system

Abstract

The system according to this embodiment aims to provide medical consultations that support multiple languages. [Solution] The system according to this embodiment comprises a reception unit, a translation unit, a search unit, and a guidance unit. The reception unit receives input of symptoms and conditions from the user. The translation unit analyzes the information received by the reception unit, identifies the language, and performs translation. The search unit summarizes the symptoms based on the information translated by the translation unit and searches for appropriate medical personnel or hospitals. The guidance unit provides guidance on first aid based on the information retrieved by the search unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

[0001] The technology of this disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the acceptance system for foreign patients is not well established, and medical treatment problems due to language barriers are becoming prominent.

[0005] The system according to the embodiment aims to provide multilingual medical consultations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a translation unit, a search unit, and a guidance unit. The reception unit receives input of symptoms and conditions from the user. The translation unit analyzes the information received by the reception unit, identifies the language, and performs translation. The search unit summarizes the symptoms based on the information translated by the translation unit and searches for appropriate medical personnel or hospitals. The guidance unit provides guidance on first aid based on the information retrieved by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide medical consultations in multiple languages. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical consultation system according to an embodiment of the present invention is a system that provides multilingual medical consultations using generative AI. In the medical consultation system, the user inputs symptoms and circumstances, the generative AI analyzes the input information to identify the language, and performs translation. Furthermore, the generative AI summarizes the symptoms and searches for medical personnel or clinics that can provide appropriate examination and support. This system allows foreign patients to receive appropriate medical support without feeling a language barrier. For example, the user inputs symptoms and circumstances. For example, the user inputs symptoms in French such as "Elle a mal au ventre et a de la fievre depuis hier." This information is input to the generative AI. Next, the generative AI analyzes the input information to identify the language and performs translation. The generative AI analyzes the French sentence and translates it into Japanese such as "She has had a stomach ache and fever since yesterday." This allows medical professionals to accurately understand the patient's symptoms. Furthermore, the generative AI summarizes the symptoms and searches for medical personnel or clinics that can provide appropriate examination and support. For example, the generative AI extracts the keywords "stomach ache" and "fever" and searches for internal medicine doctors or clinics based on these. Furthermore, the system can also provide guidance on first aid. For example, it can suggest first aid methods such as "applying cold" and "drinking fluids." This system allows foreign patients to receive appropriate medical support without experiencing language barriers. For instance, when a French-speaking patient seeks medical attention at a Japanese hospital, the AI ​​can translate their symptoms and suggest appropriate medical personnel and clinics, enabling smooth medical care. Additionally, the AI's guidance on first aid allows patients to take appropriate action before receiving medical treatment. Thus, a multilingual medical consultation system using AI is an effective means of solving the problem of providing medical care to foreign patients and preparing medical institutions to accept them. As a result, the medical consultation system allows foreign patients to receive appropriate medical support without experiencing language barriers.

[0029] The medical consultation system according to this embodiment comprises a reception unit, a translation unit, a search unit, and a guidance unit. The reception unit receives input of symptoms and situations from the user. Symptoms and situations from the user include, but are not limited to, physical symptoms, mental conditions, and emergencies. The reception unit receives the symptoms and situations entered by the user in text format, for example. The reception unit can also receive the user's symptoms and situations using voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can also receive the user's symptoms and situations using image input. For example, the reception unit analyzes images taken by the user to identify the symptoms and situations. The translation unit uses generative AI to analyze the information received by the reception unit, identify the language, and perform translation. Translation is performed using, for example, a language identification algorithm or a translation engine, but is not limited to such examples. For example, the translation unit uses generative AI to translate French sentences into Japanese. The translation unit can also use generative AI to translate English sentences into Japanese. Furthermore, the translation unit can also translate Spanish sentences into Japanese using generative AI. For example, the translation unit can use generative AI to analyze a French sentence and translate it into Japanese such as "She has had a stomach ache and fever since yesterday." The search unit uses generative AI to summarize the symptoms based on the information translated by the translation unit and search for appropriate medical personnel or clinics. The search may be performed by extracting keyword symptoms and using them as a basis, but is not limited to such examples. For example, the search unit may extract the keywords "stomach ache" and "fever" and use them to search for internal medicine doctors or clinics. The search unit may also extract the keywords "headache" and "nausea" and use them to search for neurologists or clinics. The search unit may also extract the keywords "cough" and "sore throat" and use them to search for otolaryngologists or clinics. The guidance unit uses generative AI to provide first aid guidance based on the information retrieved by the search unit. The guidance may be performed by suggesting first aid methods, but is not limited to such examples. For example, the information desk might suggest first aid measures such as "cooling the area" or "drinking fluids."Furthermore, the guidance system can also suggest first aid methods such as "rest" or "consult a doctor." It can also suggest first aid methods such as "take medication" or "go to the hospital." This allows the medical consultation system, according to this embodiment, to receive the user's symptoms and situation in multiple languages ​​and provide appropriate medical support.

[0030] The reception desk accepts input of symptoms and situations from users. These symptoms and situations include, but are not limited to, physical symptoms, mental conditions, and emergencies. The reception desk accepts user-submitted symptoms and situations in text format. Specifically, users can input detailed information about their symptoms and situations through a dedicated web form or application. For example, they can input specific symptoms such as headaches, fever, and stomachaches, or mental conditions such as stress and anxiety. The reception desk can also accept user symptoms and situations using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. When a user dictates their symptoms using a smartphone or microphone, the voice is converted into text in real time. Furthermore, the reception desk can accept user symptoms and situations using image input. For example, the reception desk analyzes images taken by users to identify symptoms and situations. When a user uploads images of skin rashes or swelling, image analysis technology is used to identify the symptoms and provide appropriate information. This allows the reception desk to collect information quickly and accurately, enabling users to input symptoms and situations in a variety of ways. Furthermore, the reception department can centrally manage the collected information and process it efficiently in cooperation with other departments. For example, collected data can be stored on a cloud server and made accessible to the translation and search departments. The reception department can also encrypt data and control access to protect user privacy. This allows the reception department to collect data efficiently and effectively while gaining user trust, thereby improving the overall system performance.

[0031] The translation department uses generative AI to analyze information received by the reception department, identify the language, and perform translation. Translation is performed using, for example, language identification algorithms and translation engines, but is not limited to these examples. Specifically, the generative AI analyzes text or audio data entered by the user and identifies the language. For example, if the user enters in French, the generative AI recognizes the text as French and translates it into Japanese. The translation department uses generative AI to translate French sentences into Japanese. The translation department can also use generative AI to translate English sentences into Japanese. For example, if the user enters "I have a headache and a fever," the generative AI translates this into Japanese as "I have a headache and a fever." The translation department can also use generative AI to translate Spanish sentences into Japanese. For example, if the user enters "Tengo dolor de estomago y fiebre," the generative AI translates this into Japanese as "I have a stomach ache and a fever." Furthermore, the translation department can use generative AI to support multiple languages. For example, it can translate texts from various languages, such as German, Italian, and Chinese, into Japanese. This enables the translation department to realize a multilingual medical consultation system and provide appropriate medical support to users who speak different languages. Furthermore, the translation department can continuously learn to improve the accuracy of translations. For example, it can improve its translation algorithm based on user feedback to provide more natural and accurate translations. In addition, by using translation models specialized for technical and medical terminology, the translation department can achieve highly accurate translations in the medical field. This allows the translation department to accurately understand the user's symptoms and situation and build a foundation for providing appropriate medical support.

[0032] The search unit uses generative AI to summarize symptoms based on information translated by the translation unit and searches for appropriate medical professionals and clinics. The search is performed, for example, by extracting keyword information about symptoms and using it as a basis. Specifically, the generative AI extracts important keywords from the translated text and searches for appropriate medical resources based on them. For example, if a user enters the symptoms "stomach ache" and "fever," the generative AI extracts these keywords and searches for internal medicine doctors and clinics. The search unit can also extract the keywords "headache" and "nausea" and search for neurologists and clinics based on these. Furthermore, the search unit can extract the keywords "cough" and "sore throat" and search for otolaryngologists and clinics based on these. Using generative AI, the search unit can quickly identify the medical resources best suited to the user's symptoms. In addition, the search unit can suggest the optimal medical resources by considering detailed information such as location, clinic hours, and doctors' specialties. For example, based on the user's current location, the system can search for the nearest hospital or clinic and check its operating hours and appointment availability. The search function can also find specialists suited to the user's symptoms and provide information to ensure appropriate treatment. This allows the search function to play a crucial role in enabling users to receive prompt and appropriate medical support. Furthermore, the search function can continuously improve its search algorithm based on past search history and user feedback. This allows the search function to consistently provide highly accurate search results based on the latest information, thereby improving user satisfaction.

[0033] The guidance unit uses generative AI to provide first aid guidance based on information retrieved by the search unit. Guidance is provided, for example, by suggesting first aid methods, but is not limited to such examples. Specifically, the generative AI suggests the most appropriate first aid method according to the user's symptoms. For example, if the user says "cool down" or "drink fluids," the guidance unit may suggest first aid methods such as "rest" or "consult a doctor." For example, if the user complains of a headache, the generative AI may suggest first aid methods such as "rest" or "drink fluids." If the user complains of a fever, the generative AI may suggest first aid methods such as "cool down" or "consult a doctor." Furthermore, the guidance unit may also suggest first aid methods such as "take medicine" or "go to the hospital." For example, if the user complains of a stomach ache, the generative AI may suggest first aid methods such as "take medicine" or "go to the hospital." In this way, the guidance unit can provide information to enable the user to take quick and appropriate first aid. Furthermore, the guidance system can continuously improve its first-aid suggestions based on user feedback. For example, by providing feedback on the results of users implementing the suggested first aid, the generated AI can learn from this information and make more effective first-aid suggestions. The guidance system can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the guidance system to provide users with first-aid information quickly and reliably, minimizing the risk of disaster.

[0034] The reception desk can analyze the user's past medical history and provide the optimal input format. For example, the reception desk can automatically display relevant input fields based on symptoms and conditions previously entered by the user. The reception desk can also prioritize displaying input fields related to specific medical histories based on the user's past medical history. Furthermore, the reception desk can analyze the user's past medical history and propose the most efficient input format. This improves input efficiency by providing the optimal input format based on past medical history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past medical history data into a generating AI and have the generating AI propose the optimal input format.

[0035] The reception desk can customize input fields based on the user's current health status and lifestyle when they input symptoms or conditions. For example, the reception desk can prioritize displaying relevant input fields based on the user's current health status. It can also customize input fields considering the user's lifestyle (smoking, drinking, etc.). Furthermore, the reception desk can dynamically change input fields based on the user's current health status and lifestyle. This enables more accurate information collection by providing input fields that are tailored to the user's health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's health status data into a generating AI and have the generating AI perform the customization of input fields.

[0036] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location when the user enters symptoms or conditions. For example, the reception system can prioritize displaying input fields related to region-specific diseases or symptoms based on the user's current location. The reception system can also display input fields related to the nearest medical institution, taking the user's geographical location into consideration. Furthermore, the reception system can prioritize displaying input fields related to local medical resources based on the user's geographical location. This allows the system to address region-specific diseases and symptoms by providing input fields based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0037] The reception desk can analyze the user's social media activity when they input symptoms or conditions and suggest relevant input fields. For example, the reception desk can analyze the user's social media posts and suggest input fields based on relevant symptoms or conditions. The reception desk can also suggest input fields related to specific health problems based on the user's social media activity. Furthermore, the reception desk can suggest input fields related to the user's current health status and lifestyle based on the user's social media activity. This allows for the collection of more relevant information by providing input fields based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant input fields.

[0038] The translation unit can adjust the level of detail in translations based on the importance of medical terms. For example, the translation unit can provide detailed translations for important medical terms, and concise translations for common medical terms. Furthermore, the translation unit can dynamically adjust the level of detail based on the importance of medical terms. This allows for more accurate information transmission by providing translations tailored to the importance of medical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input medical term importance data into a generating AI and have the generating AI adjust the level of detail in the translations.

[0039] The translation unit can apply different translation algorithms depending on regional differences in language during translation. For example, the translation unit can consider the differences between European French and Canadian French when translating. It can also consider the differences between American English and British English. Furthermore, it can consider the differences between Spanish spoken in Spain and Spanish spoken in Latin America when translating. This allows for more appropriate information transmission by providing translations that are tailored to regional differences. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input regional difference data into a generating AI and have the generating AI apply different translation algorithms.

[0040] The translation unit can determine translation priorities based on the submission timing of the input information during the translation process. For example, the translation unit can prioritize the translation of information that is urgent. It can also prioritize the translation of information that has an early submission date. Furthermore, the translation unit can dynamically adjust the translation priority based on the submission date. This allows for the rapid translation of urgent information by providing translation priorities based on the submission date. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input submission date data into a generating AI and have the generating AI determine the translation priority.

[0041] The translation unit can adjust the translation order based on the relevance of the input information during translation. For example, the translation unit can prioritize translating important information. It can also prioritize translating highly relevant information. Furthermore, the translation unit can dynamically adjust the translation order based on the relevance of the input information. This allows important information to be translated preferentially by providing a translation order based on relevance. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input relevance data of the input information into a generating AI and have the generating AI perform the adjustment of the translation order.

[0042] The search unit can improve the accuracy of searches by considering the interrelationships between symptoms. For example, if multiple symptoms are related, the search unit will provide search results that take these relationships into account. The search unit can also search for the most suitable medical personnel or hospitals based on the interrelationships between symptoms. Furthermore, the search unit can improve the accuracy of search results by considering the interrelationships between symptoms. This allows for the search of more appropriate medical personnel or hospitals by providing searches that take the interrelationships between symptoms into account. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the interrelationship data of symptoms into a generating AI and have the generating AI perform the search accuracy improvement.

[0043] The search unit can perform searches while considering the attribute information of healthcare professionals. For example, the search unit can search for the most suitable healthcare professionals by considering their area of ​​expertise. The search unit can also search for the most suitable healthcare professionals by considering their years of experience. Furthermore, the search unit can search for the most suitable healthcare professionals based on their attribute information. This allows for the search of more appropriate healthcare professionals by providing a search based on the attribute information of healthcare professionals. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input healthcare professional attribute information data into a generating AI and have the generating AI perform the search.

[0044] The search unit can perform searches while considering the geographical distribution of medical institutions. For example, the search unit can search for the nearest medical institution based on the user's current location. The search unit can also search for the optimal medical institution by considering the geographical distribution of medical institutions. Furthermore, the search unit can search for the optimal medical institution based on the user's geographical location information. This allows for quick searching of the nearest medical institution by providing a search based on geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of medical institutions into a generating AI and have the generating AI perform the search.

[0045] The search unit can improve the accuracy of its search by referring to related literature during the search process. For example, the search unit can search for the most suitable medical personnel or hospitals based on related literature. The search unit can also improve the accuracy of its search results by referring to related literature. Furthermore, the search unit can supplement its search results based on related literature. This improves the accuracy of search results by referring to related literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI and have the generating AI perform the search accuracy improvement.

[0046] The guidance unit can select the optimal guidance method by referring to past first aid data during guidance. For example, the guidance unit can propose the optimal first aid method based on past first aid data. The guidance unit can also select the most effective first aid method by referring to past first aid data. Furthermore, the guidance unit can propose the optimal first aid method to the user based on past first aid data. This enables more effective first aid by providing guidance methods based on past first aid data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past first aid data into a generating AI and have the generating AI select the optimal guidance method.

[0047] The guidance unit can apply different guidance methods depending on the symptom category during guidance. For example, the guidance unit can suggest the most appropriate first aid method depending on the symptom category. The guidance unit can also apply different guidance methods based on the symptom category. Furthermore, the guidance unit can dynamically change the first aid guidance method depending on the symptom category. This enables more appropriate first aid by providing guidance methods according to the symptom category. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input symptom category data into a generating AI and have the generating AI execute the application of guidance methods.

[0048] The guidance unit can adjust the order of first aid treatments based on the timing of symptom onset during guidance. For example, the guidance unit can suggest the optimal order of first aid treatments based on the timing of symptom onset. The guidance unit can also dynamically adjust the order of first aid treatments, taking into account the timing of symptom onset. Furthermore, the guidance unit can determine the priority of first aid treatments based on the timing of symptom onset. This enables more appropriate first aid treatment by providing an order of first aid treatments based on the timing of symptom onset. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input symptom onset timing data into a generating AI and have the generating AI perform the adjustment of the order of first aid treatments.

[0049] The guidance unit can improve the accuracy of its guidance by referring to relevant medical guidelines during the guidance process. For example, the guidance unit can suggest the most appropriate first aid method based on relevant medical guidelines. The guidance unit can also improve the accuracy of first aid guidance by referring to medical guidelines. Furthermore, the guidance unit can supplement first aid methods based on relevant medical guidelines. This enables more accurate first aid by providing guidance based on medical guidelines. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input medical guideline data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

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

[0051] The reception desk can automatically determine the urgency of the symptoms based on the user's input and suggest appropriate actions. For example, if the symptoms entered by the user are highly urgent, such as "chest pain" or "difficulty breathing," the reception desk will immediately advise the user to call an ambulance. If the symptoms entered by the user are less urgent, such as "mild headache" or "mild cough," the reception desk can also suggest ways to manage the situation at home. Furthermore, depending on the urgency of the symptoms, the reception desk can recommend visiting a medical institution. This allows users to quickly take appropriate action according to their symptoms.

[0052] The reception desk can analyze a user's past medical history, automatically extract risk factors related to specific medical histories, and reflect them in the input. For example, if a user has a history of heart disease, the reception desk will prioritize displaying risk factors related to heart disease and prompt the user to enter them. Similarly, if a user has a history of diabetes, the reception desk can display risk factors related to diabetes and complete the input. Furthermore, it can suggest preventive measures related to specific medical histories based on the user's past medical history. This enables the extraction of risk factors and completion of input based on past medical history.

[0053] The reception desk can dynamically adjust the priority of input content based on the user's current health status and lifestyle. For example, if the user is a smoker, the reception desk will prioritize displaying symptoms and risk factors related to smoking and prompting the user to input this information. Similarly, if the user has high blood pressure, the reception desk can prioritize displaying symptoms and risk factors related to high blood pressure. Furthermore, it can suggest preventive measures related to specific health risks based on the user's lifestyle. By providing input content priorities tailored to the user's health status and lifestyle, more accurate information can be collected.

[0054] The reception desk can automatically display input fields related to region-specific health risks based on the user's geographical location. For example, if the user lives in a tropical region, the reception desk will prioritize displaying symptoms and risk factors related to tropical diseases and prompting the user to input this information. Similarly, if the user lives in a cold region, the reception desk can display input fields related to health risks specific to cold climates. Furthermore, it can provide information related to local medical resources based on the user's geographical location. This enables the display of input fields based on geographical location information, allowing for the addressing of region-specific health risks.

[0055] The reception desk can analyze users' social media activity and suggest input fields based on health-related trends and topics. For example, if a user frequently posts about "flu" on social media, the reception desk will prioritize displaying flu-related symptoms and preventative measures to encourage input. Similarly, if a user posts about "dieting," the reception desk can display health risks and nutritional information related to dieting. Furthermore, it can provide information related to specific health topics based on the user's social media activity. This enables the suggestion of input fields based on social media activity, allowing for the collection of more relevant information.

[0056] The translation department can dynamically adjust the level of detail in translations based on the specialization of medical terminology. For example, it can provide detailed explanations for specialized medical terms and concise translations for common medical terms. Furthermore, it can provide supplementary information to improve translation accuracy, depending on the specialization of the medical terminology. This allows for more accurate information transmission by providing translations with levels of detail appropriate to the specialization of medical terminology.

[0057] The translation department can provide more appropriate translations by considering the cultural background and nuances of the language during the translation process. For example, the translation department can select appropriate expressions considering the cultural background of French. It can also provide more natural translations by considering the cultural nuances of English. Furthermore, it can provide appropriate translations by considering regional differences in Spanish. This allows for more accurate information transmission by providing translations based on the cultural background and nuances of the language.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives input from the user regarding their symptoms and situation. These symptoms and situations may include physical symptoms, mental conditions, and emergencies. The reception desk accepts the user's symptoms and situation using text, voice input, and image input. For example, it may use speech recognition technology to convert speech to text or image analysis technology to identify symptoms from images. Step 2: The translation department uses a generative AI to analyze the information received by the reception department, identify the language, and perform the translation. Translation is carried out using language identification algorithms and translation engines. For example, it translates French, English, and Spanish texts into Japanese. Step 3: The search unit uses generation AI to summarize the symptoms based on the information translated by the translation unit and searches for appropriate medical personnel or clinics. The search is performed by extracting keywords related to the symptoms. For example, it extracts the keywords "abdominal pain" and "fever" and searches for internal medicine doctors or clinics. Step 4: The guidance unit uses generation AI to provide first aid guidance based on the information retrieved by the search unit. Guidance is provided by suggesting first aid methods. For example, it may suggest first aid methods such as "apply cold," "drink fluids," "rest," "consult a doctor," "take medicine," or "go to the hospital."

[0060] (Example of form 2) The medical consultation system according to an embodiment of the present invention is a system that provides multilingual medical consultations using generative AI. In the medical consultation system, the user inputs symptoms and circumstances, the generative AI analyzes the input information to identify the language, and performs translation. Furthermore, the generative AI summarizes the symptoms and searches for medical personnel or clinics that can provide appropriate examination and support. This system allows foreign patients to receive appropriate medical support without feeling a language barrier. For example, the user inputs symptoms and circumstances. For example, the user inputs symptoms in French such as "Elle a mal au ventre et a de la fievre depuis hier." This information is input to the generative AI. Next, the generative AI analyzes the input information to identify the language and performs translation. The generative AI analyzes the French sentence and translates it into Japanese such as "She has had a stomach ache and fever since yesterday." This allows medical professionals to accurately understand the patient's symptoms. Furthermore, the generative AI summarizes the symptoms and searches for medical personnel or clinics that can provide appropriate examination and support. For example, the generative AI extracts the keywords "stomach ache" and "fever" and searches for internal medicine doctors or clinics based on these. Furthermore, the system can also provide guidance on first aid. For example, it can suggest first aid methods such as "applying cold" and "drinking fluids." This system allows foreign patients to receive appropriate medical support without experiencing language barriers. For instance, when a French-speaking patient seeks medical attention at a Japanese hospital, the AI ​​can translate their symptoms and suggest appropriate medical personnel and clinics, enabling smooth medical care. Additionally, the AI's guidance on first aid allows patients to take appropriate action before receiving medical treatment. Thus, a multilingual medical consultation system using AI is an effective means of solving the problem of providing medical care to foreign patients and preparing medical institutions to accept them. As a result, the medical consultation system allows foreign patients to receive appropriate medical support without experiencing language barriers.

[0061] The medical consultation system according to this embodiment comprises a reception unit, a translation unit, a search unit, and a guidance unit. The reception unit receives input of symptoms and situations from the user. Symptoms and situations from the user include, but are not limited to, physical symptoms, mental conditions, and emergencies. The reception unit receives the symptoms and situations entered by the user in text format, for example. The reception unit can also receive the user's symptoms and situations using voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can also receive the user's symptoms and situations using image input. For example, the reception unit analyzes images taken by the user to identify the symptoms and situations. The translation unit uses generative AI to analyze the information received by the reception unit, identify the language, and perform translation. Translation is performed using, for example, a language identification algorithm or a translation engine, but is not limited to such examples. For example, the translation unit uses generative AI to translate French sentences into Japanese. The translation unit can also use generative AI to translate English sentences into Japanese. Furthermore, the translation unit can also translate Spanish sentences into Japanese using generative AI. For example, the translation unit can use generative AI to analyze a French sentence and translate it into Japanese such as "She has had a stomach ache and fever since yesterday." The search unit uses generative AI to summarize the symptoms based on the information translated by the translation unit and search for appropriate medical personnel or clinics. The search may be performed by extracting keyword symptoms and using them as a basis, but is not limited to such examples. For example, the search unit may extract the keywords "stomach ache" and "fever" and use them to search for internal medicine doctors or clinics. The search unit may also extract the keywords "headache" and "nausea" and use them to search for neurologists or clinics. The search unit may also extract the keywords "cough" and "sore throat" and use them to search for otolaryngologists or clinics. The guidance unit uses generative AI to provide first aid guidance based on the information retrieved by the search unit. The guidance may be performed by suggesting first aid methods, but is not limited to such examples. For example, the information desk might suggest first aid measures such as "cooling the area" or "drinking fluids."Furthermore, the guidance system can also suggest first aid methods such as "rest" or "consult a doctor." It can also suggest first aid methods such as "take medication" or "go to the hospital." This allows the medical consultation system, according to this embodiment, to receive the user's symptoms and situation in multiple languages ​​and provide appropriate medical support.

[0062] The reception desk accepts input of symptoms and situations from users. These symptoms and situations include, but are not limited to, physical symptoms, mental conditions, and emergencies. The reception desk accepts user-submitted symptoms and situations in text format. Specifically, users can input detailed information about their symptoms and situations through a dedicated web form or application. For example, they can input specific symptoms such as headaches, fever, and stomachaches, or mental conditions such as stress and anxiety. The reception desk can also accept user symptoms and situations using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. When a user dictates their symptoms using a smartphone or microphone, the voice is converted into text in real time. Furthermore, the reception desk can accept user symptoms and situations using image input. For example, the reception desk analyzes images taken by users to identify symptoms and situations. When a user uploads images of skin rashes or swelling, image analysis technology is used to identify the symptoms and provide appropriate information. This allows the reception desk to collect information quickly and accurately, enabling users to input symptoms and situations in a variety of ways. Furthermore, the reception department can centrally manage the collected information and process it efficiently in cooperation with other departments. For example, collected data can be stored on a cloud server and made accessible to the translation and search departments. The reception department can also encrypt data and control access to protect user privacy. This allows the reception department to collect data efficiently and effectively while gaining user trust, thereby improving the overall system performance.

[0063] The translation department uses generative AI to analyze information received by the reception department, identify the language, and perform translation. Translation is performed using, for example, language identification algorithms and translation engines, but is not limited to these examples. Specifically, the generative AI analyzes text or audio data entered by the user and identifies the language. For example, if the user enters in French, the generative AI recognizes the text as French and translates it into Japanese. The translation department uses generative AI to translate French sentences into Japanese. The translation department can also use generative AI to translate English sentences into Japanese. For example, if the user enters "I have a headache and a fever," the generative AI translates this into Japanese as "I have a headache and a fever." The translation department can also use generative AI to translate Spanish sentences into Japanese. For example, if the user enters "Tengo dolor de estomago y fiebre," the generative AI translates this into Japanese as "I have a stomach ache and a fever." Furthermore, the translation department can use generative AI to support multiple languages. For example, it can translate texts from various languages, such as German, Italian, and Chinese, into Japanese. This enables the translation department to realize a multilingual medical consultation system and provide appropriate medical support to users who speak different languages. Furthermore, the translation department can continuously learn to improve the accuracy of translations. For example, it can improve its translation algorithm based on user feedback to provide more natural and accurate translations. In addition, by using translation models specialized for technical and medical terminology, the translation department can achieve highly accurate translations in the medical field. This allows the translation department to accurately understand the user's symptoms and situation and build a foundation for providing appropriate medical support.

[0064] The search unit uses generative AI to summarize symptoms based on information translated by the translation unit and searches for appropriate medical professionals and clinics. The search is performed, for example, by extracting keyword information about symptoms and using it as a basis. Specifically, the generative AI extracts important keywords from the translated text and searches for appropriate medical resources based on them. For example, if a user enters the symptoms "stomach ache" and "fever," the generative AI extracts these keywords and searches for internal medicine doctors and clinics. The search unit can also extract the keywords "headache" and "nausea" and search for neurologists and clinics based on these. Furthermore, the search unit can extract the keywords "cough" and "sore throat" and search for otolaryngologists and clinics based on these. Using generative AI, the search unit can quickly identify the medical resources best suited to the user's symptoms. In addition, the search unit can suggest the optimal medical resources by considering detailed information such as location, clinic hours, and doctors' specialties. For example, based on the user's current location, the system can search for the nearest hospital or clinic and check its operating hours and appointment availability. The search function can also find specialists suited to the user's symptoms and provide information to ensure appropriate treatment. This allows the search function to play a crucial role in enabling users to receive prompt and appropriate medical support. Furthermore, the search function can continuously improve its search algorithm based on past search history and user feedback. This allows the search function to consistently provide highly accurate search results based on the latest information, thereby improving user satisfaction.

[0065] The guidance unit uses generative AI to provide first aid guidance based on information retrieved by the search unit. Guidance is provided, for example, by suggesting first aid methods, but is not limited to such examples. Specifically, the generative AI suggests the most appropriate first aid method according to the user's symptoms. For example, if the user says "cool down" or "drink fluids," the guidance unit may suggest first aid methods such as "rest" or "consult a doctor." For example, if the user complains of a headache, the generative AI may suggest first aid methods such as "rest" or "drink fluids." If the user complains of a fever, the generative AI may suggest first aid methods such as "cool down" or "consult a doctor." Furthermore, the guidance unit may also suggest first aid methods such as "take medicine" or "go to the hospital." For example, if the user complains of a stomach ache, the generative AI may suggest first aid methods such as "take medicine" or "go to the hospital." In this way, the guidance unit can provide information to enable the user to take quick and appropriate first aid. Furthermore, the guidance system can continuously improve its first-aid suggestions based on user feedback. For example, by providing feedback on the results of users implementing the suggested first aid, the generated AI can learn from this information and make more effective first-aid suggestions. The guidance system can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the guidance system to provide users with first-aid information quickly and reliably, minimizing the risk of disaster.

[0066] The reception desk can estimate the user's emotions and adjust the input method for symptoms and situations based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and easy-to-understand input form and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of symptoms and situations. This enables more appropriate information gathering by providing input methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The reception desk can analyze the user's past medical history and provide the optimal input format. For example, the reception desk can automatically display relevant input fields based on symptoms and conditions previously entered by the user. The reception desk can also prioritize displaying input fields related to specific medical histories based on the user's past medical history. Furthermore, the reception desk can analyze the user's past medical history and propose the most efficient input format. This improves input efficiency by providing the optimal input format based on past medical history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past medical history data into a generating AI and have the generating AI propose the optimal input format.

[0068] The reception desk can customize input fields based on the user's current health status and lifestyle when they input symptoms or conditions. For example, the reception desk can prioritize displaying relevant input fields based on the user's current health status. It can also customize input fields considering the user's lifestyle (smoking, drinking, etc.). Furthermore, the reception desk can dynamically change input fields based on the user's current health status and lifestyle. This enables more accurate information collection by providing input fields that are tailored to the user's health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's health status data into a generating AI and have the generating AI perform the customization of input fields.

[0069] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is feeling anxious, the reception desk can prioritize displaying important input fields to allow for quick completion. If the user is relaxed, the reception desk can provide more detailed input fields to enrich the input. If the user is in a hurry, the reception desk can prioritize displaying the most important input fields to allow for quick completion. This allows for the rapid collection of important information by providing input priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location when the user enters symptoms or conditions. For example, the reception system can prioritize displaying input fields related to region-specific diseases or symptoms based on the user's current location. The reception system can also display input fields related to the nearest medical institution, taking the user's geographical location into consideration. Furthermore, the reception system can prioritize displaying input fields related to local medical resources based on the user's geographical location. This allows the system to address region-specific diseases and symptoms by providing input fields based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0071] The reception desk can analyze the user's social media activity when they input symptoms or conditions and suggest relevant input fields. For example, the reception desk can analyze the user's social media posts and suggest input fields based on relevant symptoms or conditions. The reception desk can also suggest input fields related to specific health problems based on the user's social media activity. Furthermore, the reception desk can suggest input fields related to the user's current health status and lifestyle based on the user's social media activity. This allows for the collection of more relevant information by providing input fields based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant input fields.

[0072] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is feeling anxious, the translation unit will provide a concise and easy-to-understand translation. If the user is relaxed, the translation unit can provide a more detailed and informative translation. If the user is in a hurry, the translation unit can provide a quick and concise translation. This allows for more appropriate information transmission by providing translation expressions that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI or not. For example, the translation unit can input the user's text data into the generative AI and have the generative AI adjust the translation's expression.

[0073] The translation unit can adjust the level of detail in translations based on the importance of medical terms. For example, the translation unit can provide detailed translations for important medical terms, and concise translations for common medical terms. Furthermore, the translation unit can dynamically adjust the level of detail based on the importance of medical terms. This allows for more accurate information transmission by providing translations tailored to the importance of medical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input medical term importance data into a generating AI and have the generating AI adjust the level of detail in the translations.

[0074] The translation unit can apply different translation algorithms depending on regional differences in language during translation. For example, the translation unit can consider the differences between European French and Canadian French when translating. It can also consider the differences between American English and British English. Furthermore, it can consider the differences between Spanish spoken in Spain and Spanish spoken in Latin America when translating. This allows for more appropriate information transmission by providing translations that are tailored to regional differences. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input regional difference data into a generating AI and have the generating AI apply different translation algorithms.

[0075] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is feeling anxious, the translation unit will provide a concise and to-the-point translation. If the user is relaxed, the translation unit may provide a longer translation with more detailed explanations. If the user is in a hurry, the translation unit may provide a quick and concise translation. By providing translation lengths that match the user's emotions, more appropriate information can be conveyed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not using AI. For example, the translation unit can input user emotion data into the generative AI and have the generative AI adjust the translation length.

[0076] The translation unit can determine translation priorities based on the submission timing of the input information during the translation process. For example, the translation unit can prioritize the translation of information that is urgent. It can also prioritize the translation of information that has an early submission date. Furthermore, the translation unit can dynamically adjust the translation priority based on the submission date. This allows for the rapid translation of urgent information by providing translation priorities based on the submission date. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input submission date data into a generating AI and have the generating AI determine the translation priority.

[0077] The translation unit can adjust the translation order based on the relevance of the input information during translation. For example, the translation unit can prioritize translating important information. It can also prioritize translating highly relevant information. Furthermore, the translation unit can dynamically adjust the translation order based on the relevance of the input information. This allows important information to be translated preferentially by providing a translation order based on relevance. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input relevance data of the input information into a generating AI and have the generating AI perform the adjustment of the translation order.

[0078] The search unit can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is feeling anxious, the search unit can provide a simple and highly visible display. If the user is relaxed, the search unit can also provide a display that includes detailed information. If the user is in a hurry, the search unit can provide a concise display. By providing search results that are tailored to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0079] The search unit can improve the accuracy of searches by considering the interrelationships between symptoms. For example, if multiple symptoms are related, the search unit will provide search results that take these relationships into account. The search unit can also search for the most suitable medical personnel or hospitals based on the interrelationships between symptoms. Furthermore, the search unit can improve the accuracy of search results by considering the interrelationships between symptoms. This allows for the search of more appropriate medical personnel or hospitals by providing searches that take the interrelationships between symptoms into account. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the interrelationship data of symptoms into a generating AI and have the generating AI perform the search accuracy improvement.

[0080] The search unit can perform searches while considering the attribute information of healthcare professionals. For example, the search unit can search for the most suitable healthcare professionals by considering their area of ​​expertise. The search unit can also search for the most suitable healthcare professionals by considering their years of experience. Furthermore, the search unit can search for the most suitable healthcare professionals based on their attribute information. This allows for the search of more appropriate healthcare professionals by providing a search based on the attribute information of healthcare professionals. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input healthcare professional attribute information data into a generating AI and have the generating AI perform the search.

[0081] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is feeling anxious, the search unit will prioritize displaying important information. It can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize displaying concise information. This allows for the prioritization of important information by providing a search result display order tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the display order of search results.

[0082] The search unit can perform searches while considering the geographical distribution of medical institutions. For example, the search unit can search for the nearest medical institution based on the user's current location. The search unit can also search for the optimal medical institution by considering the geographical distribution of medical institutions. Furthermore, the search unit can search for the optimal medical institution based on the user's geographical location information. This allows for quick searching of the nearest medical institution by providing a search based on geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of medical institutions into a generating AI and have the generating AI perform the search.

[0083] The search unit can improve the accuracy of its search by referring to related literature during the search process. For example, the search unit can search for the most suitable medical personnel or hospitals based on related literature. The search unit can also improve the accuracy of its search results by referring to related literature. Furthermore, the search unit can supplement its search results based on related literature. This improves the accuracy of search results by referring to related literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI and have the generating AI perform the search accuracy improvement.

[0084] The guidance unit can estimate the user's emotions and adjust the first aid guidance method based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit can provide concise and easy-to-understand first aid guidance. If the user is relaxed, the guidance unit can also provide detailed first aid guidance. If the user is in a hurry, the guidance unit can also provide quick and concise first aid guidance. This allows for more appropriate first aid by providing first aid guidance methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI adjust the first aid guidance method.

[0085] The guidance unit can select the optimal guidance method by referring to past first aid data during guidance. For example, the guidance unit can propose the optimal first aid method based on past first aid data. The guidance unit can also select the most effective first aid method by referring to past first aid data. Furthermore, the guidance unit can propose the optimal first aid method to the user based on past first aid data. This enables more effective first aid by providing guidance methods based on past first aid data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past first aid data into a generating AI and have the generating AI select the optimal guidance method.

[0086] The guidance unit can apply different guidance methods depending on the symptom category during guidance. For example, the guidance unit can suggest the most appropriate first aid method depending on the symptom category. The guidance unit can also apply different guidance methods based on the symptom category. Furthermore, the guidance unit can dynamically change the first aid guidance method depending on the symptom category. This enables more appropriate first aid by providing guidance methods according to the symptom category. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input symptom category data into a generating AI and have the generating AI execute the application of guidance methods.

[0087] The guidance unit can estimate the user's emotions and determine the priority of first aid based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit will prioritize guiding the user to important first aid. If the user is relaxed, the guidance unit can also guide the user to detailed first aid. If the user is in a hurry, the guidance unit can also prioritize guiding the user to the most important first aid. This allows for quick guidance of important first aid by providing a priority of first aid according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI determine the priority of first aid.

[0088] The guidance unit can adjust the order of first aid treatments based on the timing of symptom onset during guidance. For example, the guidance unit can suggest the optimal order of first aid treatments based on the timing of symptom onset. The guidance unit can also dynamically adjust the order of first aid treatments, taking into account the timing of symptom onset. Furthermore, the guidance unit can determine the priority of first aid treatments based on the timing of symptom onset. This enables more appropriate first aid treatment by providing an order of first aid treatments based on the timing of symptom onset. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input symptom onset timing data into a generating AI and have the generating AI perform the adjustment of the order of first aid treatments.

[0089] The guidance unit can improve the accuracy of its guidance by referring to relevant medical guidelines during the guidance process. For example, the guidance unit can suggest the most appropriate first aid method based on relevant medical guidelines. The guidance unit can also improve the accuracy of first aid guidance by referring to medical guidelines. Furthermore, the guidance unit can supplement first aid methods based on relevant medical guidelines. This enables more accurate first aid by providing guidance based on medical guidelines. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input medical guideline data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can automatically determine the urgency of the symptoms based on the user's input and suggest appropriate actions. For example, if the symptoms entered by the user are highly urgent, such as "chest pain" or "difficulty breathing," the reception desk will immediately advise the user to call an ambulance. If the symptoms entered by the user are less urgent, such as "mild headache" or "mild cough," the reception desk can also suggest ways to manage the situation at home. Furthermore, depending on the urgency of the symptoms, the reception desk can recommend visiting a medical institution. This allows users to quickly take appropriate action according to their symptoms.

[0092] The reception desk can estimate the user's emotions and adjust the way it verifies input based on those emotions. For example, if the user is feeling anxious, the reception desk will carefully review the input and provide support to help the user feel at ease. If the user is relaxed, the reception desk can provide a concise verification method to allow them to quickly move on to the next step. Furthermore, if the user is in a hurry, the reception desk can review only the most important input and process it quickly. By providing verification methods tailored to the user's emotions, this enables smoother information gathering.

[0093] The reception desk can analyze a user's past medical history, automatically extract risk factors related to specific medical histories, and reflect them in the input. For example, if a user has a history of heart disease, the reception desk will prioritize displaying risk factors related to heart disease and prompt the user to enter them. Similarly, if a user has a history of diabetes, the reception desk can display risk factors related to diabetes and complete the input. Furthermore, it can suggest preventive measures related to specific medical histories based on the user's past medical history. This enables the extraction of risk factors and completion of input based on past medical history.

[0094] The reception desk can dynamically adjust the priority of input content based on the user's current health status and lifestyle. For example, if the user is a smoker, the reception desk will prioritize displaying symptoms and risk factors related to smoking and prompting the user to input this information. Similarly, if the user has high blood pressure, the reception desk can prioritize displaying symptoms and risk factors related to high blood pressure. Furthermore, it can suggest preventive measures related to specific health risks based on the user's lifestyle. By providing input content priorities tailored to the user's health status and lifestyle, more accurate information can be collected.

[0095] The reception desk can estimate the user's emotions and adjust the feedback method for the input based on those emotions. For example, if the user is feeling anxious, the reception desk can provide careful feedback on the input to help the user feel at ease. If the user is relaxed, the reception desk can provide concise feedback to allow them to quickly move on to the next step. Furthermore, if the user is in a hurry, the reception desk can provide feedback only on the most important input to expedite processing. This allows for smoother information gathering by providing feedback methods tailored to the user's emotions.

[0096] The reception desk can automatically display input fields related to region-specific health risks based on the user's geographical location. For example, if the user lives in a tropical region, the reception desk will prioritize displaying symptoms and risk factors related to tropical diseases and prompting the user to input this information. Similarly, if the user lives in a cold region, the reception desk can display input fields related to health risks specific to cold climates. Furthermore, it can provide information related to local medical resources based on the user's geographical location. This enables the display of input fields based on geographical location information, allowing for the addressing of region-specific health risks.

[0097] The reception desk can analyze users' social media activity and suggest input fields based on health-related trends and topics. For example, if a user frequently posts about "flu" on social media, the reception desk will prioritize displaying flu-related symptoms and preventative measures to encourage input. Similarly, if a user posts about "dieting," the reception desk can display health risks and nutritional information related to dieting. Furthermore, it can provide information related to specific health topics based on the user's social media activity. This enables the suggestion of input fields based on social media activity, allowing for the collection of more relevant information.

[0098] The translation unit can estimate the user's emotions and adjust the tone and style of the translation based on those estimates. For example, if the user is feeling anxious, the translation unit will provide a reassuring translation in a gentle tone. If the user is relaxed, the translation unit can provide a friendly translation in a casual tone. Furthermore, if the user is in a hurry, the translation unit can provide a concise and to-the-point translation. By providing a translation tone and style that matches the user's emotions, more appropriate information can be conveyed.

[0099] The translation department can dynamically adjust the level of detail in translations based on the specialization of medical terminology. For example, it can provide detailed explanations for specialized medical terms and concise translations for common medical terms. Furthermore, it can provide supplementary information to improve translation accuracy, depending on the specialization of the medical terminology. This allows for more accurate information transmission by providing translations with levels of detail appropriate to the specialization of medical terminology.

[0100] The translation department can provide more appropriate translations by considering the cultural background and nuances of the language during the translation process. For example, the translation department can select appropriate expressions considering the cultural background of French. It can also provide more natural translations by considering the cultural nuances of English. Furthermore, it can provide appropriate translations by considering regional differences in Spanish. This allows for more accurate information transmission by providing translations based on the cultural background and nuances of the language.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives input from the user regarding their symptoms and situation. These symptoms and situations may include physical symptoms, mental conditions, and emergencies. The reception desk accepts the user's symptoms and situation using text, voice input, and image input. For example, it may use speech recognition technology to convert speech to text or image analysis technology to identify symptoms from images. Step 2: The translation department uses a generative AI to analyze the information received by the reception department, identify the language, and perform the translation. Translation is carried out using language identification algorithms and translation engines. For example, it translates French, English, and Spanish texts into Japanese. Step 3: The search unit uses generation AI to summarize the symptoms based on the information translated by the translation unit and searches for appropriate medical personnel or clinics. The search is performed by extracting keywords related to the symptoms. For example, it extracts the keywords "abdominal pain" and "fever" and searches for internal medicine doctors or clinics. Step 4: The guidance unit uses generation AI to provide first aid guidance based on the information retrieved by the search unit. Guidance is provided by suggesting first aid methods. For example, it may suggest first aid methods such as "apply cold," "drink fluids," "rest," "consult a doctor," "take medicine," or "go to the hospital."

[0103] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0106] Each of the multiple elements described above, including the reception unit, translation unit, search unit, and guidance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input of symptoms and conditions from the user. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information using a generation AI, identifies the language, and performs translation. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the symptoms based on the translated information and searches for appropriate medical personnel or hospitals. The guidance unit is implemented by the output device 40 of the smart device 14 and provides guidance on first aid based on the retrieved information. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, translation unit, search unit, and guidance unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of symptoms and conditions from the user. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information using a generation AI, identifies the language, and performs translation. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the symptoms based on the translated information and searches for appropriate medical personnel or hospitals. The guidance unit is implemented by the speaker 240 of the smart glasses 214 and provides guidance on first aid based on the searched information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, translation unit, search unit, and guidance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input of symptoms and conditions from the user. The translation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the received information using a generation AI, identifies the language, and performs translation. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and summarizes the symptoms based on the translated information and searches for appropriate medical personnel or hospitals. The guidance unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides guidance on first aid based on the searched information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, translation unit, search unit, and guidance unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input of symptoms and conditions from the user. The translation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the received information using a generation AI, identifies the language, and performs translation. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and summarizes the symptoms based on the translated information and searches for appropriate medical personnel or hospitals. The guidance unit is implemented by, for example, the speaker 240 of the robot 414 and provides guidance on first aid based on the retrieved information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A reception area that receives input from users regarding symptoms and conditions, The translation unit analyzes the information received by the reception unit, identifies the language, and performs the translation. A search unit summarizes the symptoms based on the information translated by the aforementioned translation unit and searches for appropriate medical personnel and hospitals. The system includes a guidance unit that provides guidance on first aid based on the information retrieved by the search unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for symptoms and situations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past medical history and provides the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter symptoms or conditions, the input fields are customized based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter symptoms or conditions, the system prioritizes displaying the most relevant input fields, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input symptoms or conditions, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned translation department, During translation, adjust the level of detail based on the importance of medical terms. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned translation department, During translation, different translation algorithms are applied depending on regional differences in language. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, During translation, translation priorities are determined based on when the input information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, consider the interrelationships between symptoms to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the search will take into account the attribute information of healthcare professionals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the search will take into account the geographical distribution of medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, refer to related literature to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is The system estimates the user's emotions and adjusts the first aid guidance method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is When providing guidance, refer to past emergency response data to select the most appropriate guidance method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is When providing guidance, different guidance methods will be applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is It estimates the user's emotions and determines the priority of emergency response based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is During the consultation, we will adjust the order of first aid based on when the symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is When providing guidance, we will refer to relevant medical guidelines to improve the accuracy of the guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users regarding symptoms and conditions, The translation unit analyzes the information received by the reception unit, identifies the language, and performs the translation. A search unit summarizes the symptoms based on the information translated by the aforementioned translation unit and searches for appropriate medical personnel and hospitals. The system includes a guidance unit that provides guidance on first aid based on the information retrieved by the search unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for symptoms and situations based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past medical history and provides the optimal input format. The system according to feature 1.

4. The aforementioned reception unit is When users enter symptoms or conditions, the input fields are customized based on their current health status and lifestyle. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter symptoms or conditions, the system prioritizes displaying the most relevant input fields, taking into account their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users input symptoms or conditions, the system analyzes their social media activity and suggests relevant input fields. The system according to feature 1.

8. The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system according to feature 1.

9. The aforementioned translation department, During translation, adjust the level of detail based on the importance of medical terms. The system according to feature 1.

10. The aforementioned translation department, During translation, different translation algorithms are applied depending on regional differences in language. The system according to feature 1.

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