System

Abstract

To provide a system for grasping the type and needs of a patient and providing appropriate advice to a medical doctor.SOLUTION: The system according to an embodiment includes an interview unit, a determination unit, and an advice unit. The interview unit conducts an interview with a patient. The determination unit determines the type of the patient based on the information obtained by the interview unit. The advice unit provides advice to a medical doctor based on the type of the patient determined by the determination unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately identify the type and needs of patients and provide appropriate advice to doctors, so there is room for improvement.

[0005] The system according to the embodiment aims to understand the type and needs of patients and provide appropriate advice to doctors. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a determination unit, and an advising unit. The hearing unit hears the patient. The determination unit determines the type of the patient based on information obtained by the hearing unit. The advising unit provides advice to a doctor based on the type of the patient determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can identify the type and needs of a patient and provide appropriate advice to a doctor. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The interview system according to the embodiment of the present invention is a system that inputs an interview form through a robot. This interview system interviews a patient, determines the type of the patient based on information obtained during the interview, and gives advice to a doctor on how to treat the patient. For example, the interview system interviews a patient. At this time, questions are customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking are added. In addition, the patient's social media activity can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions are asked based on the content of the posts. Next, the type of the patient is determined based on information obtained during the interview. For example, anxious patients often have many health concerns. On the other hand, problem-solving-oriented patients tend to seek specific solutions. Also, confirmation-oriented patients have a strong need to confirm whether there is a problem. Furthermore, based on the type of patient, advice is given to the doctor on how to treat the patient. For example, it is important to provide a sense of security to anxious patients. On the other hand, problem-solving-oriented patients are required to provide detailed explanations. Also, confirmation-oriented patients require a quick response. This system allows doctors to tailor treatment to suit the type and needs of each patient, which is expected to improve patient satisfaction and the quality of medical care.

[0029] The interview system according to the embodiment includes a hearing unit, a determination unit, and an advice unit. The hearing unit hears the patient. When hearing the patient, for example, questions can be customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. In addition, the patient's social media activities can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions are asked based on the content of the posts. The determination unit determines the type of patient based on the information obtained by the hearing unit. For example, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many health concerns. On the other hand, problem-solving oriented patients tend to seek specific solutions. Also, confirmation oriented patients have a strong need to confirm whether there is a problem. The advice unit advises the doctor on how to treat the patient based on the type of patient determined by the determination unit. For example, it is important to provide a sense of security to anxious patients. On the other hand, detailed explanations are required for problem-solving oriented patients. Also, confirmation oriented patients require a quick response. As a result, the medical interview system according to the embodiment can respond according to the type and needs of the patient, which is expected to improve patient satisfaction and the quality of medical care.

[0030] The hearing section hears the patient. When hearing the patient, for example, the questions can be customized based on the patient's lifestyle and environment. Specifically, if the patient is a smoker, questions about smoking can be added. For example, the frequency and duration of smoking, the age at which the patient started smoking, and subjective symptoms regarding the effects of smoking on health can be asked in detail. In addition, the patient's social media activities can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions can be asked based on the content of the posts. Specifically, if the patient frequently posts about a specific health issue, detailed questions related to the issue can be asked to understand the patient's interests and concerns. In addition, the hearing section can collect basic information such as the patient's age, sex, occupation, and home environment, and customize the questions based on this information. For example, it is possible to focus on questions about exercise habits and dietary habits for elderly people, and add questions about stress and sleep for younger people. This allows the hearing section to collect detailed information according to the situation of each patient and enable individual response. In addition, the hearing section stores the collected information as digital data, making it easy to access for subsequent judgment and advice sections. This enables the hearing department to collect patient information efficiently and effectively, improving the accuracy and reliability of the entire system.

[0031] The judgment section judges the type of patient based on the information obtained by the hearing section. Specifically, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many health concerns. This type of patient tends to worry about serious illnesses even for minor symptoms, and often seeks reassurance from doctors. On the other hand, problem-solving oriented patients tend to seek concrete solutions. This type of patient seeks detailed explanations about the causes and treatments of symptoms, and is proactive in taking measures on their own. Confirmation oriented patients also have a strong need to check whether there are any problems. This type of patient places importance on regular health checks and confirmation of test results, and seeks clear feedback from doctors. The judgment section uses natural language processing technology using AI to distinguish these types. Specifically, it analyzes the text data obtained from the hearing section and extracts psychological characteristics from the patient's remarks and expression methods. For example, anxious patients tend to frequently use keywords such as "anxiety," "worry," and "fear," while problem-solving oriented patients tend to frequently use keywords such as "what to do," "measures," and "methods." This allows the determination unit to extract psychological characteristics from the content of the patient's remarks and the way of expression, and to determine the appropriate type. Furthermore, the determination unit can use past data and statistical information to more accurately determine the patient's type. For example, by referring to data on patients who have previously complained of similar symptoms and finding similar patterns, a more accurate determination can be made. This allows the determination unit to quickly and accurately determine the patient's type, and provide a foundation for the subsequent advice unit to take appropriate action.

[0032] The advice section gives advice to the doctor on how to treat the patient based on the type of patient determined by the judgment section. Specifically, it is important to provide a sense of security to anxious patients. For this reason, doctors are required to provide careful explanations and sympathy in order to reduce the patient's anxiety. For example, it is important to explain the cause of symptoms and treatment methods in an easy-to-understand manner and choose words that will reassure the patient. On the other hand, detailed explanations are required for problem-solving-oriented patients. For this reason, doctors are required to propose specific measures while showing scientific evidence of the cause of symptoms and treatment methods. For example, it is important to provide detailed explanations of how to improve lifestyle habits and the effects of medicines, and provide information for patients to take measures on their own. In addition, confirmation-oriented patients require prompt response. For this reason, doctors are required to provide test results and diagnosis contents promptly and provide feedback that reassures the patient. For example, by contacting the doctor immediately after the test results are available and explaining the results in detail, the patient's anxiety can be reduced. Furthermore, the advice section can collect patient feedback and continuously improve the accuracy and effectiveness of the advice content. For example, it is possible to evaluate the satisfaction and effectiveness of the advice received by the patient and review the advice content based on the results. The Advice Department also provides regular training and information to doctors, allowing them to acquire the latest medical knowledge and communication techniques. This allows the Advice Department to provide support for doctors to respond appropriately to patients, improving patient satisfaction and the quality of medical care.

[0033] The determination unit determines the needs of the patient based on the information obtained by the hearing unit, and the advice unit can provide advice to the doctor based on the type and needs of the patient determined by the determination unit. The determination unit determines the needs of the patient based on, for example, the health condition, lifestyle, and psychological state of the patient. For example, if the patient has many health concerns, it can be determined that a sense of security is required. Also, if the patient is looking for a specific solution, it can be determined that a detailed explanation is required. Furthermore, if the patient wants to confirm whether there is a problem, it can be determined that a prompt response is required. The advice unit advises the doctor on how to deal with the patient based on the type and needs of the patient determined by the determination unit. For example, advice to provide a sense of security can be provided to an anxious patient. On the other hand, advice to provide a detailed explanation can be provided to a problem-solving-oriented patient. Also, advice to provide a prompt response can be provided to a confirmation-oriented patient. As a result, by providing advice based on the type and needs of the patient, the doctor's response becomes more appropriate. A part or all of the above-mentioned processing in the determination unit and the advice unit may be performed using, for example, AI, or may be performed without using AI.

[0034] The judgment unit can judge the type of the patient from among anxiousness, problem-solving orientation, and confirmation orientation. The judgment unit judges the type of the patient from among anxiousness, problem-solving orientation, and confirmation orientation, for example, based on the patient's behavioral patterns and psychological characteristics. For example, anxious patients often have many health concerns. Problem-solving oriented patients tend to seek specific solutions. Confirmation oriented patients have a strong need to check whether there are any problems. This allows the type of the patient to be accurately judged and appropriate advice to be provided. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The judgment unit can judge the patient's needs from among reassurance, detailed explanation, and prompt response. The judgment unit judges the patient's needs from among reassurance, detailed explanation, and prompt response, for example, based on the patient's psychological state and behavioral pattern. For example, if the patient has many health concerns, it can be judged that reassurance is necessary. Also, if the patient is looking for a specific solution, it can be judged that detailed explanation is necessary. Furthermore, if the patient wants to confirm whether there is a problem, it can be judged that prompt response is necessary. This makes it possible to accurately judge the patient's needs and provide appropriate advice. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The hearing unit can customize questions for the patient based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. Also, if the patient has a specific diet, questions based on the diet can be asked. Furthermore, questions can be customized based on the patient's exercise habits and sleep patterns. This allows more appropriate information to be collected by asking questions based on the patient's lifestyle and environment. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI or may be performed without using AI.

[0037] The hearing unit can ask relevant questions based on the patient's social media activity. For example, the hearing unit analyzes the patient's social media activity and asks relevant questions. For example, if the patient posts about health on social media, questions can be asked based on the content of the posts. Information on health risks can also be collected from the patient's social media activity and questions can be asked based on the collected information. Furthermore, the patient's social media activity can be analyzed and questions can be customized based on the patient's interest in health. This allows more appropriate information to be collected by asking questions based on the patient's social media activity. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The hearing section can select questions based on the patient's past medical history. The hearing section can select optimal questions by, for example, analyzing the patient's past diagnosis results, treatment history, drug use history, etc. For example, if the patient has had a specific disease in the past, questions related to that disease can be added. In addition, questions regarding the effectiveness of treatment can be asked based on the patient's past treatment history. Furthermore, questions can be asked to confirm whether additional tests are necessary, with reference to the patient's past test results. In this way, more appropriate information can be collected by asking questions based on the patient's past medical history. Some or all of the above-mentioned processing in the hearing section can be performed, for example, using AI, or can be performed without using AI.

[0039] The hearing unit can change the questions based on the patient's current health condition during the hearing. For example, when the patient reports his / her current health condition, the hearing unit can add questions related to that condition. Also, when the patient complains of a specific symptom, the hearing unit can ask questions related to that symptom. Furthermore, when the patient's health condition changes, the questions can be updated in real time. This makes it possible to collect more appropriate information by asking questions based on the patient's current health condition. A part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI or may be performed without using AI.

[0040] During the hearing, the hearing unit can prioritize questions that are highly relevant based on the geographical location information of the patient. For example, if the patient lives in a particular area, the hearing unit can ask questions about health risks related to that area. Also, if the patient is traveling, the hearing unit can ask questions about health risks at the travel destination. Furthermore, if the patient lives in a particular environment, the hearing unit can ask questions about health risks related to that environment. This allows more appropriate information to be collected by asking questions based on the geographical location information of the patient. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The hearing unit can add related questions based on the patient's family history during the hearing. For example, if the patient's family has a history of a particular disease, the hearing unit can ask questions related to the disease. Also, based on the patient's family history, the hearing unit can ask questions about genetic risks. Furthermore, by referring to the patient's family history, questions about preventive measures can be asked. In this way, more appropriate information can be collected by asking questions based on the patient's family history. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The determination unit can improve the accuracy of type determination based on the patient's past medical history at the time of determination. The determination unit determines the type by, for example, referring to the patient's past diagnosis results, treatment history, test results, etc. For example, the type can be determined based on the patient's past diagnosis results. The type can also be determined by referring to the patient's past treatment history. Furthermore, the type can also be determined by referring to the patient's past test results. This enables more accurate determination by determining the type based on the patient's past medical history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The determination unit can determine the type based on the patient's lifestyle at the time of determination. For example, if the patient is a smoker, the type can be determined in terms of health risk. Also, if the patient is lacking in exercise, the type can be determined based on the lifestyle. Furthermore, if the patient has a specific diet, the type can also be determined based on the diet. This allows for more accurate determination by determining the type based on the patient's lifestyle. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The determination unit can determine the type based on the geographical location information of the patient at the time of determination. For example, if the patient lives in a specific area, the determination unit can determine the type based on the health risks of the area. Also, if the patient is traveling, the determination unit can determine the type based on the health risks of the travel destination. Furthermore, if the patient lives in a specific environment, the type can also be determined based on the health risks of the environment. This allows for more accurate determination by determining the type based on the geographical location information of the patient. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI.

[0045] At the time of judgment, the judgment unit can reflect the type judgment based on the social media activity of the patient. For example, if the patient posts about health on social media, the type judgment can be made based on the content. In addition, information on health risks can be collected from the patient's social media activity, and the type judgment can be made based on the collected information. Furthermore, the patient's social media activity can be analyzed and the type judgment can be made based on the patient's interest in health. This makes it possible to make a more accurate judgment by judging the type based on the patient's social media activity. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0046] When giving advice, the advice unit can provide advice based on the patient's past medical history. The advice unit provides optimal advice by, for example, referring to the patient's past diagnosis results, treatment history, test results, etc. For example, appropriate advice can be provided based on the patient's past diagnosis results. In addition, advice regarding the effectiveness of treatment can be provided by referring to the patient's past treatment history. Furthermore, advice can be provided to confirm whether additional tests are necessary by referring to the patient's past test results. This makes it possible to provide more appropriate advice by providing advice based on the patient's past medical history. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The advice unit can customize the contents of the advice based on the patient's current health condition when giving advice. For example, when the patient reports his / her current health condition, the advice unit can provide advice related to that condition. Also, when the patient complains of a specific symptom, the advice unit can provide advice related to that symptom. Furthermore, when the patient's health condition changes, the advice content can be updated in real time. This makes it possible to provide more appropriate advice by providing advice based on the patient's current health condition. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The advice unit can provide advice based on the geographical location information of the patient when giving advice. For example, if the patient lives in a particular area, the advice unit can provide advice based on the health risks of that area. Also, if the patient is traveling, the advice unit can provide advice based on the health risks of the travel destination. Furthermore, if the patient lives in a particular environment, advice can also be provided based on the health risks of that environment. This makes it possible to provide more appropriate advice by providing advice based on the geographical location information of the patient. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI.

[0049] The advice unit can provide relevant advice based on the patient's social media activity when giving advice. The advice unit, for example, analyzes the patient's social media activity and provides advice based on the content. For example, if the patient posts about health on social media, advice can be provided based on the content. Information on health risks can also be collected from the patient's social media activity and advice can be provided based on the collected information. Furthermore, the patient's social media activity can be analyzed and advice can be provided based on the patient's interest in health. This makes it possible to provide more appropriate advice by providing advice based on the patient's social media activity. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI.

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

[0051] The hearing section can also customize questions based on the patient's hobbies and interests. For example, if the patient is interested in sports, questions related to exercise habits and sports can be added. Also, if the patient is interested in music, questions can be asked about the impact of musical activities on health. Furthermore, if the patient likes reading, questions can be asked about how reading habits help with stress management. This allows for more personalized hearing by asking questions based on the patient's hobbies and interests.

[0052] The judgment unit can also judge the type based on the patient's occupation. For example, a patient who is engaged in a stressful occupation may be judged as an anxious type. Also, a patient who is engaged in an occupation that requires problem solving may be judged as a problem-solving oriented type. Furthermore, a patient who is engaged in an occupation that requires a lot of checking work may be judged as a checking oriented type. Thus, by judging the type based on the patient's occupation, a more accurate judgment can be made.

[0053] The advice unit can adjust the content of advice based on the cultural background of the patient. For example, a patient from a different cultural region can be provided with health advice appropriate to that culture. Also, for a patient with a religious background, advice can be given that takes into consideration that religion. Furthermore, in the case of language differences, advice can be provided in a language that is easy for the patient to understand. This allows for more appropriate treatment by providing advice based on the cultural background of the patient.

[0054] The interview department can customize questions based on the patient's digital device usage. For example, if the patient frequently uses a smartphone, they can ask questions about the impact of digital device use on their health. Also, if the patient uses a wearable device, they can ask questions based on the health data obtained from the device. Furthermore, if the patient searches for health information online, they can ask questions about the reliability of that information. This allows more appropriate information to be collected by asking questions based on the patient's digital device usage.

[0055] The determination unit can determine the type of the patient based on the patient's diet. For example, a patient who eats a balanced diet can be determined as a health-conscious type. A patient who frequently eats junk food can be determined as a type with a high health risk. Furthermore, a patient who has specific dietary restrictions can be determined as a type based on the restrictions. This allows for more accurate determination by determining the type of the patient based on the patient's diet.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The interview department interviews the patient. During the interview, questions can be customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. In addition, the department can analyze the patient's social media activity and ask related questions. For example, if the patient posts about health on social media, questions can be asked based on the content of those posts. Step 2: The judgment section judges the patient's type based on the information obtained by the hearing section. For example, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many concerns about their health. On the other hand, problem-solving oriented patients tend to seek concrete solutions. Confirmation oriented patients also have a strong need to make sure that there are no problems. Step 3: The advice section gives advice to the doctor on how to deal with the patient based on the type of patient determined by the judgment section. For example, it is important to provide a sense of security to anxious patients. On the other hand, problem-solving oriented patients require detailed explanations. Also, confirmation oriented patients require prompt response.

[0058] (Example 2) The interview system according to the embodiment of the present invention is a system that inputs an interview form through a robot. This interview system interviews a patient, determines the type of the patient based on information obtained during the interview, and gives advice to a doctor on how to treat the patient. For example, the interview system interviews a patient. At this time, questions are customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking are added. In addition, the patient's social media activity can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions are asked based on the content of the posts. Next, the type of the patient is determined based on information obtained during the interview. For example, anxious patients often have many health concerns. On the other hand, problem-solving-oriented patients tend to seek specific solutions. Also, confirmation-oriented patients have a strong need to confirm whether there is a problem. Furthermore, based on the type of patient, advice is given to the doctor on how to treat the patient. For example, it is important to provide a sense of security to anxious patients. On the other hand, problem-solving-oriented patients are required to provide detailed explanations. Also, confirmation-oriented patients require a quick response. This system allows doctors to tailor treatment to suit the type and needs of each patient, which is expected to improve patient satisfaction and the quality of medical care.

[0059] The interview system according to the embodiment includes a hearing unit, a determination unit, and an advice unit. The hearing unit hears the patient. When hearing the patient, for example, questions can be customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. In addition, the patient's social media activities can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions are asked based on the content of the posts. The determination unit determines the type of patient based on the information obtained by the hearing unit. For example, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many health concerns. On the other hand, problem-solving oriented patients tend to seek specific solutions. Also, confirmation oriented patients have a strong need to confirm whether there is a problem. The advice unit advises the doctor on how to treat the patient based on the type of patient determined by the determination unit. For example, it is important to provide a sense of security to anxious patients. On the other hand, detailed explanations are required for problem-solving oriented patients. Also, confirmation oriented patients require a quick response. As a result, the medical interview system according to the embodiment can respond according to the type and needs of the patient, which is expected to improve patient satisfaction and the quality of medical care.

[0060] The hearing section hears the patient. When hearing the patient, for example, the questions can be customized based on the patient's lifestyle and environment. Specifically, if the patient is a smoker, questions about smoking can be added. For example, the frequency and duration of smoking, the age at which the patient started smoking, and subjective symptoms regarding the effects of smoking on health can be asked in detail. In addition, the patient's social media activities can be analyzed and related questions can be asked. For example, if the patient posts about health on social media, questions can be asked based on the content of the posts. Specifically, if the patient frequently posts about a specific health issue, detailed questions related to the issue can be asked to understand the patient's interests and concerns. In addition, the hearing section can collect basic information such as the patient's age, sex, occupation, and home environment, and customize the questions based on this information. For example, it is possible to focus on questions about exercise habits and dietary habits for elderly people, and add questions about stress and sleep for younger people. This allows the hearing section to collect detailed information according to the situation of each patient and enable individual response. In addition, the hearing section stores the collected information as digital data, making it easy to access for subsequent judgment and advice sections. This enables the hearing department to collect patient information efficiently and effectively, improving the accuracy and reliability of the entire system.

[0061] The judgment section judges the type of patient based on the information obtained by the hearing section. Specifically, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many health concerns. This type of patient tends to worry about serious illnesses even for minor symptoms, and often seeks reassurance from doctors. On the other hand, problem-solving oriented patients tend to seek concrete solutions. This type of patient seeks detailed explanations about the causes and treatments of symptoms, and is proactive in taking measures on their own. Confirmation oriented patients also have a strong need to check whether there are any problems. This type of patient places importance on regular health checks and confirmation of test results, and seeks clear feedback from doctors. The judgment section uses natural language processing technology using AI to distinguish these types. Specifically, it analyzes the text data obtained from the hearing section and extracts psychological characteristics from the patient's remarks and expression methods. For example, anxious patients tend to frequently use keywords such as "anxiety," "worry," and "fear," while problem-solving oriented patients tend to frequently use keywords such as "what to do," "measures," and "methods." This allows the determination unit to extract psychological characteristics from the content of the patient's remarks and the way of expression, and to determine the appropriate type. Furthermore, the determination unit can use past data and statistical information to more accurately determine the patient's type. For example, by referring to data on patients who have previously complained of similar symptoms and finding similar patterns, a more accurate determination can be made. This allows the determination unit to quickly and accurately determine the patient's type, and provide a foundation for the subsequent advice unit to take appropriate action.

[0062] The advice section gives advice to the doctor on how to treat the patient based on the type of patient determined by the judgment section. Specifically, it is important to provide a sense of security to anxious patients. For this reason, doctors are required to provide careful explanations and sympathy in order to reduce the patient's anxiety. For example, it is important to explain the cause of symptoms and treatment methods in an easy-to-understand manner and choose words that will reassure the patient. On the other hand, detailed explanations are required for problem-solving-oriented patients. For this reason, doctors are required to propose specific measures while showing scientific evidence of the cause of symptoms and treatment methods. For example, it is important to provide detailed explanations of how to improve lifestyle habits and the effects of medicines, and provide information for patients to take measures on their own. In addition, confirmation-oriented patients require prompt response. For this reason, doctors are required to provide test results and diagnosis contents promptly and provide feedback that reassures the patient. For example, by contacting the doctor immediately after the test results are available and explaining the results in detail, the patient's anxiety can be reduced. Furthermore, the advice section can collect patient feedback and continuously improve the accuracy and effectiveness of the advice content. For example, it is possible to evaluate the satisfaction and effectiveness of the advice received by the patient and review the advice content based on the results. The Advice Department also provides regular training and information to doctors, allowing them to acquire the latest medical knowledge and communication techniques. This allows the Advice Department to provide support for doctors to respond appropriately to patients, improving patient satisfaction and the quality of medical care.

[0063] The determination unit determines the needs of the patient based on the information obtained by the hearing unit, and the advice unit can provide advice to the doctor based on the type and needs of the patient determined by the determination unit. The determination unit determines the needs of the patient based on, for example, the health condition, lifestyle, and psychological state of the patient. For example, if the patient has many health concerns, it can be determined that a sense of security is required. Also, if the patient is looking for a specific solution, it can be determined that a detailed explanation is required. Furthermore, if the patient wants to confirm whether there is a problem, it can be determined that a prompt response is required. The advice unit advises the doctor on how to deal with the patient based on the type and needs of the patient determined by the determination unit. For example, advice to provide a sense of security can be provided to an anxious patient. On the other hand, advice to provide a detailed explanation can be provided to a problem-solving-oriented patient. Also, advice to provide a prompt response can be provided to a confirmation-oriented patient. As a result, by providing advice based on the type and needs of the patient, the doctor's response becomes more appropriate. A part or all of the above-mentioned processing in the determination unit and the advice unit may be performed using, for example, AI, or may be performed without using AI.

[0064] The judgment unit can judge the type of the patient from among anxiousness, problem-solving orientation, and confirmation orientation. The judgment unit judges the type of the patient from among anxiousness, problem-solving orientation, and confirmation orientation, for example, based on the patient's behavioral patterns and psychological characteristics. For example, anxious patients often have many health concerns. Problem-solving oriented patients tend to seek specific solutions. Confirmation oriented patients have a strong need to check whether there are any problems. This allows the type of the patient to be accurately judged and appropriate advice to be provided. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The judgment unit can judge the patient's needs from among reassurance, detailed explanation, and prompt response. The judgment unit judges the patient's needs from among reassurance, detailed explanation, and prompt response, for example, based on the patient's psychological state and behavioral pattern. For example, if the patient has many health concerns, it can be judged that reassurance is necessary. Also, if the patient is looking for a specific solution, it can be judged that detailed explanation is necessary. Furthermore, if the patient wants to confirm whether there is a problem, it can be judged that prompt response is necessary. This makes it possible to accurately judge the patient's needs and provide appropriate advice. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The hearing unit can customize questions for the patient based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. Also, if the patient has a specific diet, questions based on the diet can be asked. Furthermore, questions can be customized based on the patient's exercise habits and sleep patterns. This allows more appropriate information to be collected by asking questions based on the patient's lifestyle and environment. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI or may be performed without using AI.

[0067] The hearing unit can ask relevant questions based on the patient's social media activity. For example, the hearing unit analyzes the patient's social media activity and asks relevant questions. For example, if the patient posts about health on social media, questions can be asked based on the content of the posts. Information on health risks can also be collected from the patient's social media activity and questions can be asked based on the collected information. Furthermore, the patient's social media activity can be analyzed and questions can be customized based on the patient's interest in health. This allows more appropriate information to be collected by asking questions based on the patient's social media activity. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The hearing unit can estimate the patient's emotions and adjust the order of questions based on the estimated emotions. The hearing unit estimates the patient's emotions using techniques such as facial expression analysis, voice analysis, and text analysis of the patient. For example, if the patient is nervous, simple questions can be asked to relax the patient. If the patient is relaxed, detailed questions can be asked early. If the patient is in a hurry, important questions can be asked with priority. This allows for more effective hearing by adjusting the order of questions according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI.

[0069] The hearing section can select questions based on the patient's past medical history. The hearing section can select optimal questions by, for example, analyzing the patient's past diagnosis results, treatment history, drug use history, etc. For example, if the patient has had a specific disease in the past, questions related to that disease can be added. In addition, questions regarding the effectiveness of treatment can be asked based on the patient's past treatment history. Furthermore, questions can be asked to confirm whether additional tests are necessary, with reference to the patient's past test results. In this way, more appropriate information can be collected by asking questions based on the patient's past medical history. Some or all of the above-mentioned processing in the hearing section can be performed, for example, using AI, or can be performed without using AI.

[0070] The hearing unit can change the questions based on the patient's current health condition during the hearing. For example, when the patient reports his / her current health condition, the hearing unit can add questions related to that condition. Also, when the patient complains of a specific symptom, the hearing unit can ask questions related to that symptom. Furthermore, when the patient's health condition changes, the questions can be updated in real time. This makes it possible to collect more appropriate information by asking questions based on the patient's current health condition. A part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI or may be performed without using AI.

[0071] The hearing unit can estimate the patient's emotions and adjust the level of detail of the questions based on the estimated emotions. The hearing unit estimates the patient's emotions using, for example, techniques such as facial expression analysis, voice analysis, and text analysis of the patient. For example, if the patient is nervous, concise and easy-to-understand questions can be asked. Also, if the patient is relaxed, detailed questions can be asked. Furthermore, if the patient is in a hurry, questions that focus on the main points can be asked. This allows for more effective hearing by adjusting the level of detail of questions according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI.

[0072] During the hearing, the hearing unit can prioritize questions that are highly relevant based on the geographical location information of the patient. For example, if the patient lives in a particular area, the hearing unit can ask questions about health risks related to that area. Also, if the patient is traveling, the hearing unit can ask questions about health risks at the travel destination. Furthermore, if the patient lives in a particular environment, the hearing unit can ask questions about health risks related to that environment. This allows more appropriate information to be collected by asking questions based on the geographical location information of the patient. Some or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The hearing unit can add related questions based on the patient's family history during the hearing. For example, if the patient's family has a history of a particular disease, the hearing unit can ask questions related to the disease. Also, based on the patient's family history, the hearing unit can ask questions about genetic risks. Furthermore, by referring to the patient's family history, questions about preventive measures can be asked. In this way, more appropriate information can be collected by asking questions based on the patient's family history. Some or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The judgment unit can estimate the patient's emotions and adjust the type judgment criteria based on the estimated emotions. The judgment unit estimates the patient's emotions using techniques such as facial expression analysis, voice analysis, and text analysis of the patient. For example, if the patient is nervous, the criteria for judging the patient as an anxious type can be relaxed. Also, if the patient is relaxed, the criteria for judging the patient as a problem-solving oriented type can be strengthened. Furthermore, if the patient is in a hurry, the criteria for judging the patient as a confirmation oriented type can be adjusted. This allows for more accurate type judgment by adjusting the type judgment criteria according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using an AI, or may be performed without using an AI.

[0075] The determination unit can improve the accuracy of type determination based on the patient's past medical history at the time of determination. The determination unit determines the type by, for example, referring to the patient's past diagnosis results, treatment history, test results, etc. For example, the type can be determined based on the patient's past diagnosis results. The type can also be determined by referring to the patient's past treatment history. Furthermore, the type can also be determined by referring to the patient's past test results. This enables more accurate determination by determining the type based on the patient's past medical history. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The determination unit can determine the type based on the patient's lifestyle at the time of determination. For example, if the patient is a smoker, the type can be determined in terms of health risk. Also, if the patient is lacking in exercise, the type can be determined based on the lifestyle. Furthermore, if the patient has a specific diet, the type can also be determined based on the diet. This allows for more accurate determination by determining the type based on the patient's lifestyle. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The judgment unit can estimate the patient's emotion and determine the priority of the type based on the estimated emotion. The judgment unit estimates the patient's emotion using, for example, a technique such as facial expression analysis, voice analysis, and text analysis of the patient. For example, if the patient is nervous, it can determine the anxious type as a priority. Also, if the patient is relaxed, it can determine the problem-solving oriented type as a priority. Furthermore, if the patient is in a hurry, it can determine the confirmation oriented type as a priority. This allows for more appropriate response by determining the priority of the type according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI.

[0078] The determination unit can determine the type based on the geographical location information of the patient at the time of determination. For example, if the patient lives in a specific area, the determination unit can determine the type based on the health risks of the area. Also, if the patient is traveling, the determination unit can determine the type based on the health risks of the travel destination. Furthermore, if the patient lives in a specific environment, the type can also be determined based on the health risks of the environment. This allows for more accurate determination by determining the type based on the geographical location information of the patient. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI.

[0079] At the time of judgment, the judgment unit can reflect the type judgment based on the social media activity of the patient. For example, if the patient posts about health on social media, the type judgment can be made based on the content. In addition, information on health risks can be collected from the patient's social media activity, and the type judgment can be made based on the collected information. Furthermore, the patient's social media activity can be analyzed and the type judgment can be made based on the patient's interest in health. This makes it possible to make a more accurate judgment by judging the type based on the patient's social media activity. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI.

[0080] The advice unit can estimate the patient's emotion and adjust the way of expressing advice based on the estimated emotion. The advice unit estimates the patient's emotion using, for example, techniques such as facial expression analysis, voice analysis, and text analysis of the patient. For example, if the patient is nervous, a gentle expression can be used to provide a sense of security. Also, if the patient is relaxed, a detailed explanation can be provided. Furthermore, if the patient is in a hurry, quick and concise advice can be provided. This allows for more effective advice by adjusting the way of expressing advice according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using an AI, or may be performed without using an AI.

[0081] When giving advice, the advice unit can provide advice based on the patient's past medical history. The advice unit provides optimal advice by, for example, referring to the patient's past diagnosis results, treatment history, test results, etc. For example, appropriate advice can be provided based on the patient's past diagnosis results. In addition, advice regarding the effectiveness of treatment can be provided by referring to the patient's past treatment history. Furthermore, advice can be provided to confirm whether additional tests are necessary by referring to the patient's past test results. This makes it possible to provide more appropriate advice by providing advice based on the patient's past medical history. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The advice unit can customize the contents of the advice based on the patient's current health condition when giving advice. For example, when the patient reports his / her current health condition, the advice unit can provide advice related to that condition. Also, when the patient complains of a specific symptom, the advice unit can provide advice related to that symptom. Furthermore, when the patient's health condition changes, the advice content can be updated in real time. This makes it possible to provide more appropriate advice by providing advice based on the patient's current health condition. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The advice unit can estimate the patient's emotion and determine the priority of advice based on the estimated emotion. The advice unit estimates the patient's emotion using, for example, a technique such as facial expression analysis, voice analysis, or text analysis of the patient. For example, if the patient is nervous, advice that provides a sense of security can be prioritized. Also, if the patient is relaxed, advice that provides a detailed explanation can be prioritized. Furthermore, if the patient is in a hurry, quick and concise advice can be prioritized. This allows for more appropriate response by determining the priority of advice according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI.

[0084] The advice unit can provide advice based on the geographical location information of the patient when giving advice. For example, if the patient lives in a particular area, the advice unit can provide advice based on the health risks of that area. Also, if the patient is traveling, the advice unit can provide advice based on the health risks of the travel destination. Furthermore, if the patient lives in a particular environment, advice can also be provided based on the health risks of that environment. This makes it possible to provide more appropriate advice by providing advice based on the geographical location information of the patient. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI.

[0085] The advice unit can provide relevant advice based on the patient's social media activity when giving advice. The advice unit, for example, analyzes the patient's social media activity and provides advice based on the content. For example, if the patient posts about health on social media, advice can be provided based on the content. Information on health risks can also be collected from the patient's social media activity and advice can be provided based on the collected information. Furthermore, the patient's social media activity can be analyzed and advice can be provided based on the patient's interest in health. This makes it possible to provide more appropriate advice by providing advice based on the patient's social media activity. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The hearing section can also customize questions based on the patient's hobbies and interests. For example, if the patient is interested in sports, questions related to exercise habits and sports can be added. Also, if the patient is interested in music, questions can be asked about the impact of musical activities on health. Furthermore, if the patient likes reading, questions can be asked about how reading habits help with stress management. This allows for more personalized hearing by asking questions based on the patient's hobbies and interests.

[0088] The judgment unit can also judge the type based on the patient's occupation. For example, a patient who is engaged in a stressful occupation may be judged as an anxious type. Also, a patient who is engaged in an occupation that requires problem solving may be judged as a problem-solving oriented type. Furthermore, a patient who is engaged in an occupation that requires a lot of checking work may be judged as a checking oriented type. Thus, by judging the type based on the patient's occupation, a more accurate judgment can be made.

[0089] The advice unit can adjust the content of advice based on the cultural background of the patient. For example, a patient from a different cultural region can be provided with health advice appropriate to that culture. Also, for a patient with a religious background, advice can be given that takes into consideration that religion. Furthermore, in the case of language differences, advice can be provided in a language that is easy for the patient to understand. This allows for more appropriate treatment by providing advice based on the cultural background of the patient.

[0090] The interview department can customize questions based on the patient's digital device usage. For example, if the patient frequently uses a smartphone, they can ask questions about the impact of digital device use on their health. Also, if the patient uses a wearable device, they can ask questions based on the health data obtained from the device. Furthermore, if the patient searches for health information online, they can ask questions about the reliability of that information. This allows more appropriate information to be collected by asking questions based on the patient's digital device usage.

[0091] The determination unit can determine the type of the patient based on the patient's diet. For example, a patient who eats a balanced diet can be determined as a health-conscious type. A patient who frequently eats junk food can be determined as a type with a high health risk. Furthermore, a patient who has specific dietary restrictions can be determined as a type based on the restrictions. This allows for more accurate determination by determining the type of the patient based on the patient's diet.

[0092] The hearing unit can estimate the patient's emotions and adjust the timing of questions based on the estimated emotions. For example, if the patient is feeling anxious, questions to relax the patient can be asked first. Also, if the patient is relaxed, detailed questions can be asked earlier. Furthermore, if the patient is in a hurry, important questions can be asked first. This allows for more effective hearing by adjusting the timing of questions according to the patient's emotions.

[0093] The judgment unit can estimate the patient's emotions and adjust the type judgment criteria based on the estimated emotions. For example, if the patient is tense, the criteria for judging the patient as an anxious type can be relaxed. Also, if the patient is relaxed, the criteria for judging the patient as a problem-solving oriented type can be strengthened. Furthermore, if the patient is in a hurry, the criteria for judging the patient as a confirmation oriented type can be adjusted. In this way, by adjusting the type judgment criteria according to the patient's emotions, more accurate type judgment is possible.

[0094] The advice unit can estimate the patient's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the patient is nervous, a gentle expression can be used to provide a sense of security. If the patient is relaxed, a detailed explanation can be provided. If the patient is in a hurry, quick and concise advice can be provided. This allows for more effective advice by adjusting the way the advice is expressed according to the patient's emotions.

[0095] The advice unit can estimate the patient's emotions and determine the priority of advice based on the estimated emotions. For example, if the patient is nervous, advice that provides a sense of security can be given priority. If the patient is relaxed, advice that provides detailed explanations can be given priority. If the patient is in a hurry, quick and concise advice can be given priority. This makes it possible to respond more appropriately by determining the priority of advice according to the patient's emotions.

[0096] The advising unit can estimate the patient's emotions and adjust the content of the advice based on the estimated emotions. For example, if the patient is nervous, advice to provide a sense of security can be provided. If the patient is relaxed, advice including detailed explanations can be provided. Furthermore, if the patient is in a hurry, quick and concise advice can be provided. This allows for more effective advice by adjusting the content of the advice according to the patient's emotions.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The interview department interviews the patient. During the interview, questions can be customized based on the patient's lifestyle and environment. For example, if the patient is a smoker, questions about smoking can be added. In addition, the department can analyze the patient's social media activity and ask related questions. For example, if the patient posts about health on social media, questions can be asked based on the content of those posts. Step 2: The judgment section judges the patient's type based on the information obtained by the hearing section. For example, there are types such as anxious, problem-solving oriented, and confirmation oriented. Anxious patients often have many concerns about their health. On the other hand, problem-solving oriented patients tend to seek concrete solutions. Confirmation oriented patients also have a strong need to make sure that there are no problems. Step 3: The advice section gives advice to the doctor on how to deal with the patient based on the type of patient determined by the judgment section. For example, it is important to provide a sense of security to anxious patients. On the other hand, problem-solving oriented patients require detailed explanations. Also, confirmation oriented patients require prompt response.

[0099] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] For example, the hearing unit is realized by the microphone 38B and the control unit 46A of the smart device 14, and detects the patient's voice and asks a question. The determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes information obtained from the hearing unit to determine the type of the patient. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice to the doctor based on the determined type of the patient. The hearing unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the determination unit and the advice unit may be realized by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0106] 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.

[0107] 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 the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] 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 performed in a secure state.

[0110] Fig. 4 shows an example of 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.

[0111] 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.

[0112] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] For example, the hearing unit is realized by the microphone 238 and the control unit 46A of the smart glasses 214, and detects the patient's voice and asks a question. The determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes information obtained from the hearing unit to determine the type of the patient. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice to the doctor based on the determined type of the patient. The hearing unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the determination unit and the advice unit may be realized by, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] 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.

[0127] 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.

[0128] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 voice indicating a user input for 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the hearing unit is realized by the microphone 238 and the control unit 46A of the headset type terminal 314, and detects the voice of the patient and asks a question. The determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes information obtained from the hearing unit to determine the type of the patient. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice to the doctor based on the determined type of the patient. The hearing unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the determination unit and the advice unit may be realized by, for example, the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0143] 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.

[0144] 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.

[0145] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0151] For example, the hearing unit is realized by the microphone 238 and the control unit 46A of the robot 414, and detects the voice of the patient and asks a question. The judgment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes information obtained from the hearing unit to judge the type of the patient. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice to the doctor based on the judged type of the patient. The hearing unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the judgment unit and the advice unit may be realized by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] 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 Universal Serial Bus (USB) 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.

[0161] In addition, 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 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) A hearing department that conducts hearings with patients; A determination unit that determines a type of the patient based on the information obtained by the hearing unit; and an advice unit that gives advice to a doctor based on the type of the patient determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is determining the needs of the patient based on the information obtained by the hearing section; The advice unit is Providing advice to the physician based on the type and needs of the patient determined by the determining unit. 2. The system of claim 1, (Appendix 3) The determination unit is Identify the patient as either anxious, problem-solving, or confirmation-oriented 2. The system of claim 1, (Appendix 4) The determination unit is Determine the patient's needs among reassurance, detailed explanations, and prompt response 2. The system of claim 1, (Appendix 5) The hearing section includes: Customize questions for patients based on their lifestyle and environment 2. The system of claim 1, (Appendix 6) The hearing section includes: Ask relevant questions based on the patient's social media activity 2. The system of claim 1, (Appendix 7) The hearing section includes: Infer patient emotions and adjust question order based on inferred emotions 2. The system of claim 1, (Appendix 8) The hearing section includes: Select questions based on the patient's past medical history 2. The system of claim 1, (Appendix 9) The hearing section includes: During interviews, adapt questions based on the patient's current health status 2. The system of claim 1, (Appendix 10) The hearing section includes: Infer patient emotions and adjust question detail based on the inferred emotions 2. The system of claim 1, (Appendix 11) The hearing section includes: Prioritize relevant questions during interviews based on the patient's geographic location 2. The system of claim 1, (Appendix 12) The hearing section includes: During the interview, add relevant questions based on the patient's family history 2. The system of claim 1, (Appendix 13) The determination unit is Infer the patient's emotions and adjust the typing criteria based on the inferred emotions 2. The system of claim 1, (Appendix 14) The determination unit is When making a decision, improve the accuracy of type determination based on the patient's past medical history. 2. The system of claim 1, (Appendix 15) The determination unit is When making a decision, the type is determined based on the patient's lifestyle. 2. The system of claim 1, (Appendix 16) The determination unit is Estimate the patient's emotions and prioritize types based on the estimated emotions 2. The system of claim 1, (Appendix 17) The determination unit is When making a decision, the type is determined based on the patient's geographic location. 2. The system of claim 1, (Appendix 18) The determination unit is When making a decision, consider the patient's social media activity to determine the type of treatment. 2. The system of claim 1, (Appendix 19) The advice unit is Infer the patient's emotions and tailor the way you present your advice based on those emotions 2. The system of claim 1, (Appendix 20) The advice unit is When providing advice, provide advice based on the patient's past medical history 2. The system of claim 1, (Appendix 21) The advice unit is Customize advice based on the patient's current health status 2. The system of claim 1, (Appendix 22) The advice unit is Estimate the patient's emotions and prioritize advice based on the estimated emotions 2. The system of claim 1, (Appendix 23) The advice unit is At the time of advice, advice is provided based on the patient's geographic location 2. The system of claim 1, (Appendix 24) The advice unit is At the time of consultation, provide relevant advice based on the patient's social media activity 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A hearing department that conducts hearings with patients; A determination unit that determines a type of the patient based on the information obtained by the hearing unit; and an advice unit that gives advice to a doctor based on the type of the patient determined by the determination unit. A system characterized by:

2. The determination unit is determining the needs of the patient based on the information obtained by the hearing section; The advice unit, Providing advice to the physician based on the type and needs of the patient determined by the determining unit.

2. The system of claim 1 .

3. The determination unit is Identify the patient as either anxious, problem-solving, or confirmation-oriented 2. The system of claim 1 .

4. The determination unit is Determine the patient's needs among reassurance, detailed explanations, and prompt response 2. The system of claim 1 .

5. The hearing section includes: Customize questions for the patient based on the patient's lifestyle and environment 2. The system of claim 1 .

6. The hearing section includes: Ask relevant questions based on the patient's social media activity 2. The system of claim 1 .

7. The hearing section includes: Estimating the patient's emotions and adjusting the order of questions based on the estimated emotions.

2. The system of claim 1 .

8. The hearing section includes: Selecting questions based on the patient's past medical history 2. The system of claim 1 .

9. The hearing section includes: During the interview, modify the questions based on the patient's current health condition.

2. The system of claim 1 .

10. The hearing section includes: Estimating the patient's emotion and adjusting the question detail based on the estimated emotion.

2. The system of claim 1 .

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