system

Abstract

The system according to the embodiment aims to estimate the name of a disease based on symptoms and quickly find a nearby medical institution. [Solution] A system according to an embodiment includes a reception unit, an estimation unit, a search unit, a provision unit, and a medical treatment unit. The reception unit receives input of symptoms. The estimation unit estimates the name of a disease based on the symptoms received by the reception unit. The search unit searches for nearby medical institutions based on the name of the disease estimated by the estimation unit. The provision unit provides the user with the medical institutions searched for by the search unit. The medical treatment unit receives remote medical treatment at the medical institution provided by the provision 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 technology has had the problem of making it difficult to accurately identify a disease based on symptoms and quickly find a nearby medical institution.

[0005] The system according to the embodiment aims to estimate the name of a disease based on symptoms and quickly find a nearby medical institution. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an estimation unit, a search unit, a provision unit, and a medical treatment unit. The reception unit receives input of symptoms. The estimation unit estimates the name of a disease based on the symptoms received by the reception unit. The search unit searches for nearby medical institutions based on the name of the disease estimated by the estimation unit. The provision unit provides the user with the medical institutions searched for by the search unit. The medical treatment unit receives remote medical treatment at the medical institution provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the name of the disease based on the symptoms and quickly find nearby medical institutions. [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 users to input their symptoms. Based on the input, a generation AI then suggests possible illnesses, recommends nearby medical institutions, and allows users to receive remote medical care. In this medical support system, users input their symptoms, and the generation AI analyzes the symptoms and suggests possible illnesses. Furthermore, the generation AI searches for nearby medical institutions based on the user's location information and displays a list. The user can select a medical institution from the list and receive remote medical care. For example, if a user inputs "headache," the generation AI suggests possible illnesses, such as "migraine" or "tension headache." The generation AI then searches for nearby medical institutions based on the user's location information and displays a list. The user can select a medical institution from the list and receive remote medical care. This allows users to quickly and easily receive appropriate medical care. The medical support system allows users to quickly and easily receive appropriate medical care simply by inputting their symptoms.

[0029] A medical support system according to an embodiment includes a reception unit, an estimation unit, a search unit, a provision unit, and a medical treatment unit. The reception unit accepts symptoms input by a user. For example, when a user enters "headache," the reception unit receives the information. The estimation unit uses a generation AI to estimate a disease name based on the symptoms accepted by the reception unit. For example, the generation AI analyzes the symptom "headache" and presents possible disease names such as "migraine" or "tension headache." The search unit searches for nearby medical institutions using the user's location information based on the disease name estimated by the estimation unit. For example, the search unit lists nearby hospitals and clinics based on the user's location information. The provision unit provides the user with a list of medical institutions searched by the search unit. For example, the provision unit displays a list of nearby medical institutions to the user, allowing the user to select one. The medical treatment unit enables the user to receive remote medical treatment at the medical institution selected by the user. For example, the medical treatment unit collaborates with the medical institution selected by the user and conducts remote medical treatment via video calls or chats. As a result, the medical support system according to the embodiment can consistently perform everything from inputting symptoms to estimating the name of the disease, searching for medical institutions, providing information, and remote medical treatment.

[0030] The estimation unit can analyze symptoms using a generation AI and infer a likely disease name. For example, the estimation unit uses a generation AI to analyze symptoms entered by a user. For example, if a user enters "headache," the estimation unit uses the generation AI to analyze the symptom and suggest possible disease names, such as "migraine" or "tension headache." The estimation unit can also estimate a disease name by having the generation AI take into account the severity and frequency of symptoms. For example, if a user enters "frequent headaches," the generation AI can prioritize disease names with higher severity. This allows the generation AI to analyze symptoms and infer a disease name with high accuracy. Some or all of the above-described processing in the estimation unit is performed using a generation AI (e.g., an AI using deep learning or natural language processing technology). For example, the estimation unit inputs the symptoms entered by the user into the generation AI, and the generation AI executes a process to infer a disease name.

[0031] The search unit can search for nearby medical institutions based on the user's location information. For example, the search unit acquires the user's location information and searches for nearby medical institutions based on that information. For example, the search unit identifies the user's current location using GPS or Wi-Fi location information and lists nearby medical institutions. The search unit can also search for medical institutions based on the user's location information, taking into account distance and transportation means. For example, the search unit can prioritize displaying medical institutions within walking distance of the user's current location. This allows for efficient search for nearby medical institutions based on the user's location information. Some or all of the above-mentioned processing in the search unit is performed using, for example, a location information service or a map database. For example, the search unit inputs the user's location information into a location information service and executes a process to acquire data on nearby medical institutions.

[0032] The providing unit can provide the user with a list of medical institutions searched by the search unit. The providing unit, for example, displays the list of medical institutions searched by the search unit to the user. For example, the providing unit displays a list of nearby medical institutions on the user's screen, allowing the user to select one. The providing unit can also customize the list of medical institutions according to the user's preferences. For example, the providing unit can preferentially display medical institutions that are most suitable for the user based on the user's past selection history and evaluations. This makes it easier for the user to make a selection by providing the user with a list of searched medical institutions. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a user interface or a database. For example, the providing unit executes a process of displaying data of medical institutions acquired from the search unit on a user interface.

[0033] The medical department allows the user to receive remote medical treatment at a medical institution selected by the user. The medical department, for example, cooperates with the medical institution selected by the user to provide remote medical treatment. For example, the medical department connects the user with a doctor via video call or chat and provides medical treatment. The medical department can also provide the user's medical history and health information to the doctor. For example, the medical department can share the user's past medical history and current health condition with the doctor to use as reference for medical treatment. This allows the user to receive remote medical treatment at a medical institution selected by the user. Some or all of the above-mentioned processing in the medical department is performed using, for example, communication technology and databases. For example, the medical department connects the doctor and the user using a video call system and executes a process to provide medical treatment.

[0034] The reception unit can analyze the user's past symptom input history and suggest an appropriate input method. The reception unit, for example, retrieves the user's past symptom input history from a database and analyzes it. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input during a specific time period based on the user's past input history. This makes user input more efficient by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit is performed using, for example, a database or a machine learning algorithm. For example, the reception unit inputs the user's past input data into a machine learning algorithm and executes a process of suggesting the optimal input method.

[0035] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. The reception unit, for example, acquires data related to the user's current health condition and lifestyle habits and filters the input content based on that information. For example, the reception unit filters out less relevant symptoms taking into account the user's current health condition. The reception unit can also prioritize displaying more relevant symptoms based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter out appropriate symptoms based on the user's age and gender. In this way, by filtering the input content based on the user's health condition and lifestyle habits, more relevant symptoms can be input preferentially. Some or all of the above-described processing in the reception unit is performed using, for example, a database or a filtering algorithm. For example, the reception unit inputs the user's health data into a filtering algorithm and executes a process to display more relevant symptoms.

[0036] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information and prioritizes displaying highly relevant symptoms based on the information. For example, when the user is in a specific area, the reception unit can prioritize displaying symptoms related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the reception unit can prioritize displaying symptoms related to health risks at the user's travel destination. Furthermore, when the user is in specific weather conditions, the reception unit can prioritize displaying symptoms related to the weather. In this way, highly relevant symptoms can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit can be performed using, for example, a location information service or a filtering algorithm. For example, the reception unit inputs the user's geographical location information into a location information service and executes a process to display highly relevant symptoms.

[0037] When entering symptoms, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the reception unit can suggest related symptoms based on health information shared by the user on social media. The reception unit can also suggest related symptoms based on information about health-related accounts the user follows on social media. Furthermore, the reception unit can suggest related symptoms based on information about health-related groups the user participates in on social media. This allows related symptoms to be efficiently input by analyzing social media activity. Some or all of the above-described processing in the reception unit is performed using, for example, a database or an analysis algorithm. For example, the reception unit inputs the user's social media data into an analysis algorithm and executes a process of suggesting related symptoms.

[0038] The estimation unit can adjust the level of detail of the estimation based on the importance of the symptoms when estimating the disease name. The estimation unit, for example, evaluates the importance of the symptoms input by the user and adjusts the level of detail of the estimation based on the information. For example, the estimation unit estimates a detailed disease name when a serious symptom is input. The estimation unit can also estimate a concise disease name when a minor symptom is input. Furthermore, when multiple symptoms are input, the estimation unit can prioritize the estimation of symptoms with higher importance. In this way, by adjusting the level of detail of the estimation based on the importance of the symptoms, a detailed disease name can be estimated for important symptoms. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, an importance evaluation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into an importance evaluation algorithm and executes a process of evaluating the importance.

[0039] The estimation unit can apply different estimation algorithms depending on the symptom category when estimating the disease name. For example, the estimation unit determines the symptom category input by the user and applies different estimation algorithms based on the information. For example, the estimation unit applies a specialized respiratory algorithm to respiratory symptoms. The estimation unit can also apply a specialized digestive algorithm to digestive symptoms. Furthermore, the estimation unit can apply a specialized neurological algorithm to nervous system symptoms. In this way, applying different estimation algorithms depending on the symptom category enables more accurate disease name estimation. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, a category determination algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a category determination algorithm and executes a process of determining the category.

[0040] When estimating a disease name, the estimation unit can determine the priority of estimation based on the time of symptom submission. The estimation unit, for example, evaluates the time of symptom submission input by the user and determines the priority of estimation based on that information. For example, the estimation unit prioritizes the most recently submitted symptoms. The estimation unit can also make estimations based on symptoms submitted in the past. Furthermore, the estimation unit can prioritize the symptoms submitted within a specific time period. In this way, determining the priority of estimation based on the time of symptom submission enables a prompt response to the latest symptoms. Some or all of the above-described processing in the estimation unit is performed using, for example, a submission time evaluation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a submission time evaluation algorithm and executes a process of evaluating the submission time.

[0041] The estimation unit can adjust the order of estimation based on the relevance of symptoms when estimating disease names. The estimation unit, for example, evaluates the relevance of symptoms input by the user and adjusts the order of estimation based on the information. For example, the estimation unit prioritizes estimating highly relevant symptoms. The estimation unit can also postpone less relevant symptoms. Furthermore, when multiple symptoms are related, the estimation unit can also estimate symptoms in order of relevance. In this way, by adjusting the order of estimation based on the relevance of symptoms, highly relevant disease names can be prioritized. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, a relevance assessment algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a relevance assessment algorithm and executes a process of evaluating the relevance.

[0042] When searching for medical institutions, the search unit can improve the accuracy of the search by taking into account the interrelationships between medical institutions. For example, the search unit obtains the interrelationships between medical institutions from a database and improves the accuracy of the search based on that information. For example, the search unit prioritizes searching for affiliated medical institutions. The search unit can also prioritize searching for medical institutions that employ specialists. Furthermore, the search unit can also consider relationships with medical institutions that the user has used in the past when searching. In this way, the accuracy of the search is improved by taking into account the interrelationships between medical institutions. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs interrelationship data between medical institutions into a search algorithm and executes a process to improve the accuracy of the search.

[0043] When searching for a medical institution, the search unit can perform a search taking into account the attribute information of the medical institution. For example, the search unit obtains the attribute information of the medical institution from a database and performs a search based on that information. For example, the search unit performs a search taking into account the medical specialty of the medical institution. The search unit can also perform a search taking into account the medical institution's consultation hours. Furthermore, the search unit can perform a search taking into account the medical institution's ratings and word-of-mouth reviews. In this way, by taking into account the attribute information of the medical institution, a more appropriate medical institution can be searched for. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs the attribute information of the medical institution into the search algorithm and executes the search process.

[0044] The search unit can link with other systems and integrate information from different data sources. For example, the search unit links with other systems through an API and integrates information from different data sources. For example, the search unit obtains and integrates information from electronic medical record systems and medical databases. The search unit can also link with other medical information systems and share data. This improves search accuracy by integrating information from different data sources. Some or all of the above-mentioned processing in the search unit is performed using, for example, an API or a data integration algorithm. For example, the search unit inputs data obtained from other systems into an integration algorithm and executes the process of integrating information.

[0045] When searching for medical institutions, the search unit can improve the accuracy of the search by referring to literature related to the medical institution. For example, the search unit obtains literature related to the medical institution from a database and improves the accuracy of the search based on that information. For example, the search unit searches by referring to research papers on the medical institution. The search unit can also search by referring to the medical institution's treatment record. Furthermore, the search unit can search by referring to literature related to the medical institution's specialty. In this way, by referring to literature related to the medical institution, the accuracy of the search is improved. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs literature data related to the medical institution into a search algorithm and executes a process to improve the accuracy of the search.

[0046] When providing a list of medical institutions, the providing unit can optimize the current method of providing the list by referring to past provided data. The providing unit, for example, obtains past provided data from a database and optimizes the current method of providing the list based on that information. For example, the providing unit analyzes the usage status of medical institution lists provided in the past and provides an optimal list. The providing unit can also improve the method of displaying the list based on past user feedback. Furthermore, the providing unit can provide a list tailored to the user's preferences from the past provided data. In this way, the method of providing the list is optimized by referring to the past provided data. Some or all of the above-described processing in the providing unit is performed using, for example, a database or an analysis algorithm. For example, the providing unit inputs the past provided data into an analysis algorithm and executes a process of optimizing the current method of providing the list.

[0047] When providing a list of medical institutions, the providing unit can apply different providing methods to each category of medical institutions. For example, the providing unit obtains the category of the medical institution from a database and applies different providing methods based on that information. For example, the providing unit displays a list by dividing it into general hospitals and specialized clinics. The providing unit can also classify and display the list by medical department. Furthermore, the providing unit can also classify and display the list by consultation hours or symptoms that can be treated. In this way, by applying different providing methods to each category of medical institution, the user can efficiently obtain the information they need. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a database or a classification algorithm. For example, the providing unit inputs medical institution category data into a classification algorithm and executes a process of classifying the list.

[0048] When providing a list of medical institutions, the providing unit can determine the priority of the list based on the submission date of the medical institution. For example, the providing unit obtains the submission date of the medical institution from a database and determines the priority of the list based on that information. For example, the providing unit prioritizes recently registered medical institutions in the list. The providing unit can also prioritize medical institutions that have received high ratings in the past in the list. Furthermore, the providing unit can prioritize medical institutions that are available during a specific time period in the list. In this way, by determining the priority of the list based on the submission date of the medical institution, the latest information can be provided preferentially. Some or all of the above-described processing in the providing unit is performed using, for example, a database or a list display algorithm. For example, the providing unit inputs the submission date data of the medical institution into the list display algorithm and executes a process of determining the priority of the list.

[0049] When providing the list of medical institutions, the providing unit can adjust the order of the list based on the relevance of the medical institutions. For example, the providing unit obtains the relevance of the medical institutions from a database and adjusts the order of the list based on the information. For example, the providing unit displays the medical institution that is most relevant to the user's symptoms at the top. The providing unit can also display the medical institution that is highly relevant based on the user's past medical history at the top. Furthermore, the providing unit can also display the medical institution that is highly relevant based on the user's current health condition at the top. In this way, by adjusting the order of the list based on the relevance of the medical institutions, it is possible to preferentially display the medical institution that is highly relevant. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a database or a list display algorithm. For example, the providing unit inputs the relevance data of the medical institutions into the list display algorithm and executes a process of adjusting the order of the list.

[0050] During remote medical consultations, the medical department can analyze the user's past medical history and select the optimal medical method. For example, the medical department obtains the user's past medical history from a database and selects the optimal medical method based on that information. For example, the medical department suggests the optimal treatment method based on the user's past medical history. The medical department can also recommend a specific doctor based on the user's past medical history. Furthermore, the medical department can analyze the user's past medical history and determine medical priorities. This makes it possible to select the optimal medical method by analyzing the past medical history. Some or all of the above-mentioned processing in the medical department is performed using, for example, a database or an analysis algorithm. For example, the medical department inputs the user's past medical data into an analysis algorithm and executes a process to select the optimal medical method.

[0051] During remote medical consultations, the medical department can customize medical treatment methods based on the user's current health condition. For example, the medical department obtains the user's current health condition from a database and customizes the medical treatment methods based on that information. For example, the medical department selects an appropriate medical treatment method taking into account the user's current health condition. The medical department can also adjust the frequency of medical treatment based on the user's current health condition. Furthermore, the medical department can recommend a specific medical treatment method based on the user's current health condition. This allows for more appropriate medical treatment by customizing the medical treatment method based on the user's current health condition. Some or all of the above-described processing in the medical department is performed using, for example, a database or a customization algorithm. For example, the medical department inputs the user's health data into a customization algorithm and executes a process to customize the medical treatment method.

[0052] During remote medical consultations, the medical department can select the optimal medical treatment method by taking into account the user's geographical location information. For example, the medical department obtains the user's geographical location information from a database and selects the optimal medical treatment method based on that information. For example, if the user is in a remote location, the medical department prioritizes online medical treatment. Also, if the user is in an urban area, the medical department can consider cooperation with nearby medical institutions. Furthermore, if the user is traveling, the medical department can suggest medical treatment options at the user's destination. In this way, the optimal medical treatment method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the medical department is performed using, for example, a database or a medical treatment algorithm. For example, the medical department inputs the user's geographical location information into a medical treatment algorithm and executes a process to select the optimal medical treatment method.

[0053] During remote medical consultations, the medical department can analyze the user's social media activity and suggest medical treatment options. For example, the medical department retrieves the user's social media activity from a database and suggests medical treatment options based on that information. For example, the medical department can suggest appropriate medical treatment options based on health information shared by the user on social media. The medical department can also suggest medical treatment options based on information about health-related accounts the user follows on social media. Furthermore, the medical department can suggest medical treatment options based on information about health-related groups the user participates in on social media. In this way, appropriate medical treatment options can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the medical department is performed using, for example, a database or an analysis algorithm. For example, the medical department inputs the user's social media data into an analysis algorithm and executes a process to suggest medical treatment options.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can complement the symptom input based on the user's past medical history. For example, if the user has previously complained of a "headache," the reception unit references that history and automatically complements the past medical treatment details and treatment when the user inputs "headache" again. The reception unit can also suggest related symptoms based on the results of the user's past medical treatment. Furthermore, the reception unit can prioritize the display of specific doctors and medical institutions based on the user's past medical history. This makes it possible to efficiently input symptoms by utilizing the user's past medical history, and provide more appropriate medical services.

[0056] The estimation unit can improve the accuracy of disease name estimation based on the user's lifestyle habit data. For example, if the user is a smoker, the estimation unit will preferentially present disease names related to smoking. Also, if the user exercises regularly, disease names related to lack of exercise can be excluded. Furthermore, disease names related to nutritional deficiencies or overintake can also be estimated based on the user's dietary habit data. This makes it possible to estimate disease names with higher accuracy by utilizing the user's lifestyle habit data.

[0057] The search unit can search for medical institutions taking into account the user's medical insurance information. For example, if the user is subscribed to a specific insurance plan, medical institutions that support that insurance plan are preferentially displayed. The search unit can also suggest medical institutions with low out-of-pocket expenses based on the user's insurance coverage. Furthermore, the search unit can search for medical institutions that support specific treatments or medical specialties based on the user's insurance information. This allows for a more appropriate medical institution to be searched for by taking into account the user's medical insurance information.

[0058] The providing unit can display a list of medical institutions based on the user's language setting. For example, if the user selects English, medical institutions that speak English are displayed preferentially. The providing unit can also provide information about medical institutions in multiple languages ​​based on the user's language setting. Furthermore, the providing unit can translate and display ratings and reviews of medical institutions based on the user's language setting. This makes it possible to provide information according to the user's language setting, improving convenience.

[0059] The medical department can track the progress of medical treatment in real time based on the user's medical history. For example, the medical department can refer to the medical treatments and treatments the user has received in the past and compare them with the current treatment. The medical department can also evaluate the effectiveness of treatment based on the user's medical history and adjust the treatment plan as necessary. Furthermore, the medical department can automatically schedule the next medical treatment based on the user's medical history. This allows for more effective treatment by tracking the progress of medical treatment in real time.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives symptoms input by the user. For example, if the user inputs "headache," the reception unit receives the information. Step 2: The estimation unit uses the generation AI to estimate the name of the disease based on the symptoms received by the reception unit. For example, the estimation unit uses the generation AI to analyze the symptom "headache" and present possible disease names such as "migraine" or "tension headache." Step 3: The search unit searches for nearby medical institutions using the user's location information based on the disease name estimated by the estimation unit. For example, the search unit lists nearby hospitals and clinics based on the user's location information. Step 4: The providing unit provides the user with a list of medical institutions searched by the searching unit. For example, the providing unit displays a list of nearby medical institutions to the user, allowing the user to select one. Step 5: The medical department enables the user to receive remote medical treatment at the medical institution selected by the user. For example, the medical department collaborates with the medical institution selected by the user and conducts remote medical treatment via video call or chat.

[0062] (Example 2) A medical support system according to an embodiment of the present invention allows users to input their symptoms. Based on the input, a generation AI then suggests possible illnesses, recommends nearby medical institutions, and allows users to receive remote medical care. In this medical support system, users input their symptoms, and the generation AI analyzes the symptoms and suggests possible illnesses. Furthermore, the generation AI searches for nearby medical institutions based on the user's location information and displays a list. The user can select a medical institution from the list and receive remote medical care. For example, if a user inputs "headache," the generation AI suggests possible illnesses, such as "migraine" or "tension headache." The generation AI then searches for nearby medical institutions based on the user's location information and displays a list. The user can select a medical institution from the list and receive remote medical care. This allows users to quickly and easily receive appropriate medical care. The medical support system allows users to quickly and easily receive appropriate medical care simply by inputting their symptoms.

[0063] A medical support system according to an embodiment includes a reception unit, an estimation unit, a search unit, a provision unit, and a medical treatment unit. The reception unit accepts symptoms input by a user. For example, when a user enters "headache," the reception unit receives the information. The estimation unit uses a generation AI to estimate a disease name based on the symptoms accepted by the reception unit. For example, the generation AI analyzes the symptom "headache" and presents possible disease names such as "migraine" or "tension headache." The search unit searches for nearby medical institutions using the user's location information based on the disease name estimated by the estimation unit. For example, the search unit lists nearby hospitals and clinics based on the user's location information. The provision unit provides the user with a list of medical institutions searched by the search unit. For example, the provision unit displays a list of nearby medical institutions to the user, allowing the user to select one. The medical treatment unit enables the user to receive remote medical treatment at the medical institution selected by the user. For example, the medical treatment unit collaborates with the medical institution selected by the user and conducts remote medical treatment via video calls or chats. As a result, the medical support system according to the embodiment can consistently perform everything from inputting symptoms to estimating the name of the disease, searching for medical institutions, providing information, and remote medical treatment.

[0064] The estimation unit can analyze symptoms using a generation AI and infer a likely disease name. For example, the estimation unit uses a generation AI to analyze symptoms entered by a user. For example, if a user enters "headache," the estimation unit uses the generation AI to analyze the symptom and suggest possible disease names, such as "migraine" or "tension headache." The estimation unit can also estimate a disease name by having the generation AI take into account the severity and frequency of symptoms. For example, if a user enters "frequent headaches," the generation AI can prioritize disease names with higher severity. This allows the generation AI to analyze symptoms and infer a disease name with high accuracy. Some or all of the above-described processing in the estimation unit is performed using a generation AI (e.g., an AI using deep learning or natural language processing technology). For example, the estimation unit inputs the symptoms entered by the user into the generation AI, and the generation AI executes a process to infer a disease name.

[0065] The search unit can search for nearby medical institutions based on the user's location information. For example, the search unit acquires the user's location information and searches for nearby medical institutions based on that information. For example, the search unit identifies the user's current location using GPS or Wi-Fi location information and lists nearby medical institutions. The search unit can also search for medical institutions based on the user's location information, taking into account distance and transportation means. For example, the search unit can prioritize displaying medical institutions within walking distance of the user's current location. This allows for efficient search for nearby medical institutions based on the user's location information. Some or all of the above-mentioned processing in the search unit is performed using, for example, a location information service or a map database. For example, the search unit inputs the user's location information into a location information service and executes a process to acquire data on nearby medical institutions.

[0066] The providing unit can provide the user with a list of medical institutions searched by the search unit. The providing unit, for example, displays the list of medical institutions searched by the search unit to the user. For example, the providing unit displays a list of nearby medical institutions on the user's screen, allowing the user to select one. The providing unit can also customize the list of medical institutions according to the user's preferences. For example, the providing unit can preferentially display medical institutions that are most suitable for the user based on the user's past selection history and evaluations. This makes it easier for the user to make a selection by providing the user with a list of searched medical institutions. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a user interface or a database. For example, the providing unit executes a process of displaying data of medical institutions acquired from the search unit on a user interface.

[0067] The medical department allows the user to receive remote medical treatment at a medical institution selected by the user. The medical department, for example, cooperates with the medical institution selected by the user to provide remote medical treatment. For example, the medical department connects the user with a doctor via video call or chat and provides medical treatment. The medical department can also provide the user's medical history and health information to the doctor. For example, the medical department can share the user's past medical history and current health condition with the doctor to use as reference for medical treatment. This allows the user to receive remote medical treatment at a medical institution selected by the user. Some or all of the above-mentioned processing in the medical department is performed using, for example, communication technology and databases. For example, the medical department connects the doctor and the user using a video call system and executes a process to provide medical treatment.

[0068] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user emotion. The reception unit, for example, analyzes the user's facial expressions and voice to estimate the emotion. For example, if the user is feeling anxious, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick symptom input. This improves user convenience by adjusting the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using, for example, an emotion estimation algorithm or a user interface. For example, the reception unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate the emotion.

[0069] The reception unit can analyze the user's past symptom input history and suggest an appropriate input method. The reception unit, for example, retrieves the user's past symptom input history from a database and analyzes it. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input during a specific time period based on the user's past input history. This makes user input more efficient by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit is performed using, for example, a database or a machine learning algorithm. For example, the reception unit inputs the user's past input data into a machine learning algorithm and executes a process of suggesting the optimal input method.

[0070] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. The reception unit, for example, acquires data related to the user's current health condition and lifestyle habits and filters the input content based on that information. For example, the reception unit filters out less relevant symptoms taking into account the user's current health condition. The reception unit can also prioritize displaying more relevant symptoms based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter out appropriate symptoms based on the user's age and gender. In this way, by filtering the input content based on the user's health condition and lifestyle habits, more relevant symptoms can be input preferentially. Some or all of the above-described processing in the reception unit is performed using, for example, a database or a filtering algorithm. For example, the reception unit inputs the user's health data into a filtering algorithm and executes a process to display more relevant symptoms.

[0071] The reception unit can estimate the user's emotions and determine the priority of symptoms to be input based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the reception unit can prompt the user to input serious symptoms first. The reception unit can also prompt the user to input detailed symptoms first if the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also prompt the user to input major symptoms first. This allows the priority of symptoms to be determined according to the user's emotions, allowing important symptoms to be input first. 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 reception unit is performed using, for example, an emotion estimation algorithm or a user interface. For example, the reception unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate emotions.

[0072] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information and prioritizes displaying highly relevant symptoms based on the information. For example, when the user is in a specific area, the reception unit can prioritize displaying symptoms related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the reception unit can prioritize displaying symptoms related to health risks at the user's travel destination. Furthermore, when the user is in specific weather conditions, the reception unit can prioritize displaying symptoms related to the weather. In this way, highly relevant symptoms can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit can be performed using, for example, a location information service or a filtering algorithm. For example, the reception unit inputs the user's geographical location information into a location information service and executes a process to display highly relevant symptoms.

[0073] When entering symptoms, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the reception unit can suggest related symptoms based on health information shared by the user on social media. The reception unit can also suggest related symptoms based on information about health-related accounts the user follows on social media. Furthermore, the reception unit can suggest related symptoms based on information about health-related groups the user participates in on social media. This allows related symptoms to be efficiently input by analyzing social media activity. Some or all of the above-described processing in the reception unit is performed using, for example, a database or an analysis algorithm. For example, the reception unit inputs the user's social media data into an analysis algorithm and executes a process of suggesting related symptoms.

[0074] The estimation unit can estimate the user's emotions and adjust the disease name estimation method based on the estimated user emotions. The estimation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the estimation unit can quickly and concisely estimate the disease name. Furthermore, if the user is relaxed, the estimation unit can also estimate a detailed disease name. Furthermore, if the user is in a hurry, the estimation unit can prioritize the main disease name. This allows for a more appropriate disease name to be estimated by adjusting the disease name estimation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit is performed using, for example, an emotion estimation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process of estimating emotions.

[0075] The estimation unit can adjust the level of detail of the estimation based on the importance of the symptoms when estimating the disease name. The estimation unit, for example, evaluates the importance of the symptoms input by the user and adjusts the level of detail of the estimation based on the information. For example, the estimation unit estimates a detailed disease name when a serious symptom is input. The estimation unit can also estimate a concise disease name when a minor symptom is input. Furthermore, when multiple symptoms are input, the estimation unit can prioritize the estimation of symptoms with higher importance. In this way, by adjusting the level of detail of the estimation based on the importance of the symptoms, a detailed disease name can be estimated for important symptoms. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, an importance evaluation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into an importance evaluation algorithm and executes a process of evaluating the importance.

[0076] The estimation unit can apply different estimation algorithms depending on the symptom category when estimating the disease name. For example, the estimation unit determines the symptom category input by the user and applies different estimation algorithms based on the information. For example, the estimation unit applies a specialized respiratory algorithm to respiratory symptoms. The estimation unit can also apply a specialized digestive algorithm to digestive symptoms. Furthermore, the estimation unit can apply a specialized neurological algorithm to nervous system symptoms. In this way, applying different estimation algorithms depending on the symptom category enables more accurate disease name estimation. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, a category determination algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a category determination algorithm and executes a process of determining the category.

[0077] The estimation unit can estimate the user's emotions and determine the priority of disease names to be estimated based on the estimated user emotions. The estimation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the estimation unit can prioritize serious disease names. The estimation unit can also estimate detailed disease names if the user is relaxed. Furthermore, if the user is in a hurry, the estimation unit can prioritize major disease names. This allows for prioritized disease names to be prioritized according to the user's emotions, thereby enabling important disease names to be prioritized. 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 estimation unit is performed using, for example, an emotion estimation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process of estimating emotions.

[0078] When estimating a disease name, the estimation unit can determine the priority of estimation based on the time of symptom submission. The estimation unit, for example, evaluates the time of symptom submission input by the user and determines the priority of estimation based on that information. For example, the estimation unit prioritizes the most recently submitted symptoms. The estimation unit can also make estimations based on symptoms submitted in the past. Furthermore, the estimation unit can prioritize the symptoms submitted within a specific time period. In this way, determining the priority of estimation based on the time of symptom submission enables a prompt response to the latest symptoms. Some or all of the above-described processing in the estimation unit is performed using, for example, a submission time evaluation algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a submission time evaluation algorithm and executes a process of evaluating the submission time.

[0079] The estimation unit can adjust the order of estimation based on the relevance of symptoms when estimating disease names. The estimation unit, for example, evaluates the relevance of symptoms input by the user and adjusts the order of estimation based on the information. For example, the estimation unit prioritizes estimating highly relevant symptoms. The estimation unit can also postpone less relevant symptoms. Furthermore, when multiple symptoms are related, the estimation unit can also estimate symptoms in order of relevance. In this way, by adjusting the order of estimation based on the relevance of symptoms, highly relevant disease names can be prioritized. Some or all of the above-mentioned processing in the estimation unit is performed using, for example, a relevance assessment algorithm or a disease name estimation algorithm. For example, the estimation unit inputs the user's symptom data into a relevance assessment algorithm and executes a process of evaluating the relevance.

[0080] The search unit can estimate the user's emotions and adjust search criteria for medical institutions based on the estimated user emotions. The search unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the search unit can prioritize searching for medical institutions that can respond quickly. Furthermore, if the user is relaxed, the search unit can prioritize searching for medical institutions that provide detailed information. Furthermore, if the user is in a hurry, the search unit can prioritize searching for nearby medical institutions. This allows for adjusting the search criteria for medical institutions according to the user's emotions to search for a more appropriate medical institution. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit is performed using, for example, an emotion estimation algorithm or a search algorithm. For example, the search unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate emotions.

[0081] When searching for medical institutions, the search unit can improve the accuracy of the search by taking into account the interrelationships between medical institutions. For example, the search unit obtains the interrelationships between medical institutions from a database and improves the accuracy of the search based on that information. For example, the search unit prioritizes searching for affiliated medical institutions. The search unit can also prioritize searching for medical institutions that employ specialists. Furthermore, the search unit can also consider relationships with medical institutions that the user has used in the past when searching. In this way, the accuracy of the search is improved by taking into account the interrelationships between medical institutions. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs interrelationship data between medical institutions into a search algorithm and executes a process to improve the accuracy of the search.

[0082] When searching for a medical institution, the search unit can perform a search taking into account the attribute information of the medical institution. For example, the search unit obtains the attribute information of the medical institution from a database and performs a search based on that information. For example, the search unit performs a search taking into account the medical specialty of the medical institution. The search unit can also perform a search taking into account the medical institution's consultation hours. Furthermore, the search unit can perform a search taking into account the medical institution's ratings and word-of-mouth reviews. In this way, by taking into account the attribute information of the medical institution, a more appropriate medical institution can be searched for. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs the attribute information of the medical institution into the search algorithm and executes the search process.

[0083] The search unit can link with other systems and integrate information from different data sources. For example, the search unit links with other systems through an API and integrates information from different data sources. For example, the search unit obtains and integrates information from electronic medical record systems and medical databases. The search unit can also link with other medical information systems and share data. This improves search accuracy by integrating information from different data sources. Some or all of the above-mentioned processing in the search unit is performed using, for example, an API or a data integration algorithm. For example, the search unit inputs data obtained from other systems into an integration algorithm and executes the process of integrating information.

[0084] When searching for medical institutions, the search unit can improve the accuracy of the search by referring to literature related to the medical institution. For example, the search unit obtains literature related to the medical institution from a database and improves the accuracy of the search based on that information. For example, the search unit searches by referring to research papers on the medical institution. The search unit can also search by referring to the medical institution's treatment record. Furthermore, the search unit can search by referring to literature related to the medical institution's specialty. In this way, by referring to literature related to the medical institution, the accuracy of the search is improved. Some or all of the above-mentioned processing in the search unit is performed using, for example, a database or a search algorithm. For example, the search unit inputs literature data related to the medical institution into a search algorithm and executes a process to improve the accuracy of the search.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the medical institution list based on the estimated user emotions. The providing unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the providing unit can display a simple, highly visible list. Furthermore, if the user is relaxed, the providing unit can also display a list including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a list that displays only key information. This improves user convenience by adjusting the display method of the list according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit is performed using, for example, an emotion estimation algorithm or a user interface. For example, the providing unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate emotions.

[0086] When providing a list of medical institutions, the providing unit can optimize the current method of providing the list by referring to past provided data. The providing unit, for example, obtains past provided data from a database and optimizes the current method of providing the list based on that information. For example, the providing unit analyzes the usage status of medical institution lists provided in the past and provides an optimal list. The providing unit can also improve the method of displaying the list based on past user feedback. Furthermore, the providing unit can provide a list tailored to the user's preferences from the past provided data. In this way, the method of providing the list is optimized by referring to the past provided data. Some or all of the above-described processing in the providing unit is performed using, for example, a database or an analysis algorithm. For example, the providing unit inputs the past provided data into an analysis algorithm and executes a process of optimizing the current method of providing the list.

[0087] When providing a list of medical institutions, the providing unit can apply different providing methods to each category of medical institutions. For example, the providing unit obtains the category of the medical institution from a database and applies different providing methods based on that information. For example, the providing unit displays a list by dividing it into general hospitals and specialized clinics. The providing unit can also classify and display the list by medical department. Furthermore, the providing unit can also classify and display the list by consultation hours or symptoms that can be treated. In this way, by applying different providing methods to each category of medical institution, the user can efficiently obtain the information they need. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a database or a classification algorithm. For example, the providing unit inputs medical institution category data into a classification algorithm and executes a process of classifying the list.

[0088] The providing unit can estimate the user's emotions and determine the priority of the list of medical institutions based on the estimated user emotions. The providing unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. For example, if the user is feeling anxious, the providing unit can display medical institutions that can respond quickly at the top. Furthermore, if the user is relaxed, the providing unit can also display medical institutions that provide detailed information at the top. Furthermore, if the user is in a hurry, the providing unit can display nearby medical institutions at the top. This allows important medical institutions to be displayed preferentially by determining the priority of the list according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit is performed using, for example, an emotion estimation algorithm or a list display algorithm. For example, the providing unit inputs the user's facial expression data into an emotion estimation algorithm and executes a process of estimating emotions.

[0089] When providing a list of medical institutions, the providing unit can determine the priority of the list based on the submission date of the medical institution. For example, the providing unit obtains the submission date of the medical institution from a database and determines the priority of the list based on that information. For example, the providing unit prioritizes recently registered medical institutions in the list. The providing unit can also prioritize medical institutions that have received high ratings in the past in the list. Furthermore, the providing unit can prioritize medical institutions that are available during a specific time period in the list. In this way, by determining the priority of the list based on the submission date of the medical institution, the latest information can be provided preferentially. Some or all of the above-described processing in the providing unit is performed using, for example, a database or a list display algorithm. For example, the providing unit inputs the submission date data of the medical institution into the list display algorithm and executes a process of determining the priority of the list.

[0090] When providing the list of medical institutions, the providing unit can adjust the order of the list based on the relevance of the medical institutions. For example, the providing unit obtains the relevance of the medical institutions from a database and adjusts the order of the list based on the information. For example, the providing unit displays the medical institution that is most relevant to the user's symptoms at the top. The providing unit can also display the medical institution that is highly relevant based on the user's past medical history at the top. Furthermore, the providing unit can also display the medical institution that is highly relevant based on the user's current health condition at the top. In this way, by adjusting the order of the list based on the relevance of the medical institutions, it is possible to preferentially display the medical institution that is highly relevant. Some or all of the above-mentioned processing in the providing unit is performed using, for example, a database or a list display algorithm. For example, the providing unit inputs the relevance data of the medical institutions into the list display algorithm and executes a process of adjusting the order of the list.

[0091] The medical department can estimate the user's emotions and adjust the remote medical treatment method based on the estimated user emotions. The medical department, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user feels anxious, the medical department may have the doctor provide medical treatment in a calm voice. If the user is relaxed, the medical department may provide medical treatment with detailed explanations. Furthermore, if the user is in a hurry, the medical department may provide quick and concise medical treatment. This allows for more appropriate medical treatment by adjusting the medical treatment method according to the user's emotions. Emotion estimation is achieved 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-mentioned processing in the medical department is performed using, for example, an emotion estimation algorithm or a medical treatment algorithm. For example, the medical department inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate emotions.

[0092] During remote medical consultations, the medical department can analyze the user's past medical history and select the optimal medical method. For example, the medical department obtains the user's past medical history from a database and selects the optimal medical method based on that information. For example, the medical department suggests the optimal treatment method based on the user's past medical history. The medical department can also recommend a specific doctor based on the user's past medical history. Furthermore, the medical department can analyze the user's past medical history and determine medical priorities. This makes it possible to select the optimal medical method by analyzing the past medical history. Some or all of the above-mentioned processing in the medical department is performed using, for example, a database or an analysis algorithm. For example, the medical department inputs the user's past medical data into an analysis algorithm and executes a process to select the optimal medical method.

[0093] During remote medical consultations, the medical department can customize medical treatment methods based on the user's current health condition. For example, the medical department obtains the user's current health condition from a database and customizes the medical treatment methods based on that information. For example, the medical department selects an appropriate medical treatment method taking into account the user's current health condition. The medical department can also adjust the frequency of medical treatment based on the user's current health condition. Furthermore, the medical department can recommend a specific medical treatment method based on the user's current health condition. This allows for more appropriate medical treatment by customizing the medical treatment method based on the user's current health condition. Some or all of the above-described processing in the medical department is performed using, for example, a database or a customization algorithm. For example, the medical department inputs the user's health data into a customization algorithm and executes a process to customize the medical treatment method.

[0094] The medical department can estimate the user's emotions and determine the priority of remote medical consultations based on the estimated user emotions. The medical department, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling anxious, the medical department can quickly start medical consultations. Furthermore, if the user is relaxed, the medical department can provide detailed medical consultations. Furthermore, if the user is in a hurry, the medical department can provide medical consultations focusing on the user's main symptoms. This allows prioritizing medical consultations based on the user's emotions, enabling important medical consultations to be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the medical department is performed using, for example, an emotion estimation algorithm or a medical consultation algorithm. For example, the medical department inputs the user's facial expression data into an emotion estimation algorithm and executes a process to estimate emotions.

[0095] During remote medical consultations, the medical department can select the optimal medical treatment method by taking into account the user's geographical location information. For example, the medical department obtains the user's geographical location information from a database and selects the optimal medical treatment method based on that information. For example, if the user is in a remote location, the medical department prioritizes online medical treatment. Also, if the user is in an urban area, the medical department can consider cooperation with nearby medical institutions. Furthermore, if the user is traveling, the medical department can suggest medical treatment options at the user's destination. In this way, the optimal medical treatment method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the medical department is performed using, for example, a database or a medical treatment algorithm. For example, the medical department inputs the user's geographical location information into a medical treatment algorithm and executes a process to select the optimal medical treatment method.

[0096] During remote medical consultations, the medical department can analyze the user's social media activity and suggest medical treatment options. For example, the medical department retrieves the user's social media activity from a database and suggests medical treatment options based on that information. For example, the medical department can suggest appropriate medical treatment options based on health information shared by the user on social media. The medical department can also suggest medical treatment options based on information about health-related accounts the user follows on social media. Furthermore, the medical department can suggest medical treatment options based on information about health-related groups the user participates in on social media. In this way, appropriate medical treatment options can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the medical department is performed using, for example, a database or an analysis algorithm. For example, the medical department inputs the user's social media data into an analysis algorithm and executes a process to suggest medical treatment options. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, estimation unit, search unit, provision unit, and medical treatment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives symptoms input by the user. The estimation unit is realized by the identification processing unit 290 of the data processing device 12 and estimates the name of a disease based on the symptoms using a generation AI. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for nearby medical institutions based on the user's location information. The provision unit is realized by the output device 40 of the smart device 14 and provides the user with a list of the searched medical institutions. The medical treatment unit performs remote medical treatment via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, estimation unit, search unit, provision unit, and medical treatment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives symptoms input by the user. The estimation unit is realized by the identification processing unit 290 of the data processing device 12 and estimates the name of a disease based on the symptoms using a generation AI. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for nearby medical institutions based on the user's location information. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the user with a list of the searched medical institutions. The medical treatment unit performs remote medical treatment via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, search unit, provision unit, and medical department is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives symptoms input by the user. The estimation unit is realized by the identification processing unit 290 of the data processing device 12 and estimates the name of a disease based on the symptoms using a generation AI. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for nearby medical institutions based on the user's location information. The provision unit is realized by the display 343 of the headset-type terminal 314 and provides the user with a list of the searched medical institutions. The medical department performs remote medical care via the communication I / F 44 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, estimation unit, search unit, provision unit, and medical treatment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives symptoms input by the user. The estimation unit is realized by the identification processing unit 290 of the data processing device 12 and estimates the name of a disease based on the symptoms using a generation AI. The search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for nearby medical institutions based on the user's location information. The provision unit is realized by the speaker 240 of the robot 414 and provides the user with a list of the searched medical institutions. The medical treatment unit performs remote medical treatment via the communication I / F 44 of the robot 414.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can complement the symptom input based on the user's past medical history. For example, if the user has previously complained of a "headache," the reception unit references that history and automatically complements the past medical treatment details and treatment when the user inputs "headache" again. The reception unit can also suggest related symptoms based on the results of the user's past medical treatment. Furthermore, the reception unit can prioritize the display of specific doctors and medical institutions based on the user's past medical history. This makes it possible to efficiently input symptoms by utilizing the user's past medical history, and provide more appropriate medical services.

[0099] The estimation unit can improve the accuracy of disease name estimation based on the user's lifestyle habit data. For example, if the user is a smoker, the estimation unit will preferentially present disease names related to smoking. Also, if the user exercises regularly, disease names related to lack of exercise can be excluded. Furthermore, disease names related to nutritional deficiencies or overintake can also be estimated based on the user's dietary habit data. This makes it possible to estimate disease names with higher accuracy by utilizing the user's lifestyle habit data.

[0100] The search unit can search for medical institutions taking into account the user's medical insurance information. For example, if the user is subscribed to a specific insurance plan, medical institutions that support that insurance plan are preferentially displayed. The search unit can also suggest medical institutions with low out-of-pocket expenses based on the user's insurance coverage. Furthermore, the search unit can search for medical institutions that support specific treatments or medical specialties based on the user's insurance information. This allows for a more appropriate medical institution to be searched for by taking into account the user's medical insurance information.

[0101] The providing unit can display a list of medical institutions based on the user's language setting. For example, if the user selects English, medical institutions that speak English are displayed preferentially. The providing unit can also provide information about medical institutions in multiple languages ​​based on the user's language setting. Furthermore, the providing unit can translate and display ratings and reviews of medical institutions based on the user's language setting. This makes it possible to provide information according to the user's language setting, improving convenience.

[0102] The medical department can track the progress of medical treatment in real time based on the user's medical history. For example, the medical department can refer to the medical treatments and treatments the user has received in the past and compare them with the current treatment. The medical department can also evaluate the effectiveness of treatment based on the user's medical history and adjust the treatment plan as necessary. Furthermore, the medical department can automatically schedule the next medical treatment based on the user's medical history. This allows for more effective treatment by tracking the progress of medical treatment in real time.

[0103] The reception unit can estimate the user's emotions and provide symptom input support based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can encourage the user to input symptoms in gentle words. If the user is relaxed, the reception unit can provide detailed input options. Furthermore, if the user is in a hurry, the reception unit can suggest a simple input method. This enables input support according to the user's emotions, improving user convenience.

[0104] The estimation unit can estimate the user's emotions and adjust the accuracy of the disease name estimation based on the estimated user emotions. For example, if the user is feeling anxious, the estimation unit can quickly and concisely estimate the disease name. If the user is relaxed, the estimation unit can also estimate a detailed disease name. Furthermore, if the user is in a hurry, the estimation unit can prioritize the main disease name. This makes it possible to estimate the disease name according to the user's emotions, and more appropriate medical services can be provided.

[0105] The search unit can estimate the user's emotions and customize search results for medical institutions based on the estimated user's emotions. For example, if the user is feeling anxious, the search unit can prioritize displaying medical institutions that can respond quickly. Also, if the user is relaxed, the search unit can prioritize displaying medical institutions that provide detailed information. Furthermore, if the user is in a hurry, the search unit can prioritize displaying nearby medical institutions. This provides search results that correspond to the user's emotions, improving convenience.

[0106] The providing unit can estimate the user's emotions and adjust the display method of the list of medical institutions based on the estimated user's emotions. For example, if the user is feeling anxious, a simple, highly visible list can be displayed. If the user is relaxed, a list including detailed information can be displayed. Furthermore, if the user is in a hurry, a list displaying only the main information can be provided. This provides a list display method that corresponds to the user's emotions, improving convenience.

[0107] The medical department can estimate the user's emotions and adjust the method of remote medical treatment based on the estimated user's emotions. For example, if the user feels anxious, the doctor can provide medical treatment in a calm voice. If the user feels relaxed, the doctor can provide medical treatment with detailed explanations. Furthermore, if the user is in a hurry, the doctor can provide quick and concise medical treatment. This allows for the provision of a medical treatment method that suits the user's emotions, making it possible to provide more appropriate medical services.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives symptoms input by the user. For example, if the user inputs "headache," the reception unit receives the information. Step 2: The estimation unit uses the generation AI to estimate the name of the disease based on the symptoms received by the reception unit. For example, the estimation unit uses the generation AI to analyze the symptom "headache" and present possible disease names such as "migraine" or "tension headache." Step 3: The search unit searches for nearby medical institutions using the user's location information based on the disease name estimated by the estimation unit. For example, the search unit lists nearby hospitals and clinics based on the user's location information. Step 4: The providing unit provides the user with a list of medical institutions searched by the searching unit. For example, the providing unit displays a list of nearby medical institutions to the user, allowing the user to select one. Step 5: The medical department enables the user to receive remote medical treatment at the medical institution selected by the user. For example, the medical department collaborates with the medical institution selected by the user and conducts remote medical treatment via video call or chat.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] 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.

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

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

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

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

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

[0121] Fig. 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the 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.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0128] The data processing system 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.

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

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The 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.

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 that receives input of symptoms; an estimation unit that estimates a disease name based on the symptoms received by the reception unit; a search unit that searches for nearby medical institutions based on the disease name estimated by the estimation unit; a providing unit that provides the medical institutions searched by the searching unit to a user; a medical examination unit that receives remote medical treatment at the medical institution provided by the providing unit; A system characterized by:

2. The estimation unit Analyze symptoms using generative AI and predict the likely disease name The system of claim 1 .

3. The search unit Search for nearby medical institutions based on the user's location information The system of claim 1 .

4. The providing unit A list of medical institutions searched by the search unit is provided to the user. The system of claim 1 .

5. The medical department Receive remote medical treatment at a medical institution selected by the user The system of claim 1 .

6. The reception unit Estimating a user's emotion and adjusting a symptom input interface based on the estimated user's emotion The system of claim 1 .

7. The reception unit Analyzes the user's symptom input history and suggests appropriate input methods The system of claim 1 .

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

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