System

Abstract

An object of a system according to an embodiment is to analyze information described in a medical record and quickly and accurately present the possibility of a disease, an appropriate treatment method, and a medical institution.SOLUTION: A system includes a medical chart analysis part, a disease presentation part, a treatment presentation part, and a medical institution presentation part. The medical chart analysis part analyzes information described in the medical chart by using the generation AI. The disease presentation unit presents the possibility of a disease based on the information analyzed by the medical record analysis unit. The treatment method presentation unit presents a treatment method for the disease presented by the disease presentation unit. The medical institution presentation unit presents an appropriate medical institution based on the treatment presented by the treatment presentation 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately present possible diseases, appropriate treatments, and medical institutions based on the information recorded in medical records.

[0005] The system according to the embodiment aims to analyze information recorded in a medical record and promptly and accurately present possible diseases, appropriate treatments, and medical institutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical record analysis unit, a disease presentation unit, a treatment method presentation unit, and a medical institution presentation unit. The medical record analysis unit analyzes information recorded in the medical record using a generation AI. The disease presentation unit presents the possibility of a disease based on the information analyzed by the medical record analysis unit. The treatment method presentation unit presents treatment methods for the disease presented by the disease presentation unit. The medical institution presentation unit presents appropriate medical institutions based on the treatment methods presented by the treatment method presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the information recorded in the medical records and quickly and accurately present the possibility of disease, appropriate treatment methods, and medical institutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical support system according to an embodiment of the present invention uses generative AI to assist doctors in gaining "awareness" of diseases and treatments that are difficult for them to achieve with their own knowledge. This allows the medical support system to quickly and accurately obtain information on diseases and treatments outside the doctor's area of ​​expertise, thereby improving the quality of medical examinations.

[0029] A medical support system according to an embodiment includes a medical record analysis unit, a disease presentation unit, a treatment method presentation unit, and a medical institution presentation unit. The medical record analysis unit analyzes information recorded in the medical record. For example, the medical record analysis unit uses a generation AI to analyze text data recorded in the medical record using natural language processing technology. The medical record analysis unit can also analyze image data using image analysis technology. The medical record analysis unit can also analyze audio data using audio analysis technology. The disease presentation unit presents possible diseases based on the information analyzed by the medical record analysis unit. For example, the disease presentation unit uses a generation AI to present possible diseases in a ranking format based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use a generation AI to provide basic information about diseases based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use a generation AI to present treatment methods for diseases based on the information analyzed by the medical record analysis unit. The treatment method presentation unit presents treatment methods for the diseases presented by the disease presentation unit. For example, the treatment presentation unit uses the generation AI to present treatments for the disease presented by the disease presentation unit in a ranking format. The treatment presentation unit can also use the generation AI to provide written responses regarding the risks and quality of life (QOL) of the treatments for the disease presented by the disease presentation unit. The treatment presentation unit can also use the generation AI to present potential treatments for the disease presented by the disease presentation unit. The medical institution presentation unit presents appropriate medical institutions based on the treatments presented by the treatment presentation unit. For example, the medical institution presentation unit uses the generation AI to present information about nearby medical institutions and doctors who specialize in the disease based on the treatment presented by the treatment presentation unit. The medical institution presentation unit can also use the generation AI to suggest appropriate medical institutions and doctors based on information about the doctor's location based on the treatment presented by the treatment presentation unit. This allows the medical support system according to the embodiment to gain "awareness" of diseases and treatments that are difficult for doctors to reach with their own knowledge. For example, doctors in underserved areas can now provide appropriate care for diseases outside their specialty, improving the quality of medical care for patients.In addition, it is expected that patients will be able to receive treatment more quickly as they will be referred to appropriate medical institutions more smoothly.

[0030] The medical record analysis unit takes into account the patient's past medical history and family history, allowing it to present more accurate disease possibilities. For example, when the generative AI analyzes a patient's chief complaint, the medical record analysis unit references the patient's past medical history and evaluates the correlation with past diseases. For example, if a patient who previously suffered from myocardial infarction complains of chest pain again, the medical record analysis unit will highly evaluate the possibility of recurrence. The medical record analysis unit also takes into account the patient's family history and evaluates genetic risk factors. For example, if there are many family members with diabetes, the medical record analysis unit will highly evaluate the risk of diabetes and focus on analyzing related symptoms. The medical record analysis unit can also simultaneously present the possibility of multiple diseases based on the patient's past medical history and family history. For example, if a patient who previously suffered from high blood pressure complains of headaches, the medical record analysis unit will present the risk of hypertensive encephalopathy and stroke. This allows for more accurate disease possibilities by taking into account the patient's past medical history and family history.

[0031] The medical record analysis unit can input information about a patient's lifestyle and work environment and assess their disease risk based on that information. For example, the medical record analysis unit uses generative AI to analyze a patient's lifestyle data and evaluate risk factors. For example, if a patient with a smoking habit complains of a cough, the unit will assess their risk of lung cancer or chronic obstructive pulmonary disease (COPD). The medical record analysis unit also inputs information about the patient's work environment and assesses their risk of occupational disease. For example, if a factory worker complains of itchy skin, the unit will suggest the possibility of dermatitis caused by chemicals. The medical record analysis unit can also simultaneously assess the risk of multiple diseases based on lifestyle and work environment. For example, if a patient who does a lot of desk work complains of lower back pain, the unit will suggest the risk of herniated discs and myofascial lower back pain. This allows for the assessment of disease risk based on a patient's lifestyle and work environment.

[0032] The medical record analysis unit can also use voice input to collect information from a patient's speaking style and tone of voice to evaluate the possibility of disease. For example, the medical record analysis unit uses voice input to analyze a patient's speaking style and tone of voice to evaluate emotions and tension. For example, if a patient with a trembling voice complains of chest pain, it can suggest the possibility of psychogenic chest pain. The medical record analysis unit can also evaluate the presence or absence of dyspnea or wheezing from the patient's speaking style and tone of voice. For example, if a patient with a breathless voice complains of coughing, it can evaluate the patient's risk of asthma or COPD. The medical record analysis unit can also analyze voice data to evaluate the patient's stress and anxiety levels from the patient's speaking style and tone of voice. For example, if a patient with a weak voice complains of fatigue, it can suggest the patient's risk of depression or chronic fatigue syndrome. In this way, it is possible to collect information from a patient's speaking style and tone of voice using voice input to evaluate the possibility of disease.

[0033] The medical record analysis unit can also use image analysis technology to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease. For example, the medical record analysis unit can use image analysis technology to analyze the patient's facial expressions and evaluate the level of pain or discomfort. For example, if a patient who is grimacing complains of abdominal pain, the risk of acute abdominal disease can be evaluated as high. The medical record analysis unit can also evaluate specific symptoms or abnormalities from the patient's body movements. For example, if a patient with an unsteady gait complains of dizziness, the risk of stroke or inner ear disorder can be indicated. The medical record analysis unit can also analyze image data to evaluate the patient's stress and anxiety levels from the patient's facial expressions and body movements. For example, if a restless patient complains of insomnia, the risk of anxiety disorder or depression can be indicated. In this way, the image analysis technology can be used to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease.

[0034] The treatment suggestion unit takes into account the patient's genetic information and can propose the optimal treatment from the perspective of personalized medicine. For example, the treatment suggestion unit uses a generative AI to analyze the patient's genetic information and evaluate their responsiveness to a specific treatment. For example, it proposes effective drug treatment for a patient with a specific gene mutation. The treatment suggestion unit also evaluates the risk of side effects of the treatment based on the patient's genetic information. For example, it may highly evaluate the risk that a specific gene mutation will cause drug side effects and propose an alternative treatment. The treatment suggestion unit also takes into account genetic information and proposes the optimal treatment from the perspective of personalized medicine. For example, it may present the optimal treatment for the patient in a ranking format based on the results of a genetic test. This makes it possible to propose the optimal treatment from the perspective of personalized medicine by taking into account the patient's genetic information.

[0035] The treatment presentation unit can refer to the latest medical research database and provide the latest treatments and clinical trial information. For example, the generative AI in the treatment presentation unit refers to the latest medical research database and provides the latest research results for the presented treatment. For example, it provides information on the effectiveness and side effects of new drug treatments. The treatment presentation unit also suggests treatments suitable for the patient based on clinical trial information. For example, it presents treatments that can be obtained by participating in ongoing clinical trials. The treatment presentation unit also refers to the latest medical research database and provides the latest information on the effectiveness and safety of treatments. For example, it evaluates the success rate and risks of new treatments. In this way, by referring to the latest medical research database, the latest treatments and clinical trial information can be provided.

[0036] The treatment method presentation unit can collect the opinions of other medical experts in real time and evaluate treatment methods from multiple perspectives. For example, the treatment method presentation unit uses a generative AI to collect the opinions of other medical experts in real time and reflect them in the evaluation of the treatment method. For example, it evaluates the effectiveness of a treatment method based on the opinions of specialists. The treatment method presentation unit also collects the opinions of other medical experts and evaluates treatment options from multiple perspectives. For example, it integrates the opinions of multiple experts and proposes the optimal treatment method. The treatment method presentation unit also builds a system that evaluates treatment methods based on the expert opinions collected in real time. For example, it presents treatment methods in a ranking format based on the expert evaluation scores. In this way, by collecting the opinions of other medical experts in real time, treatment methods can be evaluated from multiple perspectives.

[0037] The treatment suggestion unit can propose cost-effective treatments by taking into account the patient's financial situation. For example, the treatment suggestion unit uses a generative AI to analyze the patient's financial situation and propose cost-effective treatments. For example, it compares treatment costs and effectiveness and presents the optimal treatment. The treatment suggestion unit also considers the patient's insurance coverage and proposes treatments that are least financially burdensome. For example, it presents treatments that are covered by insurance as a priority. The treatment suggestion unit also builds a system that evaluates treatment options based on the patient's financial situation. For example, it comprehensively evaluates treatment costs and effectiveness and presents the optimal treatments in a ranking format. This makes it possible to propose cost-effective treatments by taking the patient's financial situation into consideration.

[0038] The medical institution presentation unit can suggest the most reliable medical institutions by referring to past patient treatment results and satisfaction data. For example, the generation AI analyzes past patient treatment result data and suggests highly reliable medical institutions. For example, it prioritizes the presentation of hospitals with a high treatment success rate. The medical institution presentation unit also evaluates highly reliable medical institutions based on patient satisfaction data. For example, it presents medical institutions with high patient ratings in a ranking format. The medical institution presentation unit also refers to past patient data and suggests the most suitable medical institution based on treatment results and satisfaction. For example, it presents hospitals with an extensive track record of treating a specific disease. In this way, the most reliable medical institutions can be suggested by referring to past patient treatment results and satisfaction data.

[0039] The medical institution presentation unit can provide detailed information such as the facilities of the medical institution and the years of experience of the specialists. For example, the generation AI analyzes the facilities information of the medical institution and suggests the most suitable medical institution. For example, it may preferentially suggest hospitals equipped with the latest medical equipment. The medical institution presentation unit also evaluates reliable medical institutions based on the years of experience and track record of the specialists. For example, it may suggest hospitals with experienced specialists. The medical institution presentation unit also provides detailed information about the medical institution and suggests the most suitable medical institution for the patient. For example, it may suggest hospitals with equipment specialized for specific treatments. In this way, by providing detailed information such as the facilities of the medical institution and the years of experience of the specialists, the most suitable medical institution can be suggested.

[0040] The medical institution presentation unit can collect reviews and ratings from other patients in real time and provide a comprehensive evaluation. For example, the generation AI in the medical institution presentation unit collects reviews and ratings from other patients in real time and provides a comprehensive evaluation of the medical institution. For example, it updates the evaluation of the medical institution based on the latest reviews. The medical institution presentation unit also evaluates medical institution options from multiple angles based on the evaluation data of other patients. For example, it integrates multiple reviews and presents a comprehensive evaluation. The medical institution presentation unit also builds a system that evaluates medical institutions based on the reviews and ratings collected in real time. For example, it presents medical institutions in a ranking format based on the evaluation score. This makes it possible to provide a comprehensive evaluation by collecting reviews and ratings from other patients in real time.

[0041] The medical institution presentation unit can provide convenience information such as transportation access and waiting times. For example, the generation AI analyzes transportation access information for medical institutions and suggests the most suitable medical institution. For example, it may prioritize presenting hospitals with good public transportation access. The medical institution presentation unit also evaluates medical institutions with high convenience based on waiting time data for medical institutions. For example, it may present hospitals with short waiting times in a ranking format. The medical institution presentation unit also provides convenience information and suggests the most suitable medical institution for the patient. For example, it may present medical institutions taking into consideration the availability of parking and consultation hours. In this way, by providing convenience information such as transportation access and waiting times, it is possible to suggest the most suitable medical institution for the patient.

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

[0043] The medical record analysis unit can input information about a patient's lifestyle and work environment and assess their disease risk based on that information. For example, the medical record analysis unit uses generative AI to analyze a patient's lifestyle data and evaluate risk factors. For example, if a patient with a smoking habit complains of a cough, the unit will assess their risk of lung cancer or chronic obstructive pulmonary disease (COPD). The medical record analysis unit also inputs information about the patient's work environment and assesses their risk of occupational disease. For example, if a factory worker complains of itchy skin, the unit will suggest the possibility of dermatitis caused by chemicals. The medical record analysis unit can also simultaneously assess the risk of multiple diseases based on lifestyle and work environment. For example, if a patient who does a lot of desk work complains of lower back pain, the unit will suggest the risk of herniated discs and myofascial lower back pain. This allows for the assessment of disease risk based on a patient's lifestyle and work environment.

[0044] The medical record analysis unit can also use voice input to collect information from a patient's speaking style and tone of voice to evaluate the possibility of disease. For example, the medical record analysis unit uses voice input to analyze a patient's speaking style and tone of voice to evaluate emotions and tension. For example, if a patient with a trembling voice complains of chest pain, it can suggest the possibility of psychogenic chest pain. The medical record analysis unit can also evaluate the presence or absence of dyspnea or wheezing from the patient's speaking style and tone of voice. For example, if a patient with a breathless voice complains of coughing, it can evaluate the patient's risk of asthma or COPD. The medical record analysis unit can also analyze voice data to evaluate the patient's stress and anxiety levels from the patient's speaking style and tone of voice. For example, if a patient with a weak voice complains of fatigue, it can suggest the patient's risk of depression or chronic fatigue syndrome. In this way, it is possible to collect information from a patient's speaking style and tone of voice using voice input to evaluate the possibility of disease.

[0045] The medical record analysis unit can also use image analysis technology to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease. For example, the medical record analysis unit can use image analysis technology to analyze the patient's facial expressions and evaluate the level of pain or discomfort. For example, if a patient who is grimacing complains of abdominal pain, the risk of acute abdominal disease can be evaluated as high. The medical record analysis unit can also evaluate specific symptoms or abnormalities from the patient's body movements. For example, if a patient with an unsteady gait complains of dizziness, the risk of stroke or inner ear disorder can be indicated. The medical record analysis unit can also analyze image data to evaluate the patient's stress and anxiety levels from the patient's facial expressions and body movements. For example, if a restless patient complains of insomnia, the risk of anxiety disorder or depression can be indicated. In this way, the image analysis technology can be used to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease.

[0046] The treatment suggestion unit takes into account the patient's genetic information and can propose the optimal treatment from the perspective of personalized medicine. For example, the treatment suggestion unit uses a generative AI to analyze the patient's genetic information and evaluate their responsiveness to a specific treatment. For example, it proposes effective drug treatment for a patient with a specific gene mutation. The treatment suggestion unit also evaluates the risk of side effects of the treatment based on the patient's genetic information. For example, it may highly evaluate the risk that a specific gene mutation will cause drug side effects and propose an alternative treatment. The treatment suggestion unit also takes into account genetic information and proposes the optimal treatment from the perspective of personalized medicine. For example, it may present the optimal treatment for the patient in a ranking format based on the results of a genetic test. This makes it possible to propose the optimal treatment from the perspective of personalized medicine by taking into account the patient's genetic information.

[0047] The treatment presentation unit can refer to the latest medical research database and provide the latest treatments and clinical trial information. For example, the generative AI in the treatment presentation unit refers to the latest medical research database and provides the latest research results for the presented treatment. For example, it provides information on the effectiveness and side effects of new drug treatments. The treatment presentation unit also suggests treatments suitable for the patient based on clinical trial information. For example, it presents treatments that can be obtained by participating in ongoing clinical trials. The treatment presentation unit also refers to the latest medical research database and provides the latest information on the effectiveness and safety of treatments. For example, it evaluates the success rate and risks of new treatments. In this way, by referring to the latest medical research database, the latest treatments and clinical trial information can be provided.

[0048] The treatment method presentation unit can collect the opinions of other medical experts in real time and evaluate treatment methods from multiple perspectives. For example, the treatment method presentation unit uses a generative AI to collect the opinions of other medical experts in real time and reflect them in the evaluation of the treatment method. For example, it evaluates the effectiveness of a treatment method based on the opinions of specialists. The treatment method presentation unit also collects the opinions of other medical experts and evaluates treatment options from multiple perspectives. For example, it integrates the opinions of multiple experts and proposes the optimal treatment method. The treatment method presentation unit also builds a system that evaluates treatment methods based on the expert opinions collected in real time. For example, it presents treatment methods in a ranking format based on the expert evaluation scores. In this way, by collecting the opinions of other medical experts in real time, treatment methods can be evaluated from multiple perspectives.

[0049] The treatment suggestion unit can propose cost-effective treatments by taking into account the patient's financial situation. For example, the treatment suggestion unit uses a generative AI to analyze the patient's financial situation and propose cost-effective treatments. For example, it compares treatment costs and effectiveness and presents the optimal treatment. The treatment suggestion unit also considers the patient's insurance coverage and proposes treatments that are least financially burdensome. For example, it presents treatments that are covered by insurance as a priority. The treatment suggestion unit also builds a system that evaluates treatment options based on the patient's financial situation. For example, it comprehensively evaluates treatment costs and effectiveness and presents the optimal treatments in a ranking format. This makes it possible to propose cost-effective treatments by taking the patient's financial situation into consideration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The medical record analysis unit analyzes the information recorded in the medical record. For example, the medical record analysis unit uses generative AI to analyze the text data recorded in the medical record using natural language processing technology. The medical record analysis unit can also analyze image data using image analysis technology. Furthermore, the medical record analysis unit can also analyze audio data using audio analysis technology. Step 2: The disease presentation unit presents possible diseases based on the information analyzed by the medical record analysis unit. For example, the disease presentation unit uses the generation AI to present possible diseases in a ranking format based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use the generation AI to provide basic information about diseases based on the information analyzed by the medical record analysis unit. Step 3: The treatment presentation unit presents treatments for the diseases presented by the disease presentation unit. For example, the treatment presentation unit uses a generation AI to present treatments for the diseases presented by the disease presentation unit in a ranked format. The treatment presentation unit can also use a generation AI to provide written responses regarding the risks and quality of life (QOL) of the treatments for the diseases presented by the disease presentation unit. Step 4: The medical institution presentation unit presents appropriate medical institutions based on the treatment presented by the treatment presentation unit. For example, the medical institution presentation unit uses the generation AI to present information about nearby medical institutions and doctors who specialize in the disease based on the treatment presented by the treatment presentation unit. The medical institution presentation unit can also use the generation AI to suggest appropriate medical institutions and doctors based on information about the doctor's location based on the treatment presented by the treatment presentation unit.

[0052] (Example 2) The medical support system according to an embodiment of the present invention uses generative AI to assist doctors in gaining "awareness" of diseases and treatments that are difficult for them to achieve with their own knowledge. This allows the medical support system to quickly and accurately obtain information on diseases and treatments outside the doctor's area of ​​expertise, thereby improving the quality of medical examinations.

[0053] A medical support system according to an embodiment includes a medical record analysis unit, a disease presentation unit, a treatment method presentation unit, and a medical institution presentation unit. The medical record analysis unit analyzes information recorded in the medical record. For example, the medical record analysis unit uses a generation AI to analyze text data recorded in the medical record using natural language processing technology. The medical record analysis unit can also analyze image data using image analysis technology. The medical record analysis unit can also analyze audio data using audio analysis technology. The disease presentation unit presents possible diseases based on the information analyzed by the medical record analysis unit. For example, the disease presentation unit uses a generation AI to present possible diseases in a ranking format based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use a generation AI to provide basic information about diseases based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use a generation AI to present treatment methods for diseases based on the information analyzed by the medical record analysis unit. The treatment method presentation unit presents treatment methods for the diseases presented by the disease presentation unit. For example, the treatment presentation unit uses the generation AI to present treatments for the disease presented by the disease presentation unit in a ranking format. The treatment presentation unit can also use the generation AI to provide written responses regarding the risks and quality of life (QOL) of the treatments for the disease presented by the disease presentation unit. The treatment presentation unit can also use the generation AI to present potential treatments for the disease presented by the disease presentation unit. The medical institution presentation unit presents appropriate medical institutions based on the treatments presented by the treatment presentation unit. For example, the medical institution presentation unit uses the generation AI to present information about nearby medical institutions and doctors who specialize in the disease based on the treatment presented by the treatment presentation unit. The medical institution presentation unit can also use the generation AI to suggest appropriate medical institutions and doctors based on information about the doctor's location based on the treatment presented by the treatment presentation unit. This allows the medical support system according to the embodiment to gain "awareness" of diseases and treatments that are difficult for doctors to reach with their own knowledge. For example, doctors in underserved areas can now provide appropriate care for diseases outside their specialty, improving the quality of medical care for patients.In addition, it is expected that patients will be able to receive treatment more quickly as they will be referred to appropriate medical institutions more smoothly.

[0054] The medical record analysis unit takes into account the patient's past medical history and family history, allowing it to present more accurate disease possibilities. For example, when the generative AI analyzes a patient's chief complaint, the medical record analysis unit references the patient's past medical history and evaluates the correlation with past diseases. For example, if a patient who previously suffered from myocardial infarction complains of chest pain again, the medical record analysis unit will highly evaluate the possibility of recurrence. The medical record analysis unit also takes into account the patient's family history and evaluates genetic risk factors. For example, if there are many family members with diabetes, the medical record analysis unit will highly evaluate the risk of diabetes and focus on analyzing related symptoms. The medical record analysis unit can also simultaneously present the possibility of multiple diseases based on the patient's past medical history and family history. For example, if a patient who previously suffered from high blood pressure complains of headaches, the medical record analysis unit will present the risk of hypertensive encephalopathy and stroke. This allows for more accurate disease possibilities by taking into account the patient's past medical history and family history.

[0055] The medical record analysis unit can input information about a patient's lifestyle and work environment and assess their disease risk based on that information. For example, the medical record analysis unit uses generative AI to analyze a patient's lifestyle data and evaluate risk factors. For example, if a patient with a smoking habit complains of a cough, the unit will assess their risk of lung cancer or chronic obstructive pulmonary disease (COPD). The medical record analysis unit also inputs information about the patient's work environment and assesses their risk of occupational disease. For example, if a factory worker complains of itchy skin, the unit will suggest the possibility of dermatitis caused by chemicals. The medical record analysis unit can also simultaneously assess the risk of multiple diseases based on lifestyle and work environment. For example, if a patient who does a lot of desk work complains of lower back pain, the unit will suggest the risk of herniated discs and myofascial lower back pain. This allows for the assessment of disease risk based on a patient's lifestyle and work environment.

[0056] The medical record analysis unit can use the emotion estimation function to analyze the patient's emotional response to the patient's chief complaint and present possible diseases taking into account the impact of stress and anxiety on the disease. For example, the medical record analysis unit uses the emotion estimation function to analyze the patient's emotional response to the patient's chief complaint and evaluate the impact of stress on the disease. For example, if a patient experiencing strong anxiety complains of chest pain, the medical record analysis unit presents the possibility of psychogenic chest pain. The medical record analysis unit also evaluates the risk of stress-related diseases based on the patient's emotional response data. For example, if a patient experiencing long-term stress complains of stomach pain, the medical record analysis unit highly evaluates the risk of gastric ulcers and irritable bowel syndrome. The medical record analysis unit also uses the emotion estimation data to consider the impact of stress and anxiety on the disease and present the possibility of multiple diseases simultaneously. For example, if a patient experiencing strong stress complains of a headache, the medical record analysis unit presents the risk of tension headaches and migraines. In this way, the impact of stress and anxiety on disease can be evaluated by taking the patient's emotional response into account.

[0057] The medical record analysis unit can also use voice input to collect information from a patient's speaking style and tone of voice to evaluate the possibility of disease. For example, the medical record analysis unit uses voice input to analyze a patient's speaking style and tone of voice to evaluate emotions and tension. For example, if a patient with a trembling voice complains of chest pain, it can suggest the possibility of psychogenic chest pain. The medical record analysis unit can also evaluate the presence or absence of dyspnea or wheezing from the patient's speaking style and tone of voice. For example, if a patient with a breathless voice complains of coughing, it can evaluate the patient's risk of asthma or COPD. The medical record analysis unit can also analyze voice data to evaluate the patient's stress and anxiety levels from the patient's speaking style and tone of voice. For example, if a patient with a weak voice complains of fatigue, it can suggest the patient's risk of depression or chronic fatigue syndrome. In this way, it is possible to collect information from a patient's speaking style and tone of voice using voice input to evaluate the possibility of disease.

[0058] The medical record analysis unit can also use image analysis technology to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease. For example, the medical record analysis unit can use image analysis technology to analyze the patient's facial expressions and evaluate the level of pain or discomfort. For example, if a patient who is grimacing complains of abdominal pain, the risk of acute abdominal disease can be evaluated as high. The medical record analysis unit can also evaluate specific symptoms or abnormalities from the patient's body movements. For example, if a patient with an unsteady gait complains of dizziness, the risk of stroke or inner ear disorder can be indicated. The medical record analysis unit can also analyze image data to evaluate the patient's stress and anxiety levels from the patient's facial expressions and body movements. For example, if a restless patient complains of insomnia, the risk of anxiety disorder or depression can be indicated. In this way, the image analysis technology can be used to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease.

[0059] The medical record analysis unit can use the emotion estimation function to analyze the doctor's emotional response to the patient's chief complaint and present possible diseases without the doctor's bias. The medical record analysis unit, for example, uses the emotion estimation function to analyze the doctor's emotional response and present possible diseases without the doctor's bias. For example, if the doctor is overly cautious about a particular disease, the unit corrects that bias. The medical record analysis unit also evaluates disease risk without bias based on the doctor's emotional response data. For example, it ensures that the doctor's past trauma does not affect the diagnosis. The medical record analysis unit also uses the emotion estimation data to correct the doctor's emotional bias and present an objective disease risk. For example, if the doctor is downplaying a particular symptom, the unit corrects that bias. This makes it possible to present an objective disease risk by eliminating the doctor's emotional bias.

[0060] The treatment suggestion unit takes into account the patient's genetic information and can propose the optimal treatment from the perspective of personalized medicine. For example, the treatment suggestion unit uses a generative AI to analyze the patient's genetic information and evaluate their responsiveness to a specific treatment. For example, it proposes effective drug treatment for a patient with a specific gene mutation. The treatment suggestion unit also evaluates the risk of side effects of the treatment based on the patient's genetic information. For example, it may highly evaluate the risk that a specific gene mutation will cause drug side effects and propose an alternative treatment. The treatment suggestion unit also takes into account genetic information and proposes the optimal treatment from the perspective of personalized medicine. For example, it may present the optimal treatment for the patient in a ranking format based on the results of a genetic test. This makes it possible to propose the optimal treatment from the perspective of personalized medicine by taking into account the patient's genetic information.

[0061] The treatment presentation unit can refer to the latest medical research database and provide the latest treatments and clinical trial information. For example, the generative AI in the treatment presentation unit refers to the latest medical research database and provides the latest research results for the presented treatment. For example, it provides information on the effectiveness and side effects of new drug treatments. The treatment presentation unit also suggests treatments suitable for the patient based on clinical trial information. For example, it presents treatments that can be obtained by participating in ongoing clinical trials. The treatment presentation unit also refers to the latest medical research database and provides the latest information on the effectiveness and safety of treatments. For example, it evaluates the success rate and risks of new treatments. In this way, by referring to the latest medical research database, the latest treatments and clinical trial information can be provided.

[0062] The treatment suggestion unit can use the emotion estimation function to analyze the patient's emotional response to a treatment and suggest the treatment that is most acceptable to the patient. The treatment suggestion unit, for example, uses the emotion estimation function to analyze the patient's emotional response to a treatment and suggest the treatment that is most acceptable to the patient. For example, it avoids treatments that make the patient feel anxious and suggests treatments that give the patient a sense of security. The treatment suggestion unit also evaluates treatment options based on the patient's emotional response data. For example, it preferentially suggests treatments that the patient feels positive about. The treatment suggestion unit also uses the emotion estimation data to present the treatments that the patient is most likely to accept in a ranking format. For example, treatments with high emotion scores are displayed at the top. In this way, it is possible to suggest the treatment that the patient is most likely to accept by taking the patient's emotional response into consideration.

[0063] The treatment method presentation unit can collect the opinions of other medical experts in real time and evaluate treatment methods from multiple perspectives. For example, the treatment method presentation unit uses a generative AI to collect the opinions of other medical experts in real time and reflect them in the evaluation of the treatment method. For example, it evaluates the effectiveness of a treatment method based on the opinions of specialists. The treatment method presentation unit also collects the opinions of other medical experts and evaluates treatment options from multiple perspectives. For example, it integrates the opinions of multiple experts and proposes the optimal treatment method. The treatment method presentation unit also builds a system that evaluates treatment methods based on the expert opinions collected in real time. For example, it presents treatment methods in a ranking format based on the expert evaluation scores. In this way, by collecting the opinions of other medical experts in real time, treatment methods can be evaluated from multiple perspectives.

[0064] The treatment suggestion unit can propose cost-effective treatments by taking into account the patient's financial situation. For example, the treatment suggestion unit uses a generative AI to analyze the patient's financial situation and propose cost-effective treatments. For example, it compares treatment costs and effectiveness and presents the optimal treatment. The treatment suggestion unit also considers the patient's insurance coverage and proposes treatments that are least financially burdensome. For example, it presents treatments that are covered by insurance as a priority. The treatment suggestion unit also builds a system that evaluates treatment options based on the patient's financial situation. For example, it comprehensively evaluates treatment costs and effectiveness and presents the optimal treatments in a ranking format. This makes it possible to propose cost-effective treatments by taking the patient's financial situation into consideration.

[0065] The treatment method presentation unit can use the emotion estimation function to analyze the doctor's emotional response to the treatment method and propose the treatment method that the doctor trusts most. The treatment method presentation unit, for example, uses the emotion estimation function to analyze the doctor's emotional response to the treatment method and propose a highly reliable treatment method. For example, it preferentially presents treatment methods that the doctor has positive emotions about. The treatment method presentation unit also evaluates treatment options based on the doctor's emotional response data. For example, it presents the treatment methods that the doctor trusts in a ranking format. The treatment method presentation unit also uses the emotion estimation data to identify the treatment method that the doctor trusts most and proposes that treatment method to the patient. For example, treatment methods with high emotional scores for the doctor are displayed at the top. In this way, the treatment method that the doctor trusts most can be proposed by taking the doctor's emotional response into consideration.

[0066] The medical institution presentation unit can suggest the most reliable medical institutions by referring to past patient treatment results and satisfaction data. For example, the generation AI analyzes past patient treatment result data and suggests highly reliable medical institutions. For example, it prioritizes the presentation of hospitals with a high treatment success rate. The medical institution presentation unit also evaluates highly reliable medical institutions based on patient satisfaction data. For example, it presents medical institutions with high patient ratings in a ranking format. The medical institution presentation unit also refers to past patient data and suggests the most suitable medical institution based on treatment results and satisfaction. For example, it presents hospitals with an extensive track record of treating a specific disease. In this way, the most reliable medical institutions can be suggested by referring to past patient treatment results and satisfaction data.

[0067] The medical institution presentation unit can provide detailed information such as the facilities of the medical institution and the years of experience of the specialists. For example, the generation AI analyzes the facilities information of the medical institution and suggests the most suitable medical institution. For example, it may preferentially suggest hospitals equipped with the latest medical equipment. The medical institution presentation unit also evaluates reliable medical institutions based on the years of experience and track record of the specialists. For example, it may suggest hospitals with experienced specialists. The medical institution presentation unit also provides detailed information about the medical institution and suggests the most suitable medical institution for the patient. For example, it may suggest hospitals with equipment specialized for specific treatments. In this way, by providing detailed information such as the facilities of the medical institution and the years of experience of the specialists, the most suitable medical institution can be suggested.

[0068] The medical institution presentation unit can use the emotion estimation function to analyze the patient's emotional response to medical institutions and suggest medical institutions where the patient can feel most comfortable. The medical institution presentation unit, for example, uses the emotion estimation function to analyze the patient's emotional response to medical institutions and suggest medical institutions where the patient can feel most comfortable. For example, it preferentially presents medical institutions where the patient has positive emotions. The medical institution presentation unit also evaluates medical institution options based on the patient's emotional response data. For example, it presents medical institutions where the patient feels most comfortable in a ranking format. The medical institution presentation unit also uses the emotion estimation data to identify medical institutions where the patient can feel most comfortable and suggest those medical institutions. For example, medical institutions with high patient emotion scores are displayed at the top. In this way, by taking the patient's emotional response into consideration, it is possible to suggest medical institutions where the patient can feel most comfortable.

[0069] The medical institution presentation unit can collect reviews and ratings from other patients in real time and provide a comprehensive evaluation. For example, the generation AI in the medical institution presentation unit collects reviews and ratings from other patients in real time and provides a comprehensive evaluation of the medical institution. For example, it updates the evaluation of the medical institution based on the latest reviews. The medical institution presentation unit also evaluates medical institution options from multiple angles based on the evaluation data of other patients. For example, it integrates multiple reviews and presents a comprehensive evaluation. The medical institution presentation unit also builds a system that evaluates medical institutions based on the reviews and ratings collected in real time. For example, it presents medical institutions in a ranking format based on the evaluation score. This makes it possible to provide a comprehensive evaluation by collecting reviews and ratings from other patients in real time.

[0070] The medical institution presentation unit can provide convenience information such as transportation access and waiting times. For example, the generation AI analyzes transportation access information for medical institutions and suggests the most suitable medical institution. For example, it may prioritize presenting hospitals with good public transportation access. The medical institution presentation unit also evaluates medical institutions with high convenience based on waiting time data for medical institutions. For example, it may present hospitals with short waiting times in a ranking format. The medical institution presentation unit also provides convenience information and suggests the most suitable medical institution for the patient. For example, it may present medical institutions taking into consideration the availability of parking and consultation hours. In this way, by providing convenience information such as transportation access and waiting times, it is possible to suggest the most suitable medical institution for the patient.

[0071] The medical institution presentation unit can use the emotion estimation function to analyze the doctor's emotional response to medical institutions and suggest the medical institution that the doctor trusts most. The medical institution presentation unit, for example, uses the emotion estimation function to analyze the doctor's emotional response to medical institutions and suggest highly reliable medical institutions. For example, it preferentially presents medical institutions for which the doctor has positive emotions. The medical institution presentation unit also evaluates medical institution options based on the doctor's emotional response data. For example, it presents medical institutions that the doctor trusts in a ranking format. The medical institution presentation unit also uses the emotion estimation data to identify the medical institution that the doctor trusts most and suggest those medical institutions to the patient. For example, medical institutions with high doctor emotion scores are displayed at the top. In this way, the medical institution that the doctor trusts most can be suggested by taking the doctor's emotional response into consideration.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The medical record analysis unit can input information about a patient's lifestyle and work environment and assess their disease risk based on that information. For example, the medical record analysis unit uses generative AI to analyze a patient's lifestyle data and evaluate risk factors. For example, if a patient with a smoking habit complains of a cough, the unit will assess their risk of lung cancer or chronic obstructive pulmonary disease (COPD). The medical record analysis unit also inputs information about the patient's work environment and assesses their risk of occupational disease. For example, if a factory worker complains of itchy skin, the unit will suggest the possibility of dermatitis caused by chemicals. The medical record analysis unit can also simultaneously assess the risk of multiple diseases based on lifestyle and work environment. For example, if a patient who does a lot of desk work complains of lower back pain, the unit will suggest the risk of herniated discs and myofascial lower back pain. This allows for the assessment of disease risk based on a patient's lifestyle and work environment.

[0074] The medical record analysis unit can use the emotion estimation function to analyze the patient's emotional response to the patient's chief complaint and present possible diseases taking into account the impact of stress and anxiety on the disease. For example, the medical record analysis unit uses the emotion estimation function to analyze the patient's emotional response to the patient's chief complaint and evaluate the impact of stress on the disease. For example, if a patient experiencing strong anxiety complains of chest pain, the medical record analysis unit presents the possibility of psychogenic chest pain. The medical record analysis unit also evaluates the risk of stress-related diseases based on the patient's emotional response data. For example, if a patient experiencing long-term stress complains of stomach pain, the medical record analysis unit highly evaluates the risk of gastric ulcers and irritable bowel syndrome. The medical record analysis unit also uses the emotion estimation data to consider the impact of stress and anxiety on the disease and present the possibility of multiple diseases simultaneously. For example, if a patient experiencing strong stress complains of a headache, the medical record analysis unit presents the risk of tension headaches and migraines. In this way, the impact of stress and anxiety on disease can be evaluated by taking the patient's emotional response into account.

[0075] The medical record analysis unit can also use voice input to collect information from a patient's speaking style and tone of voice to evaluate the possibility of disease. For example, the medical record analysis unit uses voice input to analyze a patient's speaking style and tone of voice to evaluate emotions and tension. For example, if a patient with a trembling voice complains of chest pain, it can suggest the possibility of psychogenic chest pain. The medical record analysis unit can also evaluate the presence or absence of dyspnea or wheezing from the patient's speaking style and tone of voice. For example, if a patient with a breathless voice complains of coughing, it can evaluate the patient's risk of asthma or COPD. The medical record analysis unit can also analyze voice data to evaluate the patient's stress and anxiety levels from the patient's speaking style and tone of voice. For example, if a patient with a weak voice complains of fatigue, it can suggest the patient's risk of depression or chronic fatigue syndrome. In this way, it is possible to collect information from a patient's speaking style and tone of voice using voice input to evaluate the possibility of disease.

[0076] The medical record analysis unit can also use image analysis technology to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease. For example, the medical record analysis unit can use image analysis technology to analyze the patient's facial expressions and evaluate the level of pain or discomfort. For example, if a patient who is grimacing complains of abdominal pain, the risk of acute abdominal disease can be evaluated as high. The medical record analysis unit can also evaluate specific symptoms or abnormalities from the patient's body movements. For example, if a patient with an unsteady gait complains of dizziness, the risk of stroke or inner ear disorder can be indicated. The medical record analysis unit can also analyze image data to evaluate the patient's stress and anxiety levels from the patient's facial expressions and body movements. For example, if a restless patient complains of insomnia, the risk of anxiety disorder or depression can be indicated. In this way, the image analysis technology can be used to collect information from the patient's facial expressions and body movements to evaluate the possibility of disease.

[0077] The medical record analysis unit can use the emotion estimation function to analyze the doctor's emotional response to the patient's chief complaint and present possible diseases without the doctor's bias. The medical record analysis unit, for example, uses the emotion estimation function to analyze the doctor's emotional response and present possible diseases without the doctor's bias. For example, if the doctor is overly cautious about a particular disease, the unit corrects that bias. The medical record analysis unit also evaluates disease risk without bias based on the doctor's emotional response data. For example, it ensures that the doctor's past trauma does not affect the diagnosis. The medical record analysis unit also uses the emotion estimation data to correct the doctor's emotional bias and present an objective disease risk. For example, if the doctor is downplaying a particular symptom, the unit corrects that bias. This makes it possible to present an objective disease risk by eliminating the doctor's emotional bias.

[0078] The treatment suggestion unit takes into account the patient's genetic information and can propose the optimal treatment from the perspective of personalized medicine. For example, the treatment suggestion unit uses a generative AI to analyze the patient's genetic information and evaluate their responsiveness to a specific treatment. For example, it proposes effective drug treatment for a patient with a specific gene mutation. The treatment suggestion unit also evaluates the risk of side effects of the treatment based on the patient's genetic information. For example, it may highly evaluate the risk that a specific gene mutation will cause drug side effects and propose an alternative treatment. The treatment suggestion unit also takes into account genetic information and proposes the optimal treatment from the perspective of personalized medicine. For example, it may present the optimal treatment for the patient in a ranking format based on the results of a genetic test. This makes it possible to propose the optimal treatment from the perspective of personalized medicine by taking into account the patient's genetic information.

[0079] The treatment presentation unit can refer to the latest medical research database and provide the latest treatments and clinical trial information. For example, the generative AI in the treatment presentation unit refers to the latest medical research database and provides the latest research results for the presented treatment. For example, it provides information on the effectiveness and side effects of new drug treatments. The treatment presentation unit also suggests treatments suitable for the patient based on clinical trial information. For example, it presents treatments that can be obtained by participating in ongoing clinical trials. The treatment presentation unit also refers to the latest medical research database and provides the latest information on the effectiveness and safety of treatments. For example, it evaluates the success rate and risks of new treatments. In this way, by referring to the latest medical research database, the latest treatments and clinical trial information can be provided.

[0080] The treatment suggestion unit can use the emotion estimation function to analyze the patient's emotional response to a treatment and suggest the treatment that is most acceptable to the patient. The treatment suggestion unit, for example, uses the emotion estimation function to analyze the patient's emotional response to a treatment and suggest the treatment that is most acceptable to the patient. For example, it avoids treatments that make the patient feel anxious and suggests treatments that give the patient a sense of security. The treatment suggestion unit also evaluates treatment options based on the patient's emotional response data. For example, it preferentially suggests treatments that the patient feels positive about. The treatment suggestion unit also uses the emotion estimation data to present the treatments that the patient is most likely to accept in a ranking format. For example, treatments with high emotion scores are displayed at the top. In this way, it is possible to suggest the treatment that the patient is most likely to accept by taking the patient's emotional response into consideration.

[0081] The treatment method presentation unit can collect the opinions of other medical experts in real time and evaluate treatment methods from multiple perspectives. For example, the treatment method presentation unit uses a generative AI to collect the opinions of other medical experts in real time and reflect them in the evaluation of the treatment method. For example, it evaluates the effectiveness of a treatment method based on the opinions of specialists. The treatment method presentation unit also collects the opinions of other medical experts and evaluates treatment options from multiple perspectives. For example, it integrates the opinions of multiple experts and proposes the optimal treatment method. The treatment method presentation unit also builds a system that evaluates treatment methods based on the expert opinions collected in real time. For example, it presents treatment methods in a ranking format based on the expert evaluation scores. In this way, by collecting the opinions of other medical experts in real time, treatment methods can be evaluated from multiple perspectives.

[0082] The treatment suggestion unit can propose cost-effective treatments by taking into account the patient's financial situation. For example, the treatment suggestion unit uses a generative AI to analyze the patient's financial situation and propose cost-effective treatments. For example, it compares treatment costs and effectiveness and presents the optimal treatment. The treatment suggestion unit also considers the patient's insurance coverage and proposes treatments that are least financially burdensome. For example, it presents treatments that are covered by insurance as a priority. The treatment suggestion unit also builds a system that evaluates treatment options based on the patient's financial situation. For example, it comprehensively evaluates treatment costs and effectiveness and presents the optimal treatments in a ranking format. This makes it possible to propose cost-effective treatments by taking the patient's financial situation into consideration.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The medical record analysis unit analyzes the information recorded in the medical record. For example, the medical record analysis unit uses generative AI to analyze the text data recorded in the medical record using natural language processing technology. The medical record analysis unit can also analyze image data using image analysis technology. Furthermore, the medical record analysis unit can also analyze audio data using audio analysis technology. Step 2: The disease presentation unit presents possible diseases based on the information analyzed by the medical record analysis unit. For example, the disease presentation unit uses the generation AI to present possible diseases in a ranking format based on the information analyzed by the medical record analysis unit. The disease presentation unit can also use the generation AI to provide basic information about diseases based on the information analyzed by the medical record analysis unit. Step 3: The treatment presentation unit presents treatments for the diseases presented by the disease presentation unit. For example, the treatment presentation unit uses a generation AI to present treatments for the diseases presented by the disease presentation unit in a ranked format. The treatment presentation unit can also use a generation AI to provide written responses regarding the risks and quality of life (QOL) of the treatments for the diseases presented by the disease presentation unit. Step 4: The medical institution presentation unit presents appropriate medical institutions based on the treatment presented by the treatment presentation unit. For example, the medical institution presentation unit uses the generation AI to present information about nearby medical institutions and doctors who specialize in the disease based on the treatment presented by the treatment presentation unit. The medical institution presentation unit can also use the generation AI to suggest appropriate medical institutions and doctors based on information about the doctor's location based on the treatment presented by the treatment presentation unit.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[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 audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0110] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] 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.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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. Using generative AI, a medical record analysis unit that analyzes information recorded in the medical record; a disease presentation unit that presents a possibility of a disease based on the information analyzed by the medical record analysis unit; a treatment suggestion unit that suggests a treatment for the disease suggested by the disease suggestion unit; a medical institution presentation unit that presents appropriate medical institutions based on the treatment methods presented by the treatment method presentation unit. A system characterized by:

2. The medical record analysis unit Taking into account the patient's past medical history and family history, the possibility of the disease is presented with greater accuracy.

2. The system of claim 1.

3. The medical record analysis unit Enter information about the patient's lifestyle and work environment and assess the risk of the disease based on that information 2. The system of claim 1.

4. The medical record analysis unit Analyze the patient's emotional response to their complaint and present the possibility of the disease, taking into account the impact of stress and anxiety on the disease.

2. The system of claim 1.

5. The medical record analysis unit Using voice input, information is also collected about the patient's speaking style and tone of voice to assess the likelihood of the disease.

2. The system of claim 1.

Images