System

Abstract

An object of the system according to the embodiment is to proceed with the medical interview during the waiting time of the patient.SOLUTION: In one embodiment, a system comprises a generating AI and a subject. The generation AI generates appropriate questions for the patients. The patient fills out a questionnaire based on the questions generated by the generating AI. The generating AI shares the patient-completed questionnaire with the physician.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the patient cannot proceed with the medical interview while waiting, which may reduce the efficiency of the medical examination.

[0005] The system according to the embodiment aims to advance medical interviews while patients are waiting. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI and a patient. The generation AI generates appropriate questions for the patient. The patient fills out a medical questionnaire based on the questions generated by the generation AI. The generation AI shares the completed medical questionnaire with the doctor. [Effects of the Invention]

[0007] The system according to the embodiment can advance the interview while the patient is waiting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical interview system according to an embodiment of the present invention uses a generation AI to allow patients to fill out a medical interview form and share the contents with a doctor, allowing the system to proceed with the medical interview while patients are waiting. This makes it easier for patients to accurately communicate their symptoms, and doctors can use the results of the medical interview form in advance to help with their examinations.

[0029] The medical interview system according to the embodiment includes a generation AI, a patient, and a doctor. The generation AI generates appropriate questions for the patient. For example, the generation AI generates questions such as "What symptoms do you have?" and "When did these symptoms start?" The generation AI receives input from a prompt containing instructions from the patient about what the patient wants the generation AI to do, and the generation AI generates questions based on the prompt. This allows the patient to accurately describe their symptoms. The patient fills out a medical interview form based on the questions generated by the generation AI. For example, the patient might answer, "I have a headache," or "It's been three days since then." The generation AI analyzes the patient's responses in real time and generates additional questions as needed. This allows the patient to describe their symptoms in detail. The generation AI shares the medical interview form completed by the patient with the doctor. For example, the generation AI can transfer the contents of the medical interview form to an electronic medical record system so that the doctor can review it before the consultation. This allows the doctor to understand the patient's symptoms in advance and use this information during the consultation. As a result, the medical interview system according to the embodiment makes it easier for patients to accurately describe their symptoms, and doctors can use the results of the medical interview form in advance to help them with their examinations.

[0030] Generative AI can refer to a patient's past medical history and generate individually customized questions. For example, generative AI can retrieve past medical records from a patient's electronic medical record and generate questions related to specific symptoms or diseases based on that information. For example, a patient previously diagnosed with asthma might be asked about whether they have recently experienced shortness of breath and how often. Generative AI can also refer to a patient's past prescription history to generate questions about the effects and side effects of medications related to their current symptoms. For example, a patient who has taken a specific medication in the past might be asked questions about the effects and side effects of that medication. Generative AI can also generate questions based on a patient's past test results to determine whether additional tests related to their current symptoms are necessary. For example, a patient previously diagnosed with high blood pressure might be asked about their most recent blood pressure measurement results. This allows for the generation of more appropriate questions based on a patient's past medical history.

[0031] The generation AI can generate more specific questions by taking into account the patient's lifestyle and environmental information. For example, the generation AI acquires the patient's lifestyle data (e.g., diet, exercise, and sleep patterns) and generates health-related questions based on that data. For example, it adds questions about diet and exercise frequency. The generation AI also references the patient's living environment information (e.g., the climate and air quality of the area where they live) and generates questions about the impact of environmental factors on health based on that information. For example, it adds questions about the seasons and environments in which allergy symptoms are more likely to occur. The generation AI also considers the patient's occupation and daily stress level and generates questions about related health risks. For example, it adds questions about back pain caused by long hours of desk work and stress management. This allows the generation of more specific questions based on the patient's lifestyle and environmental information.

[0032] The generative AI can generate questions in multiple languages ​​and create multilingual medical questionnaires. For example, the generative AI can automatically translate questions and generate multilingual medical questionnaires based on the language selected by the patient. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also accurately translates technical and medical terminology in each language to generate multilingual medical questionnaires. For example, questions containing medical terminology are accurately expressed in each language. The generative AI also generates questions that take into account cultural background and language-specific nuances based on the patient's native language. For example, questions are created using culturally appropriate expressions and examples. This makes it possible to generate questions in multiple languages ​​and create multilingual medical questionnaires.

[0033] The generation AI can respond to voice input, allowing patients to respond verbally. For example, the generation AI uses speech recognition technology to convert the patient's voice input into text and reflect it in a medical questionnaire. For example, if a patient responds verbally, "I have a headache," that content is automatically entered into the medical questionnaire. The generation AI also analyzes the patient's voice in real time as they respond verbally and generates additional questions as needed. For example, it may present more detailed questions verbally based on the patient's answers. The generation AI also improves the accuracy of speech recognition to generate a medical questionnaire that responds to voice input. For example, it trains a speech recognition model to accommodate different accents and speaking styles. This makes it easier for patients to fill out the medical questionnaire by allowing them to respond verbally.

[0034] The generation AI can analyze the patient's answers in real time and automatically point out inconsistencies or unclear points. For example, the generation AI can analyze the patient's answers in real time and automatically point out inconsistencies. For example, if a patient answers "I have a headache" and then answers "I don't have any pain," the inconsistency will be pointed out. Furthermore, if there are unclear points in the patient's answers, the generation AI will generate follow-up questions to resolve the ambiguity. For example, if the answer to the question "When did your symptoms start?" is vague, the generation AI will ask for a specific date. The generation AI can also analyze the patient's answers and generate relevant follow-up questions based on the answers. For example, if the patient answers "I have a headache," the generation AI will generate follow-up questions about the intensity and frequency of the pain. This makes it possible to automatically point out inconsistencies or unclear points in the patient's answers.

[0035] The generative AI can provide relevant medical information and advice when a patient enters their answer. For example, when a patient enters their answer, the generative AI provides relevant medical information. For example, if the patient answers "I have a headache," it will display information about the causes of headaches and how to deal with them. The generative AI also provides appropriate advice based on the patient's answer. For example, it displays advice such as "Lack of exercise may be the cause. Try to exercise regularly." The generative AI also searches for and displays relevant medical information in real time when a patient enters their answer. For example, it provides information about the latest research results and treatments related to specific symptoms. This allows the generative AI to provide relevant medical information and advice based on the patient's answer.

[0036] Generative AI can provide a mobile app that allows patients to fill out medical questionnaires using a smartphone or tablet. For example, generative AI develops a mobile app that allows patients to fill out medical questionnaires using a smartphone or tablet. For example, the generative AI presents questions within the app, allowing patients to answer by tapping or voice input. The generative AI also enables patients to fill out the medical questionnaire anytime, anywhere through the mobile app. For example, they can fill out the medical questionnaire while commuting or relaxing at home. The generative AI also uses the mobile app to provide real-time feedback to patients as they fill out the medical questionnaire. For example, it can generate additional questions based on the answers provided, allowing patients to provide more detailed information. This allows patients to fill out the medical questionnaire using a smartphone or tablet.

[0037] Generative AI can add a function that allows patients to share completed medical questionnaires with their family members and caregivers. For example, Generative AI could add a function to a mobile app that allows patients to share completed medical questionnaires with their family members and caregivers. For example, the contents of the medical questionnaire could be sent to family members and caregivers by simply pressing a share button. Generative AI could also develop a function that allows family members and caregivers to view the patient's medical questionnaire and provide additional information as needed. For example, family members could add detailed information about the patient's symptoms. Generative AI could also strengthen security features to protect privacy when sharing completed medical questionnaires with family members and caregivers. For example, a password or authentication code could be set when sharing. This would allow patients to share completed medical questionnaires with their family members and caregivers.

[0038] Generative AI can automatically summarize the contents of a medical questionnaire and provide it in a format that doctors can understand in a short time. For example, generative AI analyzes the patient's responses, extracts important information, and automatically summarizes it. For example, it concisely summarizes an overview of symptoms, the time of onset, and related medical history. Generative AI also provides the automatically summarized contents of the medical questionnaire in a format that doctors can understand in a short time. For example, it displays information in bullet points or graph format. Furthermore, when summarizing the contents of the medical questionnaire, generative AI emphasizes points that doctors should pay particular attention to. For example, it prominently displays highly urgent symptoms and important medical history. This allows doctors to understand the contents of the medical questionnaire in a short time.

[0039] Generative AI can analyze the contents of the medical questionnaire and provide doctors with additional information and points of caution that will help with diagnosis. For example, generative AI can analyze the contents of the medical questionnaire and provide additional information that will help with diagnosis. For example, it can present the latest research results and treatment guidelines related to the patient's symptoms. Generative AI can also automatically extract and present points that doctors should pay attention to based on the contents of the medical questionnaire. For example, if a particular symptom is a sign of a serious disease, it can highlight that information. Generative AI can also analyze the contents of the medical questionnaire and suggest additional questions or tests that will help with diagnosis. For example, it can suggest additional tests or diagnostic methods for specific symptoms. This allows doctors to receive additional information and points of caution that will help with diagnosis.

[0040] Generative AI can automatically organize the contents of a medical questionnaire to match the doctor's schedule, improving the efficiency of medical examinations. For example, generative AI analyzes the contents of a medical questionnaire and automatically organizes them to match the doctor's schedule. For example, it optimizes the order and priorities of examinations. Generative AI can also develop a system that efficiently displays the contents of a medical questionnaire based on the doctor's schedule. For example, it can highlight important information to match the consultation time. Generative AI can also develop algorithms to organize the contents of a medical questionnaire to match the doctor's schedule and improve the efficiency of medical examinations. For example, it can provide an optimal method of displaying information to shorten the consultation time. This allows the contents of a medical questionnaire to be organized to match the doctor's schedule, improving the efficiency of medical examinations.

[0041] The generating AI can filter the contents of the medical questionnaire according to the doctor's specialty, and prioritize the display of highly relevant information. For example, the generating AI analyzes the contents of the medical questionnaire and filters it according to the doctor's specialty. For example, it will prioritize the display of information related to internal medicine for an internist. The generating AI will also develop a system that optimizes the contents of the medical questionnaire based on the doctor's specialty. For example, it will highlight important information related to the specialty. The generating AI will also develop an algorithm that filters the contents of the medical questionnaire according to the doctor's specialty and improves the efficiency of medical examinations. For example, it will prioritize the display of information related to the specialty. This makes it possible to filter the contents of the medical questionnaire according to the doctor's specialty, and prioritize the display of highly relevant information.

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

[0043] The medical interview system can also collect lifestyle data on patients and predict their health risks. For example, it can analyze a patient's diet, exercise, and sleep patterns to assess their risk of lifestyle-related diseases. It can also predict mental health risks by taking into account the patient's stress level and work environment. It can also refer to environmental data from the patient's residential area to assess the risk of allergies and respiratory diseases. This makes it possible to predict health risks and provide preventative advice based on the patient's lifestyle and environment.

[0044] The medical interview system can also analyze the patient's responses and provide relevant medical information and advice. For example, if a patient answers, "I have a headache," it can display information about the causes of headaches and how to deal with them. If the patient answers, "Lack of exercise may be the cause," it can provide advice on appropriate exercise methods and frequency. Furthermore, based on the patient's responses, it can also display the latest relevant research results and treatment guidelines. This makes it possible to provide relevant medical information and advice based on the patient's responses and support the patient's health management.

[0045] The medical interview system can also analyze the patient's answers in real time and automatically point out any inconsistencies or unclear points. For example, if a patient answers "I have a headache" and then "I don't feel any pain," the system will point out the inconsistency. Also, if the patient gives an ambiguous answer to the question "When did your symptoms start?", the system can ask for a specific date. Furthermore, it can generate additional related questions based on the patient's answers to gather more detailed information. This allows the system to automatically point out any inconsistencies or unclear points in the patient's answers and collect accurate information.

[0046] When a patient enters their answers, the medical interview system can also provide relevant medical information and advice. For example, if a patient answers "I have a headache," it can display information about the causes of headaches and how to deal with them. If a patient answers "Lack of exercise may be the cause," it can provide advice on appropriate exercise methods and frequency. Furthermore, it can display the latest relevant research results and treatment guidelines based on the patient's answers. This makes it possible to provide relevant medical information and advice based on the patient's answers and support the patient's health management.

[0047] The medical interview system can also provide a mobile app that allows patients to fill out a medical questionnaire using a smartphone or tablet. For example, a generative AI can present questions within the app, allowing patients to answer by tapping or voice input. The mobile app can also allow patients to fill out the medical questionnaire anytime, anywhere. Furthermore, the mobile app can provide real-time feedback to patients as they fill out the medical questionnaire. This allows patients to fill out the medical questionnaire using a smartphone or tablet, improving the convenience of medical interviews.

[0048] The medical interview system can also add a function that allows patients to share the completed medical questionnaire with their family and caregivers. For example, the contents of the medical questionnaire can be sent to family and caregivers by simply pressing a share button. A function can also be developed that allows family and caregivers to view the patient's medical questionnaire and provide additional information as needed. Furthermore, when the medical questionnaire completed by the patient is shared with family and caregivers, security functions can be strengthened to protect privacy. This allows patients to share the completed medical questionnaire with their family and caregivers, thereby supporting their health management.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generative AI generates appropriate questions for the patient, such as "What symptoms do you have?" or "When did these symptoms start?" The input to the generative AI is a prompt containing instructions on what the patient wants the generative AI to do, and the generative AI generates questions based on that prompt. Step 2: The patient fills out a questionnaire based on the questions generated by the AI. For example, the patient might answer, "I have a headache," or "It's been three days since then." The AI ​​analyzes the patient's answers in real time and generates additional questions as needed. Step 3: The generative AI shares the completed medical questionnaire with the doctor. For example, it can transfer the contents of the medical questionnaire to an electronic medical record system so that the doctor can review it before the consultation.

[0051] (Example 2) The medical interview system according to an embodiment of the present invention uses a generation AI to allow patients to fill out a medical interview form and share the contents with a doctor, allowing the system to proceed with the medical interview while patients are waiting. This makes it easier for patients to accurately communicate their symptoms, and doctors can use the results of the medical interview form in advance to help with their examinations.

[0052] The medical interview system according to the embodiment includes a generation AI, a patient, and a doctor. The generation AI generates appropriate questions for the patient. For example, the generation AI generates questions such as "What symptoms do you have?" and "When did these symptoms start?" The generation AI receives input from a prompt containing instructions from the patient about what the patient wants the generation AI to do, and the generation AI generates questions based on the prompt. This allows the patient to accurately describe their symptoms. The patient fills out a medical interview form based on the questions generated by the generation AI. For example, the patient might answer, "I have a headache," or "It's been three days since then." The generation AI analyzes the patient's responses in real time and generates additional questions as needed. This allows the patient to describe their symptoms in detail. The generation AI shares the medical interview form completed by the patient with the doctor. For example, the generation AI can transfer the contents of the medical interview form to an electronic medical record system so that the doctor can review it before the consultation. This allows the doctor to understand the patient's symptoms in advance and use this information during the consultation. As a result, the medical interview system according to the embodiment makes it easier for patients to accurately describe their symptoms, and doctors can use the results of the medical interview form in advance to help them with their examinations.

[0053] Generative AI can refer to a patient's past medical history and generate individually customized questions. For example, generative AI can retrieve past medical records from a patient's electronic medical record and generate questions related to specific symptoms or diseases based on that information. For example, a patient previously diagnosed with asthma might be asked about whether they have recently experienced shortness of breath and how often. Generative AI can also refer to a patient's past prescription history to generate questions about the effects and side effects of medications related to their current symptoms. For example, a patient who has taken a specific medication in the past might be asked questions about the effects and side effects of that medication. Generative AI can also generate questions based on a patient's past test results to determine whether additional tests related to their current symptoms are necessary. For example, a patient previously diagnosed with high blood pressure might be asked about their most recent blood pressure measurement results. This allows for the generation of more appropriate questions based on a patient's past medical history.

[0054] The generation AI can generate more specific questions by taking into account the patient's lifestyle and environmental information. For example, the generation AI acquires the patient's lifestyle data (e.g., diet, exercise, and sleep patterns) and generates health-related questions based on that data. For example, it adds questions about diet and exercise frequency. The generation AI also references the patient's living environment information (e.g., the climate and air quality of the area where they live) and generates questions about the impact of environmental factors on health based on that information. For example, it adds questions about the seasons and environments in which allergy symptoms are more likely to occur. The generation AI also considers the patient's occupation and daily stress level and generates questions about related health risks. For example, it adds questions about back pain caused by long hours of desk work and stress management. This allows the generation of more specific questions based on the patient's lifestyle and environmental information.

[0055] The generation AI can use its emotion estimation function to grasp the patient's emotional state and generate questions that reduce stress and anxiety. For example, the generation AI analyzes the patient's facial expressions and voice tone to estimate their emotional state. For example, if the patient looks anxious, it generates questions that have a relaxing effect. The generation AI also generates questions to reduce stress and anxiety according to the patient's emotional state. For example, it adds questions about relaxation methods and stress management. The generation AI also uses its emotion estimation function to dynamically change the order and content of questions to help the patient relax. For example, it places simple, easy-to-answer questions first, gradually moving on to more detailed questions. This makes it possible to generate questions that reduce stress and anxiety based on the patient's emotional state.

[0056] The generative AI can generate questions in multiple languages ​​and create multilingual medical questionnaires. For example, the generative AI can automatically translate questions and generate multilingual medical questionnaires based on the language selected by the patient. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also accurately translates technical and medical terminology in each language to generate multilingual medical questionnaires. For example, questions containing medical terminology are accurately expressed in each language. The generative AI also generates questions that take into account cultural background and language-specific nuances based on the patient's native language. For example, questions are created using culturally appropriate expressions and examples. This makes it possible to generate questions in multiple languages ​​and create multilingual medical questionnaires.

[0057] The generation AI can respond to voice input, allowing patients to respond verbally. For example, the generation AI uses speech recognition technology to convert the patient's voice input into text and reflect it in a medical questionnaire. For example, if a patient responds verbally, "I have a headache," that content is automatically entered into the medical questionnaire. The generation AI also analyzes the patient's voice in real time as they respond verbally and generates additional questions as needed. For example, it may present more detailed questions verbally based on the patient's answers. The generation AI also improves the accuracy of speech recognition to generate a medical questionnaire that responds to voice input. For example, it trains a speech recognition model to accommodate different accents and speaking styles. This makes it easier for patients to fill out the medical questionnaire by allowing them to respond verbally.

[0058] The generative AI can use its emotion estimation function to dynamically change the order and content of questions according to the patient's emotions. For example, the generative AI can analyze the patient's emotional state in real time and dynamically change the order and content of questions. For example, if the patient is nervous, it will present questions that have a relaxing effect first. The generative AI also uses its emotion estimation function to adjust the difficulty and level of detail of questions according to the patient's emotions. For example, if the patient is relaxed, it will add more detailed questions. The generative AI also develops an algorithm that optimizes the order of questions based on the patient's emotional state. For example, if the emotion score is low, it will start with simple questions and gradually move on to more detailed questions. This makes it possible to dynamically change the order and content of questions according to the patient's emotions.

[0059] The generation AI can analyze the patient's answers in real time and automatically point out inconsistencies or unclear points. For example, the generation AI can analyze the patient's answers in real time and automatically point out inconsistencies. For example, if a patient answers "I have a headache" and then answers "I don't have any pain," the inconsistency will be pointed out. Furthermore, if there are unclear points in the patient's answers, the generation AI will generate follow-up questions to resolve the ambiguity. For example, if the answer to the question "When did your symptoms start?" is vague, the generation AI will ask for a specific date. The generation AI can also analyze the patient's answers and generate relevant follow-up questions based on the answers. For example, if the patient answers "I have a headache," the generation AI will generate follow-up questions about the intensity and frequency of the pain. This makes it possible to automatically point out inconsistencies or unclear points in the patient's answers.

[0060] The generative AI can provide relevant medical information and advice when a patient enters their answer. For example, when a patient enters their answer, the generative AI provides relevant medical information. For example, if the patient answers "I have a headache," it will display information about the causes of headaches and how to deal with them. The generative AI also provides appropriate advice based on the patient's answer. For example, it displays advice such as "Lack of exercise may be the cause. Try to exercise regularly." The generative AI also searches for and displays relevant medical information in real time when a patient enters their answer. For example, it provides information about the latest research results and treatments related to specific symptoms. This allows the generative AI to provide relevant medical information and advice based on the patient's answer.

[0061] The generative AI can use its emotion estimation function to provide an interface with a relaxing effect that corresponds to the patient's emotional state. For example, the generative AI uses its emotion estimation function to analyze the patient's emotional state in real time and provide an interface with a relaxing effect. For example, if the patient is tense, it can display calming music or a background image with a relaxing effect. The generative AI also dynamically changes the design and color of the interface according to the patient's emotional state. For example, if the patient is feeling anxious, it can provide an interface with calming colors. The generative AI also uses its emotion estimation function to add interface elements that help the patient relax. For example, it can display relaxing animations or guided deep breathing exercises. This makes it possible to provide an interface with a relaxing effect that corresponds to the patient's emotional state.

[0062] Generative AI can provide a mobile app that allows patients to fill out medical questionnaires using a smartphone or tablet. For example, generative AI develops a mobile app that allows patients to fill out medical questionnaires using a smartphone or tablet. For example, the generative AI presents questions within the app, allowing patients to answer by tapping or voice input. The generative AI also enables patients to fill out the medical questionnaire anytime, anywhere through the mobile app. For example, they can fill out the medical questionnaire while commuting or relaxing at home. The generative AI also uses the mobile app to provide real-time feedback to patients as they fill out the medical questionnaire. For example, it can generate additional questions based on the answers provided, allowing patients to provide more detailed information. This allows patients to fill out the medical questionnaire using a smartphone or tablet.

[0063] Generative AI can add a function that allows patients to share completed medical questionnaires with their family members and caregivers. For example, Generative AI could add a function to a mobile app that allows patients to share completed medical questionnaires with their family members and caregivers. For example, the contents of the medical questionnaire could be sent to family members and caregivers by simply pressing a share button. Generative AI could also develop a function that allows family members and caregivers to view the patient's medical questionnaire and provide additional information as needed. For example, family members could add detailed information about the patient's symptoms. Generative AI could also strengthen security features to protect privacy when sharing completed medical questionnaires with family members and caregivers. For example, a password or authentication code could be set when sharing. This would allow patients to share completed medical questionnaires with their family members and caregivers.

[0064] The generation AI can use its emotion estimation function to display encouraging and reassuring messages that correspond to the patient's emotions. For example, the generation AI can use its emotion estimation function to analyze the patient's emotional state in real time and display encouraging and reassuring messages. For example, if the patient is feeling anxious, it can display a message such as, "It's okay, please relax and answer." The generation AI can also dynamically generate positive messages according to the patient's emotional state. For example, if the patient is nervous, it can display a message such as, "To protect your health, please provide accurate information." The generation AI can also use its emotion estimation function to display messages that help the patient relax. For example, it can display a relaxing message such as, "Take a deep breath and answer slowly." This makes it possible to display encouraging and reassuring messages that correspond to the patient's emotions.

[0065] Generative AI can automatically summarize the contents of a medical questionnaire and provide it in a format that doctors can understand in a short time. For example, generative AI analyzes the patient's responses, extracts important information, and automatically summarizes it. For example, it concisely summarizes an overview of symptoms, the time of onset, and related medical history. Generative AI also provides the automatically summarized contents of the medical questionnaire in a format that doctors can understand in a short time. For example, it displays information in bullet points or graph format. Furthermore, when summarizing the contents of the medical questionnaire, generative AI emphasizes points that doctors should pay particular attention to. For example, it prominently displays highly urgent symptoms and important medical history. This allows doctors to understand the contents of the medical questionnaire in a short time.

[0066] Generative AI can analyze the contents of the medical questionnaire and provide doctors with additional information and points of caution that will help with diagnosis. For example, generative AI can analyze the contents of the medical questionnaire and provide additional information that will help with diagnosis. For example, it can present the latest research results and treatment guidelines related to the patient's symptoms. Generative AI can also automatically extract and present points that doctors should pay attention to based on the contents of the medical questionnaire. For example, if a particular symptom is a sign of a serious disease, it can highlight that information. Generative AI can also analyze the contents of the medical questionnaire and suggest additional questions or tests that will help with diagnosis. For example, it can suggest additional tests or diagnostic methods for specific symptoms. This allows doctors to receive additional information and points of caution that will help with diagnosis.

[0067] Using its emotion estimation function, generative AI can report the patient's emotional state to the doctor, which can be useful in communication during consultations. For example, generative AI can use its emotion estimation function to analyze the patient's emotional state in real time and report the results to the doctor. For example, if the patient is feeling anxious, it will convey that information to the doctor. Generative AI can also support the doctor in communicating appropriately during consultations based on the patient's emotional state. For example, if the patient is nervous, it can provide advice to help them relax. Generative AI can also provide feedback to the doctor based on the emotion estimation data to help improve communication with the patient. For example, it can suggest ways of speaking and asking questions that will help the patient relax. This allows the patient's emotional state to be reported to the doctor, which can be useful in communication during consultations.

[0068] Generative AI can automatically organize the contents of a medical questionnaire to match the doctor's schedule, improving the efficiency of medical examinations. For example, generative AI analyzes the contents of a medical questionnaire and automatically organizes them to match the doctor's schedule. For example, it optimizes the order and priorities of examinations. Generative AI can also develop a system that efficiently displays the contents of a medical questionnaire based on the doctor's schedule. For example, it can highlight important information to match the consultation time. Generative AI can also develop algorithms to organize the contents of a medical questionnaire to match the doctor's schedule and improve the efficiency of medical examinations. For example, it can provide an optimal method of displaying information to shorten the consultation time. This allows the contents of a medical questionnaire to be organized to match the doctor's schedule, improving the efficiency of medical examinations.

[0069] The generating AI can filter the contents of the medical questionnaire according to the doctor's specialty, and prioritize the display of highly relevant information. For example, the generating AI analyzes the contents of the medical questionnaire and filters it according to the doctor's specialty. For example, it will prioritize the display of information related to internal medicine for an internist. The generating AI will also develop a system that optimizes the contents of the medical questionnaire based on the doctor's specialty. For example, it will highlight important information related to the specialty. The generating AI will also develop an algorithm that filters the contents of the medical questionnaire according to the doctor's specialty and improves the efficiency of medical examinations. For example, it will prioritize the display of information related to the specialty. This makes it possible to filter the contents of the medical questionnaire according to the doctor's specialty, and prioritize the display of highly relevant information.

[0070] Using its emotion estimation function, the generative AI can suggest an examination approach based on the patient's emotional state. For example, the generative AI can use its emotion estimation function to analyze the patient's emotional state in real time and suggest an examination approach based on the results. For example, if the patient is feeling anxious, it can suggest an approach to help them relax. The generative AI can also support doctors in taking an appropriate approach during examinations based on the patient's emotional state. For example, if the patient is nervous, it can suggest ways of speaking and asking questions to help them relax. The generative AI can also provide feedback to doctors to improve communication with patients based on the emotion estimation data. For example, it can suggest an examination approach that will help the patient relax. This makes it possible to suggest examination approaches based on the patient's emotional state.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The medical interview system can also collect lifestyle data on patients and predict their health risks. For example, it can analyze a patient's diet, exercise, and sleep patterns to assess their risk of lifestyle-related diseases. It can also predict mental health risks by taking into account the patient's stress level and work environment. It can also refer to environmental data from the patient's residential area to assess the risk of allergies and respiratory diseases. This makes it possible to predict health risks and provide preventative advice based on the patient's lifestyle and environment.

[0073] The medical interview system can also estimate the patient's emotional state and provide music and images with a relaxing effect. For example, if the patient is feeling anxious, it can play calming music. If the patient is feeling tense, it can display natural images with a relaxing effect. Furthermore, it is possible to change the color and design of the interface depending on the patient's emotional state, providing a relaxing environment. This makes it possible to provide relaxing music and images based on the patient's emotional state and reduce the stress of the medical interview.

[0074] The medical interview system can also analyze the patient's responses and provide relevant medical information and advice. For example, if a patient answers, "I have a headache," it can display information about the causes of headaches and how to deal with them. If the patient answers, "Lack of exercise may be the cause," it can provide advice on appropriate exercise methods and frequency. Furthermore, based on the patient's responses, it can also display the latest relevant research results and treatment guidelines. This makes it possible to provide relevant medical information and advice based on the patient's responses and support the patient's health management.

[0075] The medical interview system can also estimate the patient's emotional state and display encouraging and reassuring messages according to their emotions. For example, if the patient is feeling anxious, it can display a message such as "It's okay, please relax and answer." If the patient is nervous, it can display a positive message such as "Please provide accurate information to protect your health." It is also possible to display messages that help patients relax and reduce the stress of the medical interview. This makes it possible to display encouraging and reassuring messages according to the patient's emotional state and reduce the stress of the medical interview.

[0076] The medical interview system can also analyze the patient's answers in real time and automatically point out any inconsistencies or unclear points. For example, if a patient answers "I have a headache" and then "I don't feel any pain," the system will point out the inconsistency. Also, if the patient gives an ambiguous answer to the question "When did your symptoms start?", the system can ask for a specific date. Furthermore, it can generate additional related questions based on the patient's answers to gather more detailed information. This allows the system to automatically point out any inconsistencies or unclear points in the patient's answers and collect accurate information.

[0077] The interview system can also estimate the patient's emotional state and suggest an examination approach based on that emotion. For example, if the patient is feeling anxious, it can suggest an approach to help them relax. Also, if the patient is nervous, it can suggest ways of speaking and asking questions to help them relax. Furthermore, it can also help doctors take an appropriate approach during examinations based on the patient's emotional state. This makes it possible to suggest an examination approach based on the patient's emotional state and improve the effectiveness of examinations.

[0078] When a patient enters their answers, the medical interview system can also provide relevant medical information and advice. For example, if a patient answers "I have a headache," it can display information about the causes of headaches and how to deal with them. If a patient answers "Lack of exercise may be the cause," it can provide advice on appropriate exercise methods and frequency. Furthermore, it can display the latest relevant research results and treatment guidelines based on the patient's answers. This makes it possible to provide relevant medical information and advice based on the patient's answers and support the patient's health management.

[0079] The medical interview system can also estimate the patient's emotional state and provide a relaxing interface according to the patient's emotions. For example, if the patient is nervous, it can display calming music or a relaxing background image. If the patient is feeling anxious, it can provide an interface with calming colors. Furthermore, it can display relaxing animations or guided deep breathing exercises according to the patient's emotional state. This can provide a relaxing interface according to the patient's emotional state and reduce the stress of the medical interview.

[0080] The medical interview system can also provide a mobile app that allows patients to fill out a medical questionnaire using a smartphone or tablet. For example, a generative AI can present questions within the app, allowing patients to answer by tapping or voice input. The mobile app can also allow patients to fill out the medical questionnaire anytime, anywhere. Furthermore, the mobile app can provide real-time feedback to patients as they fill out the medical questionnaire. This allows patients to fill out the medical questionnaire using a smartphone or tablet, improving the convenience of medical interviews.

[0081] The medical interview system can also add a function that allows patients to share the completed medical questionnaire with their family and caregivers. For example, the contents of the medical questionnaire can be sent to family and caregivers by simply pressing a share button. A function can also be developed that allows family and caregivers to view the patient's medical questionnaire and provide additional information as needed. Furthermore, when the medical questionnaire completed by the patient is shared with family and caregivers, security functions can be strengthened to protect privacy. This allows patients to share the completed medical questionnaire with their family and caregivers, thereby supporting their health management.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generative AI generates appropriate questions for the patient, such as "What symptoms do you have?" or "When did these symptoms start?" The input to the generative AI is a prompt containing instructions on what the patient wants the generative AI to do, and the generative AI generates questions based on that prompt. Step 2: The patient fills out a questionnaire based on the questions generated by the AI. For example, the patient might answer, "I have a headache," or "It's been three days since then." The AI ​​analyzes the patient's answers in real time and generates additional questions as needed. Step 3: The generative AI shares the completed medical questionnaire with the doctor. For example, it can transfer the contents of the medical questionnaire to an electronic medical record system so that the doctor can review it before the consultation.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. This system allows patients to fill out a medical questionnaire using generative AI and share the contents with the doctor, allowing them to proceed with the medical interview while they wait. The generated AI is Generate appropriate questions for patients, The patient is Fill out a medical questionnaire based on the questions generated by the AI ​​generation system, The generated AI is The patient's completed medical questionnaire is shared with the doctor. A system characterized by:

2. The generated AI is Look up the patient's past medical history and generate personalized questions 2. The system of claim 1.

3. The generated AI is Generate more specific questions by taking into account the patient's lifestyle and environmental information 2. The system of claim 1.

4. The generated AI is Understanding the patient's emotional state and generating questions to reduce stress and anxiety 2. The system of claim 1.

5. The generated AI is Generate questions in multiple languages ​​and create multilingual questionnaires 2. The system of claim 1.

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