions and adjust the reservation method based on the emotions. For example, if the user is nervous, the reservation unit provides a simple and highly visible reservation method. For example, if the user is relaxed, the reservation unit provides a detailed reservation method. Furthermore, if the user is in a hurry, the reservation unit can also provide a reservation method that focuses on the key points. For example, if the user is in a hurry, the reservation unit provides a method that allows the user to make a reservation in a short time. This allows the reservation method to be adjusted according to the user's emotions, thereby providing the optimal reservation method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's past reservation history. For example, the reservation unit extracts specific patterns from the user's past reservation history and reflects them in the current reservation. The reservation unit can also analyze the user's past reservation history and optimize the reservation algorithm. For example, the reservation unit analyzes the user's past reservation history and optimizes the reservation algorithm. In this way, the optimal reservation method can be provided by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into a generation AI and have the generation AI select a reservation method.

[0112] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. For example, if the user is busy, the reservation unit provides a simple reservation method. For example, if the user is relaxed, the reservation unit provides a detailed reservation method. The reservation unit can also gradually adjust the reservation method according to the user's living situation. For example, the reservation unit provides the optimal reservation method according to the user's living situation. In this way, by customizing the reservation method based on the user's current living situation, the optimal reservation method for the user can be provided. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into a generation AI and have the generation AI customize the reservation method.

[0113] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. For example, the reservation unit preferentially provides a specific reservation method based on the user's past feedback. The reservation unit can also analyze the user's past feedback and improve the reservation method. For example, the reservation unit analyzes the user's past feedback and improves the reservation method. In this way, the reservation method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input user feedback data into a generation AI and have the generation AI improve the reservation method.

[0114] The reservation unit can estimate the user's emotions and prioritize reservations based on the emotions. For example, if the user is nervous, the reservation unit prioritizes important reservations. For example, if the user is relaxed, the reservation unit makes detailed reservations. Furthermore, if the user is in a hurry, the reservation unit can prioritize reservations that focus on the essentials. For example, the reservation unit provides a method for making reservations quickly when the user is in a hurry. This allows important reservations to be prioritized by prioritizing reservations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0115] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's current location. For example, the reservation unit prioritizes reserving nearby hospitals based on the user's geographical location information. The reservation unit can also analyze the user's geographical location information and select the optimal reservation method. For example, the reservation unit analyzes the user's geographical location information and selects the optimal reservation method. This makes it possible to provide the optimal reservation method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information data into the generation AI and have the generation AI select the reservation method.

[0116] At the time of reservation, the reservation unit can analyze the user's social media activity and suggest a reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's social media activity. For example, the reservation unit analyzes the content of the user's social media posts and suggests a related reservation method. The reservation unit can also suggest the optimal reservation method by referring to the activity of the user's friends on social media. For example, the reservation unit suggests the optimal reservation method by referring to the activity of the user's friends on social media. In this way, the optimal reservation method can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of a reservation method.

[0117] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, suggests the optimal reservation method based on the user's past feedback. For example, the reservation unit prioritizes providing a specific reservation method based on the user's past feedback. The reservation unit can also analyze the user's past feedback and customize the reservation method. For example, the reservation unit analyzes the user's past feedback and customizes the reservation method. In this way, the reservation method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's feedback data into a generation AI and have the generation AI customize the reservation method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, diagnosis unit, suggestion unit, and reservation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input symptoms such as headache and fever. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's symptoms and photos. The diagnosis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies illnesses based on the analysis results. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and suggests recommended hospitals based on information such as consultation hours and word-of-mouth reviews. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14, and handles reservation procedures at the suggested hospitals. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, diagnosis unit, suggestion unit, and reservation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to vocally input symptoms such as headache or fever. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's symptoms and photos. The diagnosis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies illnesses based on the analysis results. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests recommended hospitals based on information such as consultation hours and word-of-mouth reviews. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214, and handles reservation procedures at the suggested hospitals. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, diagnosis unit, suggestion unit, and reservation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to vocally input symptoms such as headache or fever. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's symptoms and photos. The diagnosis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies illnesses based on the analysis results. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and suggests recommended hospitals based on information such as consultation hours and word-of-mouth reviews. The reservation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and handles reservation procedures at the suggested hospitals. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, diagnosis unit, suggestion unit, and reservation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to vocally input symptoms such as headache or fever. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's symptoms and photos. The diagnosis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies illnesses based on the analysis results. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests recommended hospitals based on information such as consultation hours and word-of-mouth reviews. The reservation unit is realized, for example, by the control unit 46A of the robot 414, and handles reservation procedures at the suggested hospitals.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit not only inputs the user's physical condition, but also automatically acquires the user's past medical history and provides it to the analysis unit. For example, the reception unit acquires information about the user's past diagnostic results and prescribed medications. The reception unit can also prioritize displaying information related to the user's current symptoms based on the user's past medical history. This allows for more accurate analysis and diagnosis by taking the user's past medical history into consideration. Furthermore, the reception unit can evaluate the user's risk of a specific disease based on the user's past medical history and suggest necessary tests and preventive measures.

[0120] The analysis unit not only analyzes the user's symptoms or photos, but also acquires the user's lifestyle data and reflects it in the analysis. For example, the analysis unit acquires the user's diet and exercise data and identifies the cause of the symptoms based on this data. The analysis unit can also analyze the user's sleep data and evaluate the impact of lack of sleep on the symptoms. This allows for a more comprehensive analysis by taking the user's lifestyle data into consideration. Furthermore, the analysis unit can analyze the user's stress level and evaluate the impact of stress on the symptoms.

[0121] The diagnostic unit not only identifies diseases based on the analysis results, but also acquires the user's genetic information and reflects it in the diagnosis. For example, the diagnostic unit evaluates specific genetic risks based on the user's genetic information. The diagnostic unit can also take into account the user's family history and evaluate the risk of diseases common to the family. This allows for more personalized diagnosis by taking into account the user's genetic information. Furthermore, the diagnostic unit can predict the effectiveness of specific treatments and suggest optimal treatments based on the user's genetic information.

[0122] The suggestion unit not only suggests hospitals based on information such as consultation times, availability of reservations, accessibility, and reviews, but can also make suggestions taking into account the user's insurance information. For example, the suggestion unit may preferentially suggest hospitals that are covered by the user's insurance. The suggestion unit can also suggest optimal treatments based on the user's insurance plan. This makes it possible to make suggestions that reduce the user's financial burden by taking into account the user's insurance information. Furthermore, the suggestion unit can also provide information on treatments not covered by insurance based on the user's insurance information, allowing the user to understand their options.

[0123] The reservation unit not only makes the suggested hospital appointment, but also acquires the user's calendar information and can suggest the optimal appointment date and time. For example, the reservation unit automatically detects available time slots based on the user's calendar information. The reservation unit can also suggest the optimal appointment date and time based on the user's schedule. This improves the convenience of appointments by taking the user's calendar information into consideration. Furthermore, the reservation unit can set reminders based on the user's calendar information to notify the user so that they do not forget the appointment date and time.

[0124] The reception unit not only estimates the user's emotions and adjusts the timing of inputting the physical condition based on the emotions, but also customizes the input interface according to the user's emotions. For example, the reception unit provides a simple and intuitive interface when the user is feeling stressed. The reception unit can also provide detailed input options when the user is relaxed. This improves input convenience by customizing the input interface according to the user's emotions. Furthermore, the reception unit can provide input guides and support according to the user's emotions, allowing the user to input with peace of mind.

[0125] The analysis unit not only estimates the user's emotions and adjusts the method of expressing the analysis based on the emotions, but also selects the method of notifying the analysis results according to the user's emotions. For example, if the user is nervous, the analysis unit notifies the user of the analysis results by email. Alternatively, if the user is relaxed, the analysis unit can display the analysis results within the app. In this way, by selecting the method of notifying the analysis results according to the user's emotions, it is possible to provide the optimal notification method for the user. Furthermore, the analysis unit can adjust the timing of notifying the analysis results according to the user's emotions, notifying the user at a time when it is most convenient for the user to receive the results.

[0126] The diagnosis unit not only estimates the user's emotions and adjusts the criteria for diagnosis based on the emotions, but also selects a method for providing feedback of the diagnosis result in accordance with the user's emotions. For example, if the user is nervous, the diagnosis unit provides a concise summary of the diagnosis result. Also, if the user is relaxed, the diagnosis unit can provide a detailed diagnosis result. In this way, by selecting a method for providing feedback of the diagnosis result in accordance with the user's emotions, it is possible to provide feedback that is easy for the user to understand. Furthermore, the diagnosis unit can adjust the timing of feedback of the diagnosis result in accordance with the user's emotions, and provide feedback at a timing that is most convenient for the user.

[0127] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the emotions, as well as customize the content of the suggestions according to the user's emotions. For example, if the user is nervous, the suggestion unit can provide simple and to-the-point suggestions. On the other hand, if the user is relaxed, the suggestion unit can provide suggestions including detailed information. In this way, by customizing the content of the suggestions according to the user's emotions, it is possible to provide optimal suggestions for the user. Furthermore, the suggestion unit can adjust the priority of suggestions according to the user's emotions and preferentially display information that the user is most interested in.

[0128] The reservation unit not only estimates the user's emotions and adjusts the reservation method based on the emotions, but also selects a reservation confirmation method according to the user's emotions. For example, if the user is nervous, the reservation unit notifies the user of the reservation confirmation by email. Also, if the user is relaxed, the reservation unit can display the reservation confirmation within the app. In this way, by selecting a reservation confirmation method according to the user's emotions, it is possible to provide the user with the optimal confirmation method. Furthermore, the reservation unit can set a reservation reminder according to the user's emotions and notify the reminder at a time that is most convenient for the user.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit inputs the user's physical condition. The user's physical condition may include, but is not limited to, symptoms such as headache and fever. The user can input symptoms such as headache and fever, and can also show the AI ​​doctor a photo of the affected area. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the user's symptoms and photos to identify possible illnesses. It can also evaluate the condition of the affected area. Step 3: The diagnosis unit identifies illnesses based on the information analyzed by the analysis unit. For example, it can identify the possibility of a cold or flu based on symptoms such as headache and fever. Step 4: The suggestion unit suggests recommended hospitals based on the illness identified by the diagnosis unit. Based on information such as consultation times, availability of reservations, access, and reviews, it can suggest hospitals close to the user's current location or hospitals with high reviews. Step 5: The reservation unit makes a reservation at the hospital suggested by the suggestion unit. If the user wishes to make a reservation at the suggested hospital, the reservation procedure can be carried out on behalf of the user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's physical condition; an analysis unit that analyzes the information input by the reception unit; a diagnosis unit that identifies a disease based on the information analyzed by the analysis unit; a suggestion unit that suggests a recommended hospital based on the disease identified by the diagnosis unit; a reservation unit that makes a reservation at the hospital suggested by the suggestion unit; A system characterized by:

2. The analysis unit Analyze the user's symptoms or photos 2. The system of claim 1.

3. The diagnostic unit Identifying diseases based on analysis results 2. The system of claim 1.

4. The proposal unit Recommend hospitals based on consultation hours, availability, access, and reviews 2. The system of claim 1.

5. The reservation unit Make a suggested hospital appointment 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of inputting the user's physical condition based on the estimated emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past health history and select the input method 2. The system of claim 1.

8. The reception unit When entering your health status, filter it based on your current lifestyle and areas of interest.

2. The system of claim 1.

9. The reception unit When inputting the state of physical condition, select the input method according to the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and determine the priority of the physical condition information to be input based on the estimated user emotions.

2. The system of claim 1.

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