System

Abstract

An object of a system according to an embodiment is to provide the latest information based on a symptom and a medical record of a patient and to support diagnosis.SOLUTION: A system includes a patient information input unit, an information analysis unit, and a diagnosis support unit. The patient information input unit inputs symptoms and medical records of a patient. The information analysis unit analyzes the information input by the patient information input unit. The diagnosis support unit provides the latest information based on the information analyzed by the information analysis unit and performs diagnosis support.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, even if doctors and nurses input patients' symptoms and medical records, it was difficult to quickly provide the latest information and support diagnosis.

[0005] The system according to the embodiment aims to provide the latest information based on the patient's symptoms and medical records, and to provide diagnostic support. [Means for solving the problem]

[0006] The system according to the embodiment includes a patient information input unit, an information analysis unit, and a diagnostic support unit. The patient information input unit inputs the patient's symptoms and medical records. The information analysis unit analyzes the information input by the patient information input unit. The diagnostic support unit provides the latest information based on the information analyzed by the information analysis unit to provide diagnostic support. [Effects of the Invention]

[0007] The system according to the embodiment can provide up-to-date information based on the patient's symptoms and medical records, and can provide diagnostic support. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) In the diagnosis support system according to an embodiment of the present invention, when a doctor or nurse inputs a patient's symptoms and medical records, a generative AI provides the latest information on the condition and treatment, thereby assisting in diagnosis. This improves the accuracy of diagnoses and the efficiency of the entire medical process.

[0029] A diagnostic support system according to an embodiment includes a patient information input unit, an information analysis unit, and a diagnostic support unit. The patient information input unit inputs a patient's symptoms and medical records. For example, a doctor or nurse records the patient's chief complaint, medical history, current symptoms, test results, and other details. The patient information input unit can also analyze voice input, automatically convert the voice input into text using natural language processing technology, and reflect the text in the medical record. For example, the system can record a patient's speech in the examination room in real time and convert the speech into text using natural language processing technology. The information analysis unit analyzes the information input by the patient information input unit. For example, a generating AI can analyze the input patient's symptoms and medical records to provide the latest information on the patient's condition and treatment. The generating AI can refer to the latest medical papers and guidelines to suggest the most appropriate treatment for the patient's symptoms. The information analysis unit can also monitor a patient's vital signs in real time and automatically add the data to the medical record. For example, a wearable device worn by the patient can be used to monitor vital signs such as heart rate and blood pressure in real time and automatically add the data to the medical record. The diagnostic support unit provides diagnostic support by providing the latest information based on the information analyzed by the information analysis unit. For example, the generative AI lists possible diagnoses based on the patient's symptoms and presents recommended treatments for each diagnosis. The diagnostic support unit can also analyze the patient's emotional state using an emotion estimation function and reflect the patient's stress and anxiety levels in medical records. For example, the diagnostic support unit can analyze the patient's facial expressions using a camera, measure the patient's stress and anxiety levels using an emotion estimation algorithm, and reflect the data in the medical records. This improves the accuracy of diagnoses and the efficiency of the entire medical process. For example, based on the latest information provided by the generative AI, doctors can quickly select appropriate treatments and promote the patient's recovery. Furthermore, the generative AI's diagnostic support can reduce the burden on medical professionals and improve the quality of medical care.

[0030] The patient information input unit can analyze voice input, automatically convert it to text using natural language processing technology, and reflect it in medical records. For example, the patient information input unit can record what a patient says in the examination room in real time and convert that voice to text using natural language processing technology. For example, if a patient says, "I have a headache," that content is automatically reflected in the medical record. The patient information input unit can also convert the patient's voice into text data using voice recognition software. For example, the voice recognition software automatically analyzes the voice and saves it as text. Furthermore, the patient information input unit can also build a system in which text is displayed on a screen simultaneously with voice input. This allows the patient's voice input to be automatically converted to text and reflected in the medical record.

[0031] The patient information input unit can monitor vital signs in real time and automatically add the data to medical records. For example, the patient information input unit can use a wearable device worn by the patient to monitor vital signs such as heart rate and blood pressure in real time and automatically add the data to medical records. For example, if the heart rate is abnormally high, that information is immediately reflected in the medical record. The patient information input unit can also measure vital signs such as body temperature and respiratory rate using a sensor and add the data to the medical record in real time. For example, if the body temperature is high, that information is automatically saved in the medical record. Furthermore, the patient information input unit can graph the vital sign data so that medical professionals can easily check it. This allows the patient's vital signs to be monitored in real time and automatically added to the medical record.

[0032] The patient information input unit can collect lifestyle data from a wearable device and integrate it into medical records. For example, the patient information input unit uses a smart watch worn by the patient to record daily exercise and sleep patterns and automatically add the data to the medical record. For example, the number of steps taken each day and the amount of sleep time are reflected in the medical record. The patient information input unit can also collect dietary data of the patient using an application that records dietary information and integrate it into the medical record. For example, when the patient inputs dietary information, the information is saved in the medical record. Furthermore, the patient information input unit can also collect exercise data of the patient in real time using the wearable device and reflect it in the medical record. In this way, the patient's lifestyle data can be collected and integrated into the medical record.

[0033] The patient information input unit can automatically import past medical records and compare them with current symptoms to detect abnormalities. The patient information input unit can, for example, automatically import a patient's past medical data from an electronic medical record system and compare them with current symptoms to detect abnormalities. For example, it can compare past blood test results with current results to identify abnormal values. The patient information input unit can also import past image data (X-rays, MRIs, etc.) and compare them with current image data to detect abnormalities. For example, it can compare past X-ray images with current images to identify abnormal changes. The patient information input unit can also import past medical records as text data and compare them with current symptoms to detect abnormalities. This makes it possible to automatically import past medical records and detect abnormalities.

[0034] The information analysis unit can take genetic information into account and propose the optimal treatment from the perspective of personalized medicine. For example, the information analysis unit analyzes the patient's genetic information, and the generation AI proposes the optimal treatment based on that information. For example, it proposes a treatment that corresponds to a specific gene mutation. The information analysis unit can also create a treatment plan for each patient based on genetic information. For example, it selects the optimal treatment from the perspective of personalized medicine based on the results of a genetic test. Furthermore, the information analysis unit can use genetic information to predict the patient's response to treatment and adjust the treatment. This makes it possible to propose the optimal treatment from the perspective of personalized medicine, taking into account the patient's genetic information.

[0035] The information analysis unit can refer to region-specific disease data and provide treatment methods appropriate for the region. For example, the information analysis unit can refer to region-specific disease data and have the generation AI propose optimal treatment methods based on that information. For example, it can propose treatment methods for infectious diseases that are prevalent in a particular region. The information analysis unit can also analyze regional epidemiological data and propose preventive measures appropriate for the region. For example, it can recommend vaccinations based on regional infectious disease data. Furthermore, the information analysis unit can also adjust treatment methods by taking into account region-specific environmental factors (climate, geography, etc.). This makes it possible to refer to region-specific disease data and provide treatment methods appropriate for the region.

[0036] The information analysis unit can take living environment data into consideration and suggest treatment methods appropriate for the environment. For example, the information analysis unit analyzes air quality data from the patient's home, and the generation AI uses that information to suggest the optimal treatment method. For example, if the air quality is poor, it can suggest the use of an air purifier. The information analysis unit can also analyze the patient's living environment data (noise level, temperature, etc.) and suggest treatment methods appropriate for the environment. For example, it can suggest the use of earplugs if the noise level is high. Furthermore, the information analysis unit can predict the patient's health condition and suggest preventive measures based on the living environment data. This makes it possible to take the patient's living environment data into consideration and suggest treatment methods appropriate for the environment.

[0037] The information analysis unit can take social background into consideration and propose treatment methods that include social support. For example, the information analysis unit analyzes the patient's family structure, and the generation AI uses that information to propose treatment methods that include social support. For example, it can propose family counseling if family support is needed. The information analysis unit can also adjust treatment methods by taking into consideration the patient's occupation and whether or not they have social support. For example, it can propose a rehabilitation program that suits their occupation. Furthermore, the information analysis unit can introduce local support services based on the patient's social background. This makes it possible to propose treatment methods that include social support by taking into consideration the patient's social background.

[0038] The diagnostic support unit can learn from past diagnostic data and improve the accuracy of diagnosis. For example, the diagnostic support unit uses a generative AI to learn from past diagnostic data and improve the accuracy of diagnosis based on that knowledge. For example, it compares past diagnostic results with current symptoms and proposes the optimal diagnosis. The diagnostic support unit can also use past diagnostic data to improve the diagnostic algorithm. For example, it improves the accuracy of the diagnostic algorithm based on past data. Furthermore, the diagnostic support unit can also use past diagnostic data to analyze diagnostic trends and improve the accuracy of diagnosis. In this way, it is possible to learn from past diagnostic data and improve the accuracy of diagnosis.

[0039] The diagnostic support unit can improve the reliability of the diagnosis by combining multiple diagnostic algorithms. For example, the diagnostic support unit uses a generative AI to perform a diagnosis by combining multiple diagnostic algorithms, and then integrates the results to propose an optimal diagnosis. For example, it combines an image analysis algorithm and a text analysis algorithm. The diagnostic support unit can also evaluate the reliability of the diagnosis using multiple diagnostic algorithms. For example, it compares the results of each algorithm and evaluates the reliability based on the degree of agreement. Furthermore, the diagnostic support unit can also improve the accuracy of the diagnosis by using multiple diagnostic algorithms. This makes it possible to improve the reliability of the diagnosis by combining multiple diagnostic algorithms.

[0040] The diagnostic support unit can improve diagnostic accuracy by strengthening collaboration with other medical institutions and sharing diagnostic results. For example, the diagnostic support unit uses the generative AI to refer to diagnostic data from other medical institutions and improve diagnostic accuracy based on that information. For example, diagnostic results from other medical institutions can be shared and used as a reference for diagnosis. The diagnostic support unit can also establish data sharing protocols with other medical institutions and share diagnostic results. For example, diagnostic data can be shared through an electronic medical record system. Furthermore, the diagnostic support unit can build a collaboration system with other medical institutions and improve diagnostic accuracy. This can strengthen collaboration with other medical institutions and improve diagnostic accuracy by sharing diagnostic results.

[0041] The diagnostic support unit can incorporate self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities. For example, the diagnostic support unit incorporates data self-diagnosed by a patient using a generation AI, and based on that information, compares it with a doctor's diagnosis to detect abnormalities. For example, it compares the patient's self-diagnosed symptoms with the doctor's diagnosis. The diagnostic support unit can also use the self-diagnosis data to evaluate the accuracy of the doctor's diagnosis. For example, it evaluates based on the degree of agreement between the self-diagnosis data and the doctor's diagnosis. Furthermore, the diagnostic support unit can use the self-diagnosis data to develop an algorithm for detecting abnormalities. This makes it possible to incorporate a patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities.

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

[0043] The diagnostic support system can also collect data on the patient's living environment and reflect it in the diagnosis. For example, the system analyzes air quality data from the patient's home and uses that information to suggest the optimal treatment. If the air quality is poor, it can suggest the use of an air purifier. It can also analyze the patient's living environment data (noise level, temperature, etc.) and suggest treatment appropriate for the environment. If the noise level is high, it can suggest the use of earplugs. Furthermore, it can predict the patient's health condition and suggest preventive measures based on the living environment data. This makes it possible to suggest treatment appropriate for the environment, taking into account the patient's living environment data.

[0044] The diagnostic support system can also take the patient's social background into account and suggest treatment methods that include social support. For example, the system analyzes the patient's family structure and uses that information to suggest treatment methods that include social support. If family support is required, family counseling can be suggested. Treatment methods can also be adjusted taking into account the patient's occupation and whether or not they have social support. Rehabilitation programs tailored to their occupation can be suggested. Furthermore, local support services can be introduced based on the patient's social background. This makes it possible to suggest treatment methods that include social support, taking into account the patient's social background.

[0045] The diagnostic support system can further consider the patient's genetic information and propose the optimal treatment from the perspective of personalized medicine. For example, the patient's genetic information is analyzed, and the generating AI proposes the optimal treatment based on that information. It can propose treatments that correspond to specific gene mutations. It can also create treatment plans for each patient based on genetic information. Based on the results of genetic testing, it can select the optimal treatment from the perspective of personalized medicine. Furthermore, genetic information can be used to predict the patient's response to treatment and adjust the treatment. This makes it possible to propose the optimal treatment from the perspective of personalized medicine, taking into account the patient's genetic information.

[0046] The diagnostic support system can further refer to region-specific disease data and provide treatments appropriate for the region. For example, the generative AI can refer to region-specific disease data and use that information to propose the optimal treatment. It can propose treatments for infectious diseases that are prevalent in a specific region. It can also analyze regional epidemiological data and propose preventive measures appropriate for the region. Vaccinations can be recommended based on regional infectious disease data. It can also adjust treatments by taking into account regional environmental factors (climate, geography, etc.). This makes it possible to refer to region-specific disease data and provide treatments appropriate for the region.

[0047] The diagnostic support system can also incorporate the patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities. For example, the generation AI can incorporate the patient's self-diagnosis data and use that information to compare it with a doctor's diagnosis to detect abnormalities. The patient's self-diagnosed symptoms can be compared with the doctor's diagnosis. The self-diagnosis data can also be used to evaluate the accuracy of the doctor's diagnosis. Evaluation can be based on the degree of agreement between the self-diagnosis data and the doctor's diagnosis. Furthermore, the self-diagnosis data can also be used to develop an algorithm for detecting abnormalities. This makes it possible to incorporate the patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The patient information input unit inputs the patient's symptoms and medical records. For example, a doctor or nurse might record the patient's chief complaint, medical history, current symptoms, test results, and other details. The patient information input unit can also analyze voice input, automatically convert it to text using natural language processing technology, and reflect it in the medical record. For example, it can record what the patient says in the examination room in real time and convert that voice to text using natural language processing technology. Step 2: The information analysis unit analyzes the information entered by the patient information input unit. For example, the generating AI analyzes the patient's symptoms and medical records entered and provides the latest information on the patient's condition and treatment. The generating AI refers to the latest medical papers and guidelines and suggests the treatment that is most appropriate for the patient's symptoms. The information analysis unit can also monitor the patient's vital signs in real time and automatically add that data to the medical record. For example, a wearable device worn by the patient can be used to monitor vital signs such as heart rate and blood pressure in real time and automatically add that data to the medical record. Step 3: The diagnostic support unit provides diagnostic support by providing the latest information based on the information analyzed by the information analysis unit. For example, the generative AI lists possible diagnoses based on the patient's symptoms and presents recommended treatments for each diagnosis. The diagnostic support unit can also analyze the patient's emotional state using an emotion estimation function and reflect the patient's stress and anxiety levels in the medical record. For example, the unit can analyze the patient's facial expressions using a camera, measure the patient's stress and anxiety levels using an emotion estimation algorithm, and reflect that data in the medical record.

[0050] (Example 2) In the diagnosis support system according to an embodiment of the present invention, when a doctor or nurse inputs a patient's symptoms and medical records, a generative AI provides the latest information on the condition and treatment, thereby assisting in diagnosis. This improves the accuracy of diagnoses and the efficiency of the entire medical process.

[0051] A diagnostic support system according to an embodiment includes a patient information input unit, an information analysis unit, and a diagnostic support unit. The patient information input unit inputs a patient's symptoms and medical records. For example, a doctor or nurse records the patient's chief complaint, medical history, current symptoms, test results, and other details. The patient information input unit can also analyze voice input, automatically convert the voice input into text using natural language processing technology, and reflect the text in the medical record. For example, the system can record a patient's speech in the examination room in real time and convert the speech into text using natural language processing technology. The information analysis unit analyzes the information input by the patient information input unit. For example, a generating AI can analyze the input patient's symptoms and medical records to provide the latest information on the patient's condition and treatment. The generating AI can refer to the latest medical papers and guidelines to suggest the most appropriate treatment for the patient's symptoms. The information analysis unit can also monitor a patient's vital signs in real time and automatically add the data to the medical record. For example, a wearable device worn by the patient can be used to monitor vital signs such as heart rate and blood pressure in real time and automatically add the data to the medical record. The diagnostic support unit provides diagnostic support by providing the latest information based on the information analyzed by the information analysis unit. For example, the generative AI lists possible diagnoses based on the patient's symptoms and presents recommended treatments for each diagnosis. The diagnostic support unit can also analyze the patient's emotional state using an emotion estimation function and reflect the patient's stress and anxiety levels in medical records. For example, the diagnostic support unit can analyze the patient's facial expressions using a camera, measure the patient's stress and anxiety levels using an emotion estimation algorithm, and reflect the data in the medical records. This improves the accuracy of diagnoses and the efficiency of the entire medical process. For example, based on the latest information provided by the generative AI, doctors can quickly select appropriate treatments and promote the patient's recovery. Furthermore, the generative AI's diagnostic support can reduce the burden on medical professionals and improve the quality of medical care.

[0052] The patient information input unit can analyze voice input, automatically convert it to text using natural language processing technology, and reflect it in medical records. For example, the patient information input unit can record what a patient says in the examination room in real time and convert that voice to text using natural language processing technology. For example, if a patient says, "I have a headache," that content is automatically reflected in the medical record. The patient information input unit can also convert the patient's voice into text data using voice recognition software. For example, the voice recognition software automatically analyzes the voice and saves it as text. Furthermore, the patient information input unit can also build a system in which text is displayed on a screen simultaneously with voice input. This allows the patient's voice input to be automatically converted to text and reflected in the medical record.

[0053] The patient information input unit can monitor vital signs in real time and automatically add the data to medical records. For example, the patient information input unit can use a wearable device worn by the patient to monitor vital signs such as heart rate and blood pressure in real time and automatically add the data to medical records. For example, if the heart rate is abnormally high, that information is immediately reflected in the medical record. The patient information input unit can also measure vital signs such as body temperature and respiratory rate using a sensor and add the data to the medical record in real time. For example, if the body temperature is high, that information is automatically saved in the medical record. Furthermore, the patient information input unit can graph the vital sign data so that medical professionals can easily check it. This allows the patient's vital signs to be monitored in real time and automatically added to the medical record.

[0054] The patient information input unit can analyze the patient's emotional state using an emotion estimation function and reflect the level of stress and anxiety in the medical record. For example, the patient information input unit can analyze the patient's facial expression using a camera, measure the patient's stress and anxiety level using an emotion estimation algorithm, and reflect the data in the medical record. For example, if the patient looks anxious during an examination, this information is saved in the medical record. The patient information input unit can also analyze the patient's voice and estimate the patient's emotion using voice analysis technology. For example, the tone and speed of the voice can be analyzed to calculate an emotion score. Furthermore, the patient information input unit can collect the patient's biometric data (heart rate and electrodermal activity) using a sensor and analyze the patient's emotion using an emotion estimation algorithm. For example, an emotion score can be calculated based on heart rate fluctuations. This allows the patient's emotional state to be analyzed and reflected in the medical record.

[0055] The patient information input unit can collect lifestyle data from a wearable device and integrate it into medical records. For example, the patient information input unit uses a smart watch worn by the patient to record daily exercise and sleep patterns and automatically add the data to the medical record. For example, the number of steps taken each day and the amount of sleep time are reflected in the medical record. The patient information input unit can also collect dietary data of the patient using an application that records dietary information and integrate it into the medical record. For example, when the patient inputs dietary information, the information is saved in the medical record. Furthermore, the patient information input unit can also collect exercise data of the patient in real time using the wearable device and reflect it in the medical record. In this way, the patient's lifestyle data can be collected and integrated into the medical record.

[0056] The patient information input unit can automatically import past medical records and compare them with current symptoms to detect abnormalities. The patient information input unit can, for example, automatically import a patient's past medical data from an electronic medical record system and compare them with current symptoms to detect abnormalities. For example, it can compare past blood test results with current results to identify abnormal values. The patient information input unit can also import past image data (X-rays, MRIs, etc.) and compare them with current image data to detect abnormalities. For example, it can compare past X-ray images with current images to identify abnormal changes. The patient information input unit can also import past medical records as text data and compare them with current symptoms to detect abnormalities. This makes it possible to automatically import past medical records and detect abnormalities.

[0057] The patient information input unit can use an emotion estimation function to analyze the emotion of the patient when entering information in real time and evaluate the reliability of the input content. For example, the patient information input unit analyzes text data entered by the patient in real time and evaluates the reliability of the input content using emotion estimation technology. For example, if a patient enters "it hurts a lot," the emotional state is analyzed and reliability is evaluated. The patient information input unit can also analyze the patient's voice and estimate the emotion using voice analysis technology to evaluate the reliability of the input content. For example, the tone and speed of the voice are analyzed and reliability is evaluated based on the emotion score. Furthermore, the patient information input unit can analyze the patient's facial expression using a camera, analyze the emotion using an emotion estimation algorithm, and evaluate the reliability of the input content. In this way, the emotion of the patient when entering information can be analyzed and the reliability of the input content can be evaluated.

[0058] The information analysis unit can take genetic information into account and propose the optimal treatment from the perspective of personalized medicine. For example, the information analysis unit analyzes the patient's genetic information, and the generation AI proposes the optimal treatment based on that information. For example, it proposes a treatment that corresponds to a specific gene mutation. The information analysis unit can also create a treatment plan for each patient based on genetic information. For example, it selects the optimal treatment from the perspective of personalized medicine based on the results of a genetic test. Furthermore, the information analysis unit can use genetic information to predict the patient's response to treatment and adjust the treatment. This makes it possible to propose the optimal treatment from the perspective of personalized medicine, taking into account the patient's genetic information.

[0059] The information analysis unit can refer to region-specific disease data and provide treatment methods appropriate for the region. For example, the information analysis unit can refer to region-specific disease data and have the generation AI propose optimal treatment methods based on that information. For example, it can propose treatment methods for infectious diseases that are prevalent in a particular region. The information analysis unit can also analyze regional epidemiological data and propose preventive measures appropriate for the region. For example, it can recommend vaccinations based on regional infectious disease data. Furthermore, the information analysis unit can also adjust treatment methods by taking into account region-specific environmental factors (climate, geography, etc.). This makes it possible to refer to region-specific disease data and provide treatment methods appropriate for the region.

[0060] The information analysis unit can take living environment data into consideration and suggest treatment methods appropriate for the environment. For example, the information analysis unit analyzes air quality data from the patient's home, and the generation AI uses that information to suggest the optimal treatment method. For example, if the air quality is poor, it can suggest the use of an air purifier. The information analysis unit can also analyze the patient's living environment data (noise level, temperature, etc.) and suggest treatment methods appropriate for the environment. For example, it can suggest the use of earplugs if the noise level is high. Furthermore, the information analysis unit can predict the patient's health condition and suggest preventive measures based on the living environment data. This makes it possible to take the patient's living environment data into consideration and suggest treatment methods appropriate for the environment.

[0061] The information analysis unit can take social background into consideration and propose treatment methods that include social support. For example, the information analysis unit analyzes the patient's family structure, and the generation AI uses that information to propose treatment methods that include social support. For example, it can propose family counseling if family support is needed. The information analysis unit can also adjust treatment methods by taking into consideration the patient's occupation and whether or not they have social support. For example, it can propose a rehabilitation program that suits their occupation. Furthermore, the information analysis unit can introduce local support services based on the patient's social background. This makes it possible to propose treatment methods that include social support by taking into consideration the patient's social background.

[0062] The information analysis unit can use the emotion estimation function to monitor the emotional state in real time and provide information according to the emotion. For example, the information analysis unit can monitor the patient's emotional state in real time, and the generation AI can provide appropriate information based on that information. For example, if the patient is feeling anxious, the information analysis unit can suggest relaxation techniques. The information analysis unit can also use the emotion estimation function to provide advice based on the patient's emotional state. For example, it can suggest stress management methods based on the emotion score. Furthermore, the information analysis unit can monitor the emotional state in real time and customize the content of the information provided. This makes it possible to monitor the patient's emotional state in real time and provide information according to the emotion.

[0063] The diagnostic support unit can learn from past diagnostic data and improve the accuracy of diagnosis. For example, the diagnostic support unit uses a generative AI to learn from past diagnostic data and improve the accuracy of diagnosis based on that knowledge. For example, it compares past diagnostic results with current symptoms and proposes the optimal diagnosis. The diagnostic support unit can also use past diagnostic data to improve the diagnostic algorithm. For example, it improves the accuracy of the diagnostic algorithm based on past data. Furthermore, the diagnostic support unit can also use past diagnostic data to analyze diagnostic trends and improve the accuracy of diagnosis. In this way, it is possible to learn from past diagnostic data and improve the accuracy of diagnosis.

[0064] The diagnostic support unit can improve the reliability of the diagnosis by combining multiple diagnostic algorithms. For example, the diagnostic support unit uses a generative AI to perform a diagnosis by combining multiple diagnostic algorithms, and then integrates the results to propose an optimal diagnosis. For example, it combines an image analysis algorithm and a text analysis algorithm. The diagnostic support unit can also evaluate the reliability of the diagnosis using multiple diagnostic algorithms. For example, it compares the results of each algorithm and evaluates the reliability based on the degree of agreement. Furthermore, the diagnostic support unit can also improve the accuracy of the diagnosis by using multiple diagnostic algorithms. This makes it possible to improve the reliability of the diagnosis by combining multiple diagnostic algorithms.

[0065] The diagnostic support unit uses the emotion estimation function to make a diagnosis that takes into account the patient's emotional state, thereby reducing the patient's psychological burden. For example, the diagnostic support unit analyzes the patient's emotional state, and the generation AI uses that information to make a diagnosis that reduces the patient's psychological burden. For example, it suggests relaxation techniques for patients with high stress. The diagnostic support unit can also use the emotion estimation function to create a diagnostic protocol based on the patient's emotional state. For example, it can perform a psychological evaluation and adjust the diagnosis based on the emotion score. Furthermore, the diagnostic support unit can monitor the patient's emotional state in real time and adjust the content of the diagnosis. This makes it possible to make a diagnosis that takes into account the patient's emotional state, thereby reducing the patient's psychological burden.

[0066] The diagnostic support unit can improve diagnostic accuracy by strengthening collaboration with other medical institutions and sharing diagnostic results. For example, the diagnostic support unit uses the generative AI to refer to diagnostic data from other medical institutions and improve diagnostic accuracy based on that information. For example, diagnostic results from other medical institutions can be shared and used as a reference for diagnosis. The diagnostic support unit can also establish data sharing protocols with other medical institutions and share diagnostic results. For example, diagnostic data can be shared through an electronic medical record system. Furthermore, the diagnostic support unit can build a collaboration system with other medical institutions and improve diagnostic accuracy. This can strengthen collaboration with other medical institutions and improve diagnostic accuracy by sharing diagnostic results.

[0067] The diagnostic support unit can incorporate self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities. For example, the diagnostic support unit incorporates data self-diagnosed by a patient using a generation AI, and based on that information, compares it with a doctor's diagnosis to detect abnormalities. For example, it compares the patient's self-diagnosed symptoms with the doctor's diagnosis. The diagnostic support unit can also use the self-diagnosis data to evaluate the accuracy of the doctor's diagnosis. For example, it evaluates based on the degree of agreement between the self-diagnosis data and the doctor's diagnosis. Furthermore, the diagnostic support unit can use the self-diagnosis data to develop an algorithm for detecting abnormalities. This makes it possible to incorporate a patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities.

[0068] The diagnostic support unit can use the emotion estimation function to monitor the emotional reactions to the diagnostic results in real time and provide appropriate feedback. For example, the diagnostic support unit can monitor the patient's emotional reactions in real time, and the generation AI can provide appropriate feedback based on that information. For example, if the patient is feeling anxious, the diagnostic support unit can suggest relaxation techniques. The diagnostic support unit can also use the emotion estimation function to provide feedback based on the patient's emotional reactions. For example, it can provide psychological support based on the emotion score. Furthermore, the diagnostic support unit can monitor the emotional reactions in real time and adjust the content of the feedback. This makes it possible to monitor the patient's emotional reactions to the diagnostic results in real time and provide appropriate feedback.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The diagnostic support system can also collect data on the patient's living environment and reflect it in the diagnosis. For example, the system analyzes air quality data from the patient's home and uses that information to suggest the optimal treatment. If the air quality is poor, it can suggest the use of an air purifier. It can also analyze the patient's living environment data (noise level, temperature, etc.) and suggest treatment appropriate for the environment. If the noise level is high, it can suggest the use of earplugs. Furthermore, it can predict the patient's health condition and suggest preventive measures based on the living environment data. This makes it possible to suggest treatment appropriate for the environment, taking into account the patient's living environment data.

[0071] The diagnostic support system can also take the patient's social background into account and suggest treatment methods that include social support. For example, the system analyzes the patient's family structure and uses that information to suggest treatment methods that include social support. If family support is required, family counseling can be suggested. Treatment methods can also be adjusted taking into account the patient's occupation and whether or not they have social support. Rehabilitation programs tailored to their occupation can be suggested. Furthermore, local support services can be introduced based on the patient's social background. This makes it possible to suggest treatment methods that include social support, taking into account the patient's social background.

[0072] The diagnostic support system can further consider the patient's genetic information and propose the optimal treatment from the perspective of personalized medicine. For example, the patient's genetic information is analyzed, and the generating AI proposes the optimal treatment based on that information. It can propose treatments that correspond to specific gene mutations. It can also create treatment plans for each patient based on genetic information. Based on the results of genetic testing, it can select the optimal treatment from the perspective of personalized medicine. Furthermore, genetic information can be used to predict the patient's response to treatment and adjust the treatment. This makes it possible to propose the optimal treatment from the perspective of personalized medicine, taking into account the patient's genetic information.

[0073] The diagnostic support system can further refer to region-specific disease data and provide treatments appropriate for the region. For example, the generative AI can refer to region-specific disease data and use that information to propose the optimal treatment. It can propose treatments for infectious diseases that are prevalent in a specific region. It can also analyze regional epidemiological data and propose preventive measures appropriate for the region. Vaccinations can be recommended based on regional infectious disease data. It can also adjust treatments by taking into account regional environmental factors (climate, geography, etc.). This makes it possible to refer to region-specific disease data and provide treatments appropriate for the region.

[0074] The diagnostic support system can also incorporate the patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities. For example, the generation AI can incorporate the patient's self-diagnosis data and use that information to compare it with a doctor's diagnosis to detect abnormalities. The patient's self-diagnosed symptoms can be compared with the doctor's diagnosis. The self-diagnosis data can also be used to evaluate the accuracy of the doctor's diagnosis. Evaluation can be based on the degree of agreement between the self-diagnosis data and the doctor's diagnosis. Furthermore, the self-diagnosis data can also be used to develop an algorithm for detecting abnormalities. This makes it possible to incorporate the patient's self-diagnosis data and compare it with a doctor's diagnosis to detect abnormalities.

[0075] The diagnostic support system can monitor a patient's emotional state in real time and provide information according to their emotions. For example, the system can monitor a patient's emotional state in real time and use the generation AI to provide appropriate information based on that information. If the patient is feeling anxious, it can suggest relaxation techniques. It can also use the emotion estimation function to provide advice based on the patient's emotional state. It can also suggest stress management methods based on the emotion score. It can also monitor the emotional state in real time and customize the content of the information provided. This makes it possible to monitor a patient's emotional state in real time and provide information according to their emotions.

[0076] The diagnostic support system can reduce the psychological burden on patients by making diagnoses that take into account the patient's emotional state. For example, the system analyzes the patient's emotional state and uses that information to make diagnoses that reduce psychological burden. It can also suggest relaxation methods for patients with high stress. It can also use the emotion estimation function to create diagnostic protocols based on the patient's emotional state. It can perform psychological evaluations and adjust diagnoses based on emotion scores. It can also monitor the emotional state in real time and adjust the content of the diagnosis. This allows for diagnoses that take into account the patient's emotional state and reduce psychological burden.

[0077] The diagnostic support system can analyze the emotions of patients when they enter text in real time and evaluate the reliability of the input content. For example, it can analyze text data entered by patients in real time and use emotion estimation technology to evaluate the reliability of the input content. If a patient enters "It hurts so much," it can analyze their emotional state and evaluate reliability. It can also analyze the patient's voice, estimate their emotions using voice analysis technology, and evaluate the reliability of the input content. It can analyze the tone and speed of the voice and evaluate reliability based on an emotion score. It can also analyze the patient's facial expressions with a camera, analyze their emotions using an emotion estimation algorithm, and evaluate the reliability of the input content. This makes it possible to analyze the emotions of patients when they enter text and evaluate the reliability of the input content.

[0078] The diagnostic support system can monitor the patient's emotional response to the diagnosis results in real time and provide appropriate feedback. For example, the patient's emotional response can be monitored in real time and the generation AI can provide appropriate feedback based on that information. If the patient is feeling anxious, relaxation techniques can be suggested. In addition, the emotion estimation function can be used to provide feedback based on the patient's emotional response. Psychological support can be provided based on the emotion score. Furthermore, emotional responses can be monitored in real time and the content of the feedback can be adjusted. This makes it possible to monitor the patient's emotional response to the diagnosis results in real time and provide appropriate feedback.

[0079] The diagnostic support system can analyze the patient's emotional state and adjust the way diagnostic results are presented based on that emotion. For example, the system analyzes the patient's emotional state and uses that information to adjust the way diagnostic results are presented. If the patient is feeling anxious, the system can explain the diagnostic results gently. The system can also use the emotion estimation function to customize the way diagnostic results are presented based on the patient's emotional state. Diagnostic results can be presented in stages based on the emotion score. Furthermore, the system can monitor the patient's emotional state in real time and adjust the way diagnostic results are presented. This makes it possible to analyze the patient's emotional state and adjust the way diagnostic results are presented based on their emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The patient information input unit inputs the patient's symptoms and medical records. For example, a doctor or nurse might record the patient's chief complaint, medical history, current symptoms, test results, and other details. The patient information input unit can also analyze voice input, automatically convert it to text using natural language processing technology, and reflect it in the medical record. For example, it can record what the patient says in the examination room in real time and convert that voice to text using natural language processing technology. Step 2: The information analysis unit analyzes the information entered by the patient information input unit. For example, the generating AI analyzes the patient's symptoms and medical records entered and provides the latest information on the patient's condition and treatment. The generating AI refers to the latest medical papers and guidelines and suggests the treatment that is most appropriate for the patient's symptoms. The information analysis unit can also monitor the patient's vital signs in real time and automatically add that data to the medical record. For example, a wearable device worn by the patient can be used to monitor vital signs such as heart rate and blood pressure in real time and automatically add that data to the medical record. Step 3: The diagnostic support unit provides diagnostic support by providing the latest information based on the information analyzed by the information analysis unit. For example, the generative AI lists possible diagnoses based on the patient's symptoms and presents recommended treatments for each diagnosis. The diagnostic support unit can also analyze the patient's emotional state using an emotion estimation function and reflect the patient's stress and anxiety levels in the medical record. For example, the unit can analyze the patient's facial expressions using a camera, measure the patient's stress and anxiety levels using an emotion estimation algorithm, and reflect that data in the medical record.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 patient information input unit for inputting patient symptoms and medical records; an information analysis unit that analyzes the information input by the patient information input unit; a diagnostic support unit that provides the latest information based on the information analyzed by the information analysis unit and performs diagnostic support. A system characterized by:

2. The patient information input unit Real-time monitoring of vital signs and automatic addition of that data to the medical record 2. The system of claim 1.

3. The patient information input unit Lifestyle data is collected from wearable devices and integrated into the medical record.

2. The system of claim 1.

4. The information analysis unit Taking genetic information into consideration, we propose optimal treatment methods from the perspective of personalized medicine.

2. The system of claim 1.

5. The diagnosis support unit Emotion estimation function is used to make diagnoses that take into account the patient's emotional state, reducing the psychological burden on the patient.

2. The system of claim 1.

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