System

Abstract

An object of the system according to the embodiment is to support identification of a disease from a medical interview result or a medical examination result.SOLUTION: A system includes an inquiry result analysis part, a medical examination result analysis part, an intractable disease specification support part, and a nursing research support part. An inquiry result analysis part analyzes the inquiry result and lists possible diseases. The medical examination result analysis part analyzes the medical examination result and supports the specification of a disease. The incurable disease specification support unit supports specification of an incurable disease. The nursing research support unit supports nursing research and case research.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of identifying diseases from medical interview and examination results was complicated, making it particularly difficult to identify intractable diseases.

[0005] The system according to the embodiment aims to provide support for identifying illnesses based on the results of medical interviews and examinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical interview result analysis unit, a medical examination result analysis unit, an intractable disease identification support unit, and a nursing research support unit. The medical interview result analysis unit analyzes the medical interview results and lists possible diseases. The medical examination result analysis unit analyzes the medical examination results and supports the identification of diseases. The intractable disease identification support unit supports the identification of intractable diseases. The nursing research support unit supports nursing research and case studies. [Effects of the Invention]

[0007] The system according to the embodiment can assist in identifying illnesses based on the results of medical interviews and examinations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 disease identification support system according to the embodiment of the present invention is a system that uses a generation AI to support identifying a disease from the results of a medical interview or a medical examination. As a result, the disease identification support system can support identifying a disease from the results of a medical interview or a medical examination.

[0029] The disease identification support system according to the embodiment includes a medical interview result analysis unit, a medical examination result analysis unit, an intractable disease identification support unit, and a nursing research support unit. The medical interview result analysis unit analyzes the medical interview results and lists possible diseases. For example, the generation AI lists related diseases based on the medical interview results and presents them to the doctor. The generation AI can also analyze the medical interview results and identify the most likely disease. The medical examination result analysis unit analyzes medical examination results and supports disease identification. For example, the generation AI analyzes the results of blood tests and diagnostic imaging to provide information useful for disease identification. The generation AI can also identify diseases based on medical examination results. The intractable disease identification support unit supports the identification of intractable diseases. For example, the generation AI identifies diseases that are likely to be overlooked in standard diagnoses based on the patient's symptoms and test results. The generation AI can also identify diseases from a broader perspective when a disease is not found even after second opinions or multiple tests. The nursing research support unit supports nursing research and case studies. For example, the generative AI provides information necessary for nursing research and case studies based on knowledge of medical literature. The generative AI can also support research by providing relevant literature and data when conducting research on specific cases. As a result, the disease identification support system according to the embodiment can support identifying diseases from medical interview results and medical examination results. For example, the generative AI can analyze medical interview results and medical examination results and list possible diseases, allowing doctors to quickly perform appropriate tests and treatments. Furthermore, nurses can efficiently conduct nursing research and case studies with the support of the generative AI.

[0030] The medical interview result analysis unit can identify diseases based on the patient's lifestyle and genetic information. For example, when the generation AI analyzes the medical interview results, the medical interview result analysis unit collects lifestyle data such as the patient's diet, exercise habits, and sleep patterns, and takes this information into consideration to identify diseases. For example, if dietary content or exercise frequency is associated with a specific disease, the possibility of the disease is increased based on this information. The medical interview result analysis unit also analyzes the patient's genetic information and identifies diseases by taking into account family history and genetic risk factors. For example, if a specific gene mutation is present, it lists diseases associated with that mutation. The medical interview result analysis unit also performs an integrated analysis of lifestyle and genetic information to identify diseases where the risk of developing the disease increases when multiple factors overlap. For example, for patients with a high genetic risk and unhealthy lifestyles, the risk of a specific disease is increased. This enables more accurate disease identification by taking lifestyle and genetic information into account.

[0031] The medical interview result analysis unit can identify diseases based on the patient's past medical history and family history. For example, the medical interview result analysis unit uses the generative AI to analyze the patient's past medical history and identify diseases by taking into account previous diagnostic results and treatment history. For example, it reexamines diseases previously diagnosed with the same symptoms. The medical interview result analysis unit also references family history to identify diseases with a high genetic risk. For example, if a particular disease runs frequently in the family, the risk of that disease increases. The medical interview result analysis unit also performs an integrated analysis of past medical history and family history to identify diseases with a higher risk of developing due to the combination of multiple factors. For example, it lists diseases where the family history matches the past medical history. This improves the accuracy of disease identification by referring to past medical history and family history.

[0032] The medical interview result analysis unit can estimate the patient's health condition based on the patient's tone of voice and speaking style using voice input. For example, the medical interview result analysis unit uses a generative AI to analyze the voice input and detect signs of stress or fatigue from the patient's tone of voice and speaking style. For example, a trembling voice can be determined to indicate high levels of stress or anxiety. The medical interview result analysis unit also analyzes the patient's voice data and estimates signs of specific illnesses from the tone of voice and speaking style. For example, a hoarse voice can suggest a throat disease or respiratory problem. The medical interview result analysis unit also uses the voice input to analyze the patient's emotional state and psychological factors and identify illnesses based on the results. For example, a low tone of voice increases the risk of depression and anxiety disorders. This allows the health condition to be estimated from the tone of voice and speaking style using voice input.

[0033] The medical interview result analysis unit analyzes the handwritten characters on the medical interview form filled out by the patient and can estimate the patient's health condition based on the handwriting. For example, the medical interview result analysis unit uses a generation AI to analyze the handwritten characters and estimate the patient's health condition from changes or irregularities in the handwriting. For example, irregular handwriting may indicate hand tremors or a nervous system problem. The medical interview result analysis unit also analyzes the patient's handwriting and detects signs of stress or fatigue from the pressure and size of the characters. For example, weak pressure may indicate high levels of fatigue or stress. The medical interview result analysis unit also estimates signs of specific illnesses based on the results of the handwritten character analysis. For example, small handwriting may indicate a higher risk of Parkinson's disease. This makes it possible to estimate the patient's health condition from handwriting by analyzing the handwritten characters.

[0034] The medical examination result analysis unit can integrate multiple test results, find correlations, and identify diseases. For example, the generative AI integrates multiple test results, such as blood tests, urine tests, and diagnostic imaging, and analyzes correlations. For example, it can identify diseases by combining abnormal blood test values ​​with diagnostic imaging results. The medical examination result analysis unit also integrates test result data and analyzes patterns of abnormal values ​​to identify diseases. For example, if multiple abnormal values ​​are related to a specific disease, it will list that disease. The medical examination result analysis unit also finds correlations based on multiple test results, which helps identify diseases. For example, it can combine blood test and urine test results to identify kidney disease. In this way, by integrating multiple test results, it is possible to find correlations and identify diseases.

[0035] The screening result analysis unit takes subtle variations in test results into account, aiming for early detection. For example, the screening result analysis unit uses generative AI to analyze subtle variations in screening results and detect signs of disease before abnormal values ​​appear. For example, it analyzes subtle variations in blood sugar levels to aim for early detection of diabetes. The screening result analysis unit also takes subtle variations in test results into account to assess the risk of disease. For example, it analyzes subtle variations in cholesterol levels to identify increased risk of cardiovascular disease. The screening result analysis unit also builds a system that aims for early detection of disease based on subtle variations. For example, it analyzes subtle variations in liver function to identify liver disease at an early stage. This makes it possible to detect disease early by taking subtle variations into account.

[0036] The screening result analysis unit converts diagnostic imaging results into 3D models, allowing lesions to be visually identified. For example, the screening result analysis unit uses generative AI to convert diagnostic imaging results into 3D models, building a system that visually identifies lesions. For example, it converts CT scan and MRI results into 3D models to identify the lesion site. The screening result analysis unit also uses the 3D models to perform detailed analysis of the lesion site. For example, it visually confirms the size and location of a tumor using the 3D model. The screening result analysis unit also converts diagnostic imaging results into 3D models, allowing doctors to visually identify lesions. For example, it uses the 3D models to create surgical plans. This makes it possible to visually identify lesions using the 3D models.

[0037] The screening result analysis unit can analyze test data in real time and provide immediate feedback. For example, the screening result analysis unit builds a system in which generative AI analyzes test data in real time and provides immediate feedback. For example, it analyzes blood test results in real time and immediately notifies the doctor if abnormal values ​​are found. The screening result analysis unit also uses real-time analysis to immediately detect abnormalities in the test data and provide feedback to the doctor. For example, it analyzes electrocardiogram abnormalities in real time and immediately notifies the doctor if an abnormality is detected. The screening result analysis unit also aims for early detection by analyzing test data in real time and providing immediate feedback. For example, it analyzes urine test results in real time and immediately notifies the doctor if an abnormality is detected. This enables early detection by analyzing in real time and providing immediate feedback.

[0038] The Intractable Disease Identification Support Department can refer to a rare disease database and identify diseases that are likely to be overlooked in standard diagnoses. For example, the generation AI in the Intractable Disease Identification Support Department refers to a rare disease database and identifies rare diseases based on the patient's symptoms and test results. For example, if a specific symptom is associated with a rare disease, that disease will be listed. The Intractable Disease Identification Support Department also uses the rare disease database to identify diseases that are likely to be overlooked in standard diagnoses. For example, if a common symptom is associated with a rare disease, that disease will be listed. The Intractable Disease Identification Support Department also builds a system in which the generation AI analyzes the rare disease database and identifies rare diseases based on the patient's symptoms and test results. For example, if a specific gene mutation is associated with a rare disease, that disease will be listed. This makes it possible to identify diseases that are likely to be overlooked in standard diagnoses by referring to the rare disease database.

[0039] The Intractable Disease Identification Support Department can analyze a patient's genetic information and diagnose diseases based on genetic factors. For example, the Intractable Disease Identification Support Department uses generative AI to analyze a patient's genetic information and identify intractable diseases by taking genetic factors into consideration. For example, if a specific genetic mutation is associated with an intractable disease, the disease is listed. The Intractable Disease Identification Support Department also identifies intractable diseases with a high genetic risk based on genetic information. For example, if there is a match between family history and genetic mutation, the disease is listed. The Intractable Disease Identification Support Department also performs an integrated analysis of a patient's genetic information and symptoms, and identifies intractable diseases by taking genetic factors into consideration. For example, if there is a match between genetic mutation and symptoms, the disease is listed. This makes it possible to analyze genetic information and make a diagnosis that takes genetic factors into consideration.

[0040] The Intractable Disease Identification Support Department can identify diseases from a global perspective based on international medical databases. For example, the generation AI in the Intractable Disease Identification Support Department references international medical databases and identifies intractable diseases based on the patient's symptoms and test results. For example, if specific symptoms are related to intractable diseases that have been reported internationally, the disease will be listed. The Intractable Disease Identification Support Department also identifies intractable diseases from a global perspective based on international medical databases. For example, if general symptoms are related to intractable diseases that have been reported internationally, the disease will be listed. The Intractable Disease Identification Support Department also builds a system in which the generation AI analyzes international medical databases and identifies intractable diseases based on the patient's symptoms and test results. For example, if a specific genetic mutation is related to an intractable disease that has been reported internationally, the disease will be listed. This allows diseases to be identified from a global perspective by referencing international medical databases.

[0041] The Intractable Disease Identification Support Department can make a diagnosis that takes environmental factors into account based on the patient's living environment and work environment. For example, the Intractable Disease Identification Support Department uses a generative AI to analyze the patient's living environment and identify intractable diseases by taking environmental factors into account. For example, if a specific environmental factor is related to an intractable disease, the disease is listed. The Intractable Disease Identification Support Department also identifies intractable diseases that involve environmental factors based on the patient's work environment. For example, if the work environment is related to a specific disease, the disease is listed. The Intractable Disease Identification Support Department also performs an integrated analysis of the living environment and work environment, and identifies intractable diseases by taking environmental factors into account. For example, if environmental factors match symptoms, the disease is listed. This makes it possible to make a diagnosis that takes environmental factors into account by taking the living environment and work environment into account.

[0042] The Nursing Research Support Department can analyze past case data and extract patterns and trends that are useful for research. For example, the Nursing Research Support Department will build a system in which generative AI analyzes past case data and extracts patterns and trends that are useful for research. For example, it will extract patterns where specific cases are frequently reported. The Nursing Research Support Department will also analyze trends that are useful for research based on case data. For example, it will extract trends where specific cases are increasing. The Nursing Research Support Department will also support nursing research by analyzing past case data and extracting patterns and trends that are useful for research. For example, it will advance research based on patterns where specific cases are frequently reported. In this way, by analyzing past case data, it will be possible to extract patterns and trends that are useful for research.

[0043] The Nursing Research Support Department can integrate data from different medical institutions, enabling research from a broad perspective. For example, the Nursing Research Support Department builds a system in which generative AI integrates data from different medical institutions, enabling research from a broad perspective. For example, it integrates case data from multiple medical institutions. The Nursing Research Support Department also supports research from a broad perspective based on data from medical institutions. For example, it integrates data from different medical institutions and extracts patterns and trends that are useful for research. The Nursing Research Support Department also supports nursing research by integrating data from different medical institutions, enabling research from a broad perspective. For example, it conducts research based on data from multiple medical institutions. In this way, integrating data from different medical institutions makes research from a broad perspective possible.

[0044] The Nursing Research Support Department can visualize research results and provide them in a visually easy-to-understand format. For example, the Nursing Research Support Department builds a system in which generative AI visualizes research results and provides them in a visually easy-to-understand format. For example, the research results are displayed using graphs and charts. The Nursing Research Support Department also provides them in a visually easy-to-understand format based on the research results. For example, the research results are visualized so that nurses can intuitively understand them. The Nursing Research Support Department also supports nursing research by visualizing research results and providing them in a visually easy-to-understand format. For example, the research results are displayed using graphs and charts. In this way, by visualizing the research results, they can be provided in a visually easy-to-understand format.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The disease identification support system can also be equipped with a social background analysis unit that takes into account the patient's social background. For example, the generative AI can collect information such as the patient's occupation, home environment, and whether or not they have social support, and analyze the impact these factors have on the onset and progression of the disease. For example, if a stressful work environment or problems at home increase the risk of illness, the system can identify the illness based on that information. Furthermore, providing appropriate support to patients who lack social support can improve disease prevention and treatment effectiveness. Taking social background into account in this way enables more comprehensive disease identification and treatment.

[0047] The medical interview result analysis unit can further include a nutritional analysis unit that performs a detailed analysis of the patient's dietary content. For example, the generative AI analyzes the patient's dietary records to evaluate nutritional balance and calorie intake. For example, if there is a deficiency of a specific nutrient, the risk of disease can be assessed based on that information. The nutritional analysis unit also analyzes the relationship between dietary content and disease, and if a specific dietary pattern increases the risk of disease, the disease can be identified based on that information. For example, if a high-fat diet increases the risk of cardiovascular disease, the disease can be identified based on that information. This allows for a more accurate assessment of disease risk through a detailed analysis of dietary content.

[0048] The medical interview result analysis unit can further include an exercise analysis unit that performs a detailed analysis of the patient's exercise habits. For example, the generative AI analyzes the patient's exercise records and evaluates the frequency and intensity of exercise. For example, if lack of exercise increases the risk of a certain disease, the disease can be identified based on that information. The exercise analysis unit also analyzes the relationship between exercise habits and disease, and if a specific exercise pattern reduces the risk of disease, it can use that information to suggest disease prevention measures. For example, if regular aerobic exercise reduces the risk of cardiovascular disease, it can use that information to suggest disease prevention measures. This detailed analysis of exercise habits can more accurately assess disease risk and suggest preventive measures.

[0049] The medical interview result analysis unit can further include a sleep analysis unit that analyzes the patient's sleep patterns. For example, the generation AI analyzes the patient's sleep records and evaluates the quality and quantity of sleep. For example, if lack of sleep or irregular sleep patterns increases the risk of a certain disease, the disease can be identified based on that information. The sleep analysis unit also analyzes the relationship between sleep patterns and disease, and if a specific sleep disorder increases the risk of disease, the disease can be identified based on that information. For example, if sleep apnea syndrome increases the risk of cardiovascular disease, the disease can be identified based on that information. This allows for a more accurate assessment of disease risk through detailed analysis of sleep patterns.

[0050] The medical interview result analysis unit can further include a handwriting analysis unit that analyzes the patient's handwriting. For example, the generation AI analyzes handwriting and estimates the patient's health condition from changes or irregularities in handwriting. For example, irregular handwriting may indicate hand tremors or a nervous system problem. The medical interview result analysis unit also analyzes the patient's handwriting and detects signs of stress or fatigue from the pressure and size of the writing. For example, weak writing pressure may indicate high levels of fatigue or stress. The medical interview result analysis unit also estimates signs of specific illnesses based on the results of the handwriting analysis. For example, small handwriting indicates a higher risk of Parkinson's disease. This makes it possible to estimate the patient's health condition from handwriting analysis.

[0051] The medical examination result analysis unit can further include a preventive measures suggestion unit that suggests preventive measures based on the patient's test results. For example, the generative AI analyzes medical examination results and suggests preventive measures if the patient is at high risk of disease. For example, if blood sugar levels are high, it suggests improvements to diet and exercise. The preventive measures suggestion unit also evaluates the risk of disease based on the test result data and suggests preventive measures. For example, if cholesterol levels are high, it recommends improving diet and exercise. The preventive measures suggestion unit also evaluates the risk of disease based on the medical examination results and suggests preventive measures, thereby supporting disease prevention. For example, if abnormalities in liver function are found, it suggests limiting alcohol intake. This makes it possible to prevent disease by suggesting preventive measures based on medical examination results.

[0052] The medical examination result analysis unit can further include a treatment effect evaluation unit that evaluates the effectiveness of treatment based on the patient's test results. For example, the generative AI analyzes medical examination results and evaluates the effectiveness of treatment. For example, it analyzes fluctuations in blood glucose levels and evaluates the effectiveness of diabetes treatment. The treatment effect evaluation unit also evaluates the effectiveness of treatment based on test result data and proposes a review of the treatment plan. For example, it analyzes fluctuations in cholesterol levels and evaluates the effectiveness of cardiovascular disease treatment. The treatment effect evaluation unit also evaluates the effectiveness of treatment based on medical examination results and proposes a review of the treatment plan, thereby supporting the optimization of treatment. For example, if no improvement in liver function is observed, it proposes a review of the treatment plan. This makes it possible to optimize treatment by evaluating the effectiveness of treatment based on medical examination results.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The medical interview result analysis unit analyzes the medical interview results and lists possible diseases. For example, the generation AI can list related diseases based on the medical interview results and present them to the doctor. The generation AI can also analyze the medical interview results and identify the most likely disease. Step 2: The screening result analysis unit analyzes the screening results and helps identify diseases. For example, the generating AI analyzes the results of blood tests and diagnostic imaging to provide information that is useful for identifying diseases. The generating AI can also identify diseases based on the screening results. Step 3: The Intractable Disease Identification Support Department will assist in identifying intractable diseases. For example, the Generative AI will identify diseases that are likely to be overlooked in standard diagnoses based on the patient's symptoms and test results. Generative AI can also identify diseases from a broader perspective when a disease cannot be found even after second opinions and multiple tests. Step 4: The Nursing Research Support Department supports nursing research and case studies. For example, the generative AI provides the information necessary for nursing research and case studies based on knowledge of medical literature. The generative AI can also support research by providing relevant literature and data when conducting research on specific cases.

[0055] (Example 2) The disease identification support system according to the embodiment of the present invention is a system that uses a generation AI to support identifying a disease from the results of a medical interview or a medical examination. As a result, the disease identification support system can support identifying a disease from the results of a medical interview or a medical examination.

[0056] The disease identification support system according to the embodiment includes a medical interview result analysis unit, a medical examination result analysis unit, an intractable disease identification support unit, and a nursing research support unit. The medical interview result analysis unit analyzes the medical interview results and lists possible diseases. For example, the generation AI lists related diseases based on the medical interview results and presents them to the doctor. The generation AI can also analyze the medical interview results and identify the most likely disease. The medical examination result analysis unit analyzes medical examination results and supports disease identification. For example, the generation AI analyzes the results of blood tests and diagnostic imaging to provide information useful for disease identification. The generation AI can also identify diseases based on medical examination results. The intractable disease identification support unit supports the identification of intractable diseases. For example, the generation AI identifies diseases that are likely to be overlooked in standard diagnoses based on the patient's symptoms and test results. The generation AI can also identify diseases from a broader perspective when a disease is not found even after second opinions or multiple tests. The nursing research support unit supports nursing research and case studies. For example, the generative AI provides information necessary for nursing research and case studies based on knowledge of medical literature. The generative AI can also support research by providing relevant literature and data when conducting research on specific cases. As a result, the disease identification support system according to the embodiment can support identifying diseases from medical interview results and medical examination results. For example, the generative AI can analyze medical interview results and medical examination results and list possible diseases, allowing doctors to quickly perform appropriate tests and treatments. Furthermore, nurses can efficiently conduct nursing research and case studies with the support of the generative AI.

[0057] The medical interview result analysis unit can identify diseases based on the patient's lifestyle and genetic information. For example, when the generation AI analyzes the medical interview results, the medical interview result analysis unit collects lifestyle data such as the patient's diet, exercise habits, and sleep patterns, and takes this information into consideration to identify diseases. For example, if dietary content or exercise frequency is associated with a specific disease, the possibility of the disease is increased based on this information. The medical interview result analysis unit also analyzes the patient's genetic information and identifies diseases by taking into account family history and genetic risk factors. For example, if a specific gene mutation is present, it lists diseases associated with that mutation. The medical interview result analysis unit also performs an integrated analysis of lifestyle and genetic information to identify diseases where the risk of developing the disease increases when multiple factors overlap. For example, for patients with a high genetic risk and unhealthy lifestyles, the risk of a specific disease is increased. This enables more accurate disease identification by taking lifestyle and genetic information into account.

[0058] The medical interview result analysis unit can identify diseases based on the patient's past medical history and family history. For example, the medical interview result analysis unit uses the generative AI to analyze the patient's past medical history and identify diseases by taking into account previous diagnostic results and treatment history. For example, it reexamines diseases previously diagnosed with the same symptoms. The medical interview result analysis unit also references family history to identify diseases with a high genetic risk. For example, if a particular disease runs frequently in the family, the risk of that disease increases. The medical interview result analysis unit also performs an integrated analysis of past medical history and family history to identify diseases with a higher risk of developing due to the combination of multiple factors. For example, it lists diseases where the family history matches the past medical history. This improves the accuracy of disease identification by referring to past medical history and family history.

[0059] The medical interview result analysis unit uses the emotion estimation function to analyze the patient's emotional state and consider the impact of stress and psychological factors on illness. For example, when the generation AI analyzes the medical interview results, the medical interview result analysis unit analyzes the patient's emotional state in real time and considers the impact of stress and anxiety on illness. For example, it lists stress-related illnesses for patients with high stress. The medical interview result analysis unit also uses the emotion estimation function to analyze the patient's psychological factors and, if psychological stress increases the risk of developing an illness, identifies the illness based on that information. For example, it lists related illnesses for patients at high risk of depression or anxiety disorders. The medical interview result analysis unit also comprehensively analyzes the patient's emotional state and the medical interview results and identifies the illness by considering the impact of psychological factors on the illness. For example, for a patient whose emotional state is deteriorating, it lists illnesses related to psychological factors. This makes it possible to evaluate the impact of stress and psychological factors on illness by considering the emotional state.

[0060] The medical interview result analysis unit can estimate the patient's health condition based on the patient's tone of voice and speaking style using voice input. For example, the medical interview result analysis unit uses a generative AI to analyze the voice input and detect signs of stress or fatigue from the patient's tone of voice and speaking style. For example, a trembling voice can be determined to indicate high levels of stress or anxiety. The medical interview result analysis unit also analyzes the patient's voice data and estimates signs of specific illnesses from the tone of voice and speaking style. For example, a hoarse voice can suggest a throat disease or respiratory problem. The medical interview result analysis unit also uses the voice input to analyze the patient's emotional state and psychological factors and identify illnesses based on the results. For example, a low tone of voice increases the risk of depression and anxiety disorders. This allows the health condition to be estimated from the tone of voice and speaking style using voice input.

[0061] The medical interview result analysis unit analyzes the handwritten characters on the medical interview form filled out by the patient and can estimate the patient's health condition based on the handwriting. For example, the medical interview result analysis unit uses a generation AI to analyze the handwritten characters and estimate the patient's health condition from changes or irregularities in the handwriting. For example, irregular handwriting may indicate hand tremors or a nervous system problem. The medical interview result analysis unit also analyzes the patient's handwriting and detects signs of stress or fatigue from the pressure and size of the characters. For example, weak pressure may indicate high levels of fatigue or stress. The medical interview result analysis unit also estimates signs of specific illnesses based on the results of the handwritten character analysis. For example, small handwriting may indicate a higher risk of Parkinson's disease. This makes it possible to estimate the patient's health condition from handwriting by analyzing the handwritten characters.

[0062] The medical interview result analysis unit uses the emotion estimation function to analyze the patient's facial expressions and attitude during the interview, and can identify illnesses taking into account their emotional state. For example, the medical interview result analysis unit uses a generative AI to analyze the patient's facial expressions during the interview in real time and estimate their emotional state. For example, it detects signs of stress or anxiety from facial expressions. The medical interview result analysis unit also analyzes the patient's attitude and behavior and identifies illnesses taking into account their emotional state. For example, if the patient appears restless, it may determine that they have high levels of stress or anxiety. The medical interview result analysis unit also identifies illnesses based on the analysis results of facial expressions and attitudes, taking into account the impact that emotional state has on illnesses. For example, a gloomy expression increases the risk of depression or anxiety disorders. This makes it possible to identify illnesses taking into account emotional states by analyzing facial expressions and attitudes.

[0063] The medical examination result analysis unit can integrate multiple test results, find correlations, and identify diseases. For example, the generative AI integrates multiple test results, such as blood tests, urine tests, and diagnostic imaging, and analyzes correlations. For example, it can identify diseases by combining abnormal blood test values ​​with diagnostic imaging results. The medical examination result analysis unit also integrates test result data and analyzes patterns of abnormal values ​​to identify diseases. For example, if multiple abnormal values ​​are related to a specific disease, it will list that disease. The medical examination result analysis unit also finds correlations based on multiple test results, which helps identify diseases. For example, it can combine blood test and urine test results to identify kidney disease. In this way, by integrating multiple test results, it is possible to find correlations and identify diseases.

[0064] The screening result analysis unit takes subtle variations in test results into account, aiming for early detection. For example, the screening result analysis unit uses generative AI to analyze subtle variations in screening results and detect signs of disease before abnormal values ​​appear. For example, it analyzes subtle variations in blood sugar levels to aim for early detection of diabetes. The screening result analysis unit also takes subtle variations in test results into account to assess the risk of disease. For example, it analyzes subtle variations in cholesterol levels to identify increased risk of cardiovascular disease. The screening result analysis unit also builds a system that aims for early detection of disease based on subtle variations. For example, it analyzes subtle variations in liver function to identify liver disease at an early stage. This makes it possible to detect disease early by taking subtle variations into account.

[0065] The screening result analysis unit converts diagnostic imaging results into 3D models, allowing lesions to be visually identified. For example, the screening result analysis unit uses generative AI to convert diagnostic imaging results into 3D models, building a system that visually identifies lesions. For example, it converts CT scan and MRI results into 3D models to identify the lesion site. The screening result analysis unit also uses the 3D models to perform detailed analysis of the lesion site. For example, it visually confirms the size and location of a tumor using the 3D model. The screening result analysis unit also converts diagnostic imaging results into 3D models, allowing doctors to visually identify lesions. For example, it uses the 3D models to create surgical plans. This makes it possible to visually identify lesions using the 3D models.

[0066] The screening result analysis unit can analyze test data in real time and provide immediate feedback. For example, the screening result analysis unit builds a system in which generative AI analyzes test data in real time and provides immediate feedback. For example, it analyzes blood test results in real time and immediately notifies the doctor if abnormal values ​​are found. The screening result analysis unit also uses real-time analysis to immediately detect abnormalities in the test data and provide feedback to the doctor. For example, it analyzes electrocardiogram abnormalities in real time and immediately notifies the doctor if an abnormality is detected. The screening result analysis unit also aims for early detection by analyzing test data in real time and providing immediate feedback. For example, it analyzes urine test results in real time and immediately notifies the doctor if an abnormality is detected. This enables early detection by analyzing in real time and providing immediate feedback.

[0067] The medical examination result analysis unit uses the emotion estimation function to analyze the patient's facial expressions and attitude during the examination, and can identify illnesses taking into account their emotional state. For example, the medical examination result analysis unit uses a generative AI to analyze the patient's facial expressions in real time during the examination and estimate their emotional state. For example, it detects signs of stress or anxiety from facial expressions. The medical examination result analysis unit also analyzes the patient's attitude and behavior and identifies illnesses taking into account their emotional state. For example, if the patient appears restless, it may determine that they have high levels of stress or anxiety. The medical examination result analysis unit also identifies illnesses based on the analysis results of facial expressions and attitudes, taking into account the impact that emotional state has on illnesses. For example, a gloomy expression increases the risk of depression or anxiety disorders. This makes it possible to identify illnesses taking into account emotional states by analyzing facial expressions and attitudes.

[0068] The Intractable Disease Identification Support Department can refer to a rare disease database and identify diseases that are likely to be overlooked in standard diagnoses. For example, the generation AI in the Intractable Disease Identification Support Department refers to a rare disease database and identifies rare diseases based on the patient's symptoms and test results. For example, if a specific symptom is associated with a rare disease, that disease will be listed. The Intractable Disease Identification Support Department also uses the rare disease database to identify diseases that are likely to be overlooked in standard diagnoses. For example, if a common symptom is associated with a rare disease, that disease will be listed. The Intractable Disease Identification Support Department also builds a system in which the generation AI analyzes the rare disease database and identifies rare diseases based on the patient's symptoms and test results. For example, if a specific gene mutation is associated with a rare disease, that disease will be listed. This makes it possible to identify diseases that are likely to be overlooked in standard diagnoses by referring to the rare disease database.

[0069] The Intractable Disease Identification Support Department can analyze a patient's genetic information and diagnose diseases based on genetic factors. For example, the Intractable Disease Identification Support Department uses generative AI to analyze a patient's genetic information and identify intractable diseases by taking genetic factors into consideration. For example, if a specific genetic mutation is associated with an intractable disease, the disease is listed. The Intractable Disease Identification Support Department also identifies intractable diseases with a high genetic risk based on genetic information. For example, if there is a match between family history and genetic mutation, the disease is listed. The Intractable Disease Identification Support Department also performs an integrated analysis of a patient's genetic information and symptoms, and identifies intractable diseases by taking genetic factors into consideration. For example, if there is a match between genetic mutation and symptoms, the disease is listed. This makes it possible to analyze genetic information and make a diagnosis that takes genetic factors into consideration.

[0070] The Intractable Disease Identification Support Department can use the emotion estimation function to analyze the patient's emotional state during the diagnostic process and provide psychological support. For example, the Intractable Disease Identification Support Department uses a generative AI to analyze the patient's emotional state in real time during the diagnostic process of an intractable disease and provide psychological support. For example, if there is high anxiety or stress regarding the diagnostic results, the impact of this is taken into consideration. The Intractable Disease Identification Support Department also uses the emotion estimation function to build a system that analyzes the patient's emotional state during the diagnostic process of an intractable disease and provides psychological support. For example, if there is high fear or anxiety regarding the diagnostic results, the impact of this is taken into consideration. The Intractable Disease Identification Support Department also provides psychological support by integrating an analysis of the patient's emotional state and the diagnostic results. For example, if there is a strong emotional reaction to the diagnostic results, support is provided taking into consideration the impact. In this way, psychological support can be provided by analyzing the emotional state.

[0071] The Intractable Disease Identification Support Department can identify diseases from a global perspective based on international medical databases. For example, the generation AI in the Intractable Disease Identification Support Department references international medical databases and identifies intractable diseases based on the patient's symptoms and test results. For example, if specific symptoms are related to intractable diseases that have been reported internationally, the disease will be listed. The Intractable Disease Identification Support Department also identifies intractable diseases from a global perspective based on international medical databases. For example, if general symptoms are related to intractable diseases that have been reported internationally, the disease will be listed. The Intractable Disease Identification Support Department also builds a system in which the generation AI analyzes international medical databases and identifies intractable diseases based on the patient's symptoms and test results. For example, if a specific genetic mutation is related to an intractable disease that has been reported internationally, the disease will be listed. This allows diseases to be identified from a global perspective by referencing international medical databases.

[0072] The Intractable Disease Identification Support Department can make a diagnosis that takes environmental factors into account based on the patient's living environment and work environment. For example, the Intractable Disease Identification Support Department uses a generative AI to analyze the patient's living environment and identify intractable diseases by taking environmental factors into account. For example, if a specific environmental factor is related to an intractable disease, the disease is listed. The Intractable Disease Identification Support Department also identifies intractable diseases that involve environmental factors based on the patient's work environment. For example, if the work environment is related to a specific disease, the disease is listed. The Intractable Disease Identification Support Department also performs an integrated analysis of the living environment and work environment, and identifies intractable diseases by taking environmental factors into account. For example, if environmental factors match symptoms, the disease is listed. This makes it possible to make a diagnosis that takes environmental factors into account by taking the living environment and work environment into account.

[0073] The Intractable Disease Identification Support Department can use the emotion estimation function to analyze the emotional state of a patient's family during the diagnostic process and provide support to the family. For example, the Intractable Disease Identification Support Department uses a generative AI to analyze the emotional state of a patient's family in real time during the diagnostic process of an intractable disease and provide support to the family. For example, if the family's anxiety or stress regarding the diagnostic results is high, the impact is taken into consideration. The Intractable Disease Identification Support Department also uses the emotion estimation function to build a system that analyzes the emotional state of a patient's family during the diagnostic process of an intractable disease and provides support to the family. For example, if the family's fear or anxiety regarding the diagnostic results is high, the impact is taken into consideration. The Intractable Disease Identification Support Department also comprehensively analyzes the emotional state of a patient's family and the diagnostic results and provides support to the family. For example, if the family has a strong emotional reaction to the diagnostic results, the impact is taken into consideration when providing support. This makes it possible to provide support to the family by analyzing the emotional state.

[0074] The Nursing Research Support Department can analyze past case data and extract patterns and trends that are useful for research. For example, the Nursing Research Support Department will build a system in which generative AI analyzes past case data and extracts patterns and trends that are useful for research. For example, it will extract patterns where specific cases are frequently reported. The Nursing Research Support Department will also analyze trends that are useful for research based on case data. For example, it will extract trends where specific cases are increasing. The Nursing Research Support Department will also support nursing research by analyzing past case data and extracting patterns and trends that are useful for research. For example, it will advance research based on patterns where specific cases are frequently reported. In this way, by analyzing past case data, it will be possible to extract patterns and trends that are useful for research.

[0075] The nursing research support department can use the emotion estimation function to analyze the emotional state of nurses and provide support according to the progress of the research. For example, the nursing research support department uses a generative AI to analyze the emotional state of nurses in real time and provide support according to the progress of the research. For example, support is provided to reduce stress and anxiety if the research is not progressing. The nursing research support department also uses the emotion estimation function to build a system that analyzes the emotional state of nurses and provides support according to the progress of the research. For example, support is provided to reduce stress and anxiety if the research is not progressing. The nursing research support department also provides support by integrating an analysis of the emotional state of nurses and the progress of the research. For example, support is provided to reduce stress and anxiety if the research is not progressing. In this way, by analyzing the emotional state of nurses, it is possible to provide support according to the progress of the research.

[0076] The Nursing Research Support Department can integrate data from different medical institutions, enabling research from a broad perspective. For example, the Nursing Research Support Department builds a system in which generative AI integrates data from different medical institutions, enabling research from a broad perspective. For example, it integrates case data from multiple medical institutions. The Nursing Research Support Department also supports research from a broad perspective based on data from medical institutions. For example, it integrates data from different medical institutions and extracts patterns and trends that are useful for research. The Nursing Research Support Department also supports nursing research by integrating data from different medical institutions, enabling research from a broad perspective. For example, it conducts research based on data from multiple medical institutions. In this way, integrating data from different medical institutions makes research from a broad perspective possible.

[0077] The Nursing Research Support Department can visualize research results and provide them in a visually easy-to-understand format. For example, the Nursing Research Support Department builds a system in which generative AI visualizes research results and provides them in a visually easy-to-understand format. For example, the research results are displayed using graphs and charts. The Nursing Research Support Department also provides them in a visually easy-to-understand format based on the research results. For example, the research results are visualized so that nurses can intuitively understand them. The Nursing Research Support Department also supports nursing research by visualizing research results and providing them in a visually easy-to-understand format. For example, the research results are displayed using graphs and charts. In this way, by visualizing the research results, they can be provided in a visually easy-to-understand format.

[0078] The Nursing Research Support Department can use the emotion estimation function to analyze the emotional state of patients participating in nursing research and consider the ethical aspects of the research. For example, the Nursing Research Support Department uses generative AI to analyze the emotional state of patients participating in nursing research in real time and consider the ethical aspects of the research. For example, it can reduce the stress and anxiety that the research causes to patients. The Nursing Research Support Department also uses the emotion estimation function to analyze the emotional state of patients participating in nursing research and monitor their emotional changes as the research progresses. For example, it can respond immediately if a patient feels anxious. The Nursing Research Support Department also performs an integrated analysis of the patient's emotional state and the progress of the research and consider the ethical aspects of the research. For example, if a patient has a strong emotional reaction, it can adjust the research method taking that impact into account. In this way, the ethical aspects of the research can be considered by analyzing the patient's emotional state.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The disease identification support system can also be equipped with a social background analysis unit that takes into account the patient's social background. For example, the generative AI can collect information such as the patient's occupation, home environment, and whether or not they have social support, and analyze the impact these factors have on the onset and progression of the disease. For example, if a stressful work environment or problems at home increase the risk of illness, the system can identify the illness based on that information. Furthermore, providing appropriate support to patients who lack social support can improve disease prevention and treatment effectiveness. Taking social background into account in this way enables more comprehensive disease identification and treatment.

[0081] The medical interview result analysis unit can further include a nutritional analysis unit that performs a detailed analysis of the patient's dietary content. For example, the generative AI analyzes the patient's dietary records to evaluate nutritional balance and calorie intake. For example, if there is a deficiency of a specific nutrient, the risk of disease can be assessed based on that information. The nutritional analysis unit also analyzes the relationship between dietary content and disease, and if a specific dietary pattern increases the risk of disease, the disease can be identified based on that information. For example, if a high-fat diet increases the risk of cardiovascular disease, the disease can be identified based on that information. This allows for a more accurate assessment of disease risk through a detailed analysis of dietary content.

[0082] The medical interview result analysis unit can further include an exercise analysis unit that performs a detailed analysis of the patient's exercise habits. For example, the generative AI analyzes the patient's exercise records and evaluates the frequency and intensity of exercise. For example, if lack of exercise increases the risk of a certain disease, the disease can be identified based on that information. The exercise analysis unit also analyzes the relationship between exercise habits and disease, and if a specific exercise pattern reduces the risk of disease, it can use that information to suggest disease prevention measures. For example, if regular aerobic exercise reduces the risk of cardiovascular disease, it can use that information to suggest disease prevention measures. This detailed analysis of exercise habits can more accurately assess disease risk and suggest preventive measures.

[0083] The medical interview result analysis unit can further include a sleep analysis unit that analyzes the patient's sleep patterns. For example, the generation AI analyzes the patient's sleep records and evaluates the quality and quantity of sleep. For example, if lack of sleep or irregular sleep patterns increases the risk of a certain disease, the disease can be identified based on that information. The sleep analysis unit also analyzes the relationship between sleep patterns and disease, and if a specific sleep disorder increases the risk of disease, the disease can be identified based on that information. For example, if sleep apnea syndrome increases the risk of cardiovascular disease, the disease can be identified based on that information. This allows for a more accurate assessment of disease risk through detailed analysis of sleep patterns.

[0084] The medical interview result analysis unit can further use an emotion estimation function to analyze the patient's emotional state, taking into account the impact of stress and psychological factors on illness. For example, when the generative AI analyzes the medical interview results, it analyzes the patient's emotional state in real time and considers the impact of stress and anxiety on illness. For example, it can list stress-related illnesses for patients with high stress levels. The medical interview result analysis unit also uses the emotion estimation function to analyze the patient's psychological factors, and if psychological stress increases the risk of developing an illness, it can identify the illness based on that information. For example, it can list related illnesses for patients at high risk of depression or anxiety disorders. The medical interview result analysis unit also performs an integrated analysis of the patient's emotional state and the medical interview results, taking into account the impact of psychological factors on illness to identify the illness. For example, for a patient whose emotional state is deteriorating, it can list illnesses related to psychological factors. This makes it possible to evaluate the impact of stress and psychological factors on illness by taking into account the patient's emotional state.

[0085] The medical interview result analysis unit can further analyze the patient's emotional state using voice input and consider the impact of stress and psychological factors on illness. For example, the generative AI analyzes the voice input and detects signs of stress or anxiety from the patient's tone of voice and speaking style. For example, a trembling voice can be determined to indicate high levels of stress or anxiety. The medical interview result analysis unit also analyzes the patient's voice data and infers signs of specific illnesses from the tone of voice and speaking style. For example, a hoarse voice can suggest a throat disease or respiratory problem. The medical interview result analysis unit also uses the voice input to analyze the patient's emotional state and psychological factors and identify illnesses based on that analysis. For example, a low voice tone increases the risk of depression and anxiety disorders. This makes it possible to estimate health status from the tone of voice and speaking style using voice input.

[0086] The medical interview result analysis unit can further include a handwriting analysis unit that analyzes the patient's handwriting. For example, the generation AI analyzes handwriting and estimates the patient's health condition from changes or irregularities in handwriting. For example, irregular handwriting may indicate hand tremors or a nervous system problem. The medical interview result analysis unit also analyzes the patient's handwriting and detects signs of stress or fatigue from the pressure and size of the writing. For example, weak writing pressure may indicate high levels of fatigue or stress. The medical interview result analysis unit also estimates signs of specific illnesses based on the results of the handwriting analysis. For example, small handwriting indicates a higher risk of Parkinson's disease. This makes it possible to estimate the patient's health condition from handwriting analysis.

[0087] The medical examination result analysis unit can further analyze the patient's emotional state based on the test results and consider the impact of stress and psychological factors on illness. For example, when the generative AI analyzes medical examination results, it analyzes the patient's emotional state in real time and considers the impact of stress and anxiety on illness. For example, it can list stress-related illnesses for patients with high stress levels. The medical examination result analysis unit also uses the emotion estimation function to analyze the patient's psychological factors and, if psychological stress increases the risk of developing an illness, identify the illness based on that information. For example, it can list related illnesses for patients at high risk of depression or anxiety disorders. The medical examination result analysis unit also performs an integrated analysis of the patient's emotional state and medical examination results, identifying illnesses by taking into account the impact of psychological factors on illness. For example, for a patient whose emotional state is deteriorating, it can list illnesses related to psychological factors. This makes it possible to evaluate the impact of stress and psychological factors on illness by taking emotional state into account.

[0088] The medical examination result analysis unit can further include a preventive measures suggestion unit that suggests preventive measures based on the patient's test results. For example, the generative AI analyzes medical examination results and suggests preventive measures if the patient is at high risk of disease. For example, if blood sugar levels are high, it suggests improvements to diet and exercise. The preventive measures suggestion unit also evaluates the risk of disease based on the test result data and suggests preventive measures. For example, if cholesterol levels are high, it recommends improving diet and exercise. The preventive measures suggestion unit also evaluates the risk of disease based on the medical examination results and suggests preventive measures, thereby supporting disease prevention. For example, if abnormalities in liver function are found, it suggests limiting alcohol intake. This makes it possible to prevent disease by suggesting preventive measures based on medical examination results.

[0089] The medical examination result analysis unit can further include a treatment effect evaluation unit that evaluates the effectiveness of treatment based on the patient's test results. For example, the generative AI analyzes medical examination results and evaluates the effectiveness of treatment. For example, it analyzes fluctuations in blood glucose levels and evaluates the effectiveness of diabetes treatment. The treatment effect evaluation unit also evaluates the effectiveness of treatment based on test result data and proposes a review of the treatment plan. For example, it analyzes fluctuations in cholesterol levels and evaluates the effectiveness of cardiovascular disease treatment. The treatment effect evaluation unit also evaluates the effectiveness of treatment based on medical examination results and proposes a review of the treatment plan, thereby supporting the optimization of treatment. For example, if no improvement in liver function is observed, it proposes a review of the treatment plan. This makes it possible to optimize treatment by evaluating the effectiveness of treatment based on medical examination results.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The medical interview result analysis unit analyzes the medical interview results and lists possible diseases. For example, the generation AI can list related diseases based on the medical interview results and present them to the doctor. The generation AI can also analyze the medical interview results and identify the most likely disease. Step 2: The screening result analysis unit analyzes the screening results and helps identify diseases. For example, the generating AI analyzes the results of blood tests and diagnostic imaging to provide information that is useful for identifying diseases. The generating AI can also identify diseases based on the screening results. Step 3: The Intractable Disease Identification Support Department will assist in identifying intractable diseases. For example, the Generative AI will identify diseases that are likely to be overlooked in standard diagnoses based on the patient's symptoms and test results. Generative AI can also identify diseases from a broader perspective when a disease cannot be found even after second opinions and multiple tests. Step 4: The Nursing Research Support Department supports nursing research and case studies. For example, the generative AI provides the information necessary for nursing research and case studies based on knowledge of medical literature. The generative AI can also support research by providing relevant literature and data when conducting research on specific cases.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that uses a generation AI to assist in identifying diseases from interview results or medical examination results, A medical interview result analysis unit analyzes the medical interview results and lists possible diseases; A screening result analysis unit that analyzes screening results and helps identify diseases; The Intractable Disease Identification Support Department, which supports the identification of intractable diseases, Nursing Research Support Department that supports nursing research and case studies. A system characterized by:

2. The medical interview result analysis unit Analyzing the patient's emotional state and considering the impact of stress and psychological factors on the disease 2. The system of claim 1.

3. The examination result analysis unit Integrating the results of multiple such tests and finding correlations to identify the disease 2. The system of claim 1.

4. The Intractable Disease Specific Support Department Referencing rare disease databases to identify diseases that are often overlooked by routine diagnostics 2. The system of claim 1.

5. The nursing research support department Analyze historical case data to extract patterns and trends that can aid in research 2. The system of claim 1.

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