System

Abstract

An object of the system according to the embodiment is to efficiently perform diagnostic support, personalized medicine, mental health care, and drug discovery.SOLUTION: A system according to an embodiment includes a diagnostic support unit, a personalized healthcare unit, a mental health care unit, and a drug discovery unit. The diagnostic support unit analyzes a large amount of medical information in a AI manner and performs early detection or diagnosis of a disease. The personalized health care unit analyzes genetic information, lifestyle, medical history, and the like of an individual patient, and provides an optimal treatment method and health care advice. The mental health care unit grasps a state of mental health of the user using text analysis or speech recognition, and provides introduction to a specialist or advice for stress management as necessary. The drug discovery unit discovers a new drug candidate from an enormous amount of compound data and finds out a possibility that an existing drug has an effect on another disease.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not sufficiently streamline the processes of diagnostic support using medical data, personalized medicine, mental health care, and new drug discovery, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide diagnostic support, personalized medicine, mental health care, and new drug discovery by utilizing medical data. [Means for solving the problem]

[0006] The system according to the embodiment comprises a diagnostic support unit, a personalized healthcare unit, a mental healthcare unit, and a drug discovery unit. The diagnostic support unit uses AI to analyze large volumes of medical data to detect and diagnose diseases early. The personalized healthcare unit analyzes each patient's genetic information, lifestyle, medical history, etc., and provides optimal treatment and health management advice. The mental healthcare unit uses text analysis and voice recognition to understand the user's mental health status and, if necessary, provides referrals to specialists and stress management advice. The drug discovery unit discovers new drug candidates from vast amounts of compound data and determines whether existing drugs may be effective against other diseases. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform diagnostic support, personalized medicine, mental health care, and new drug discovery by utilizing medical data. [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) A medical support system according to an embodiment of the present invention utilizes AI technology to provide various support functions in the medical field. This system provides four main functions: AI diagnostic support, personalized healthcare, mental healthcare, and drug discovery. This enables the medical support system to efficiently perform early detection and diagnosis of diseases, individualized health management, mental health assessment, and discovery of new drug candidates.

[0029] A medical support system according to an embodiment includes a diagnostic support unit, a personalized healthcare unit, a mental healthcare unit, and a drug discovery unit. The diagnostic support unit uses AI to analyze medical data and perform early detection and diagnosis of diseases. For example, the diagnostic support unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze X-ray images and MRI images and detect abnormalities. The generation AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the analysis results of the generation AI. The personalized healthcare unit uses AI to analyze the genetic information, lifestyle, medical history, etc. of individual patients and provide optimal treatment and health management advice for each individual. For example, the personalized healthcare unit uses generation AI to analyze the patient's genetic information and lifestyle data and propose optimal diet plans and exercise programs. The generation AI receives individual patient data as input and generates optimal advice. The mental healthcare unit uses AI to perform text analysis and speech recognition to understand the user's mental health status. For example, the mental healthcare unit uses a generative AI to analyze a user's statements and writings to detect signs of stress and anxiety. The generative AI receives the user's text data and voice data as input and evaluates their mental health state. The drug discovery unit uses AI to analyze massive amounts of compound data and discover new drug candidates. For example, the drug discovery unit uses a generative AI to analyze existing drug data and identify drugs that may be effective against other diseases. The generative AI receives compound data as input and identifies new drug candidates. As a result, the medical support system according to the embodiment can efficiently perform early detection and diagnosis of diseases, personalized health management, mental health evaluation, and discovery of new drug candidates.

[0030] The diagnostic support unit uses the generating AI to analyze medical image data and display the probability of detecting an abnormality in real time. For example, the diagnostic support unit uses the generating AI to analyze X-ray images and display the probability of abnormalities in real time. For example, in a lung X-ray image, the probability of the presence of a nodule or tumor can be quantified, allowing doctors to make an immediate judgment. The generating AI receives medical image data as input, identifies abnormalities, and displays their probability. This allows doctors to make an immediate judgment.

[0031] The diagnostic support unit can automatically search for similar past case data for abnormalities analyzed using the generation AI and provide it as reference information. For example, the diagnostic support unit uses the generation AI to analyze abnormalities in an X-ray image and automatically search for similar past case data. For example, if a lung nodule is detected, similar past case data is provided for doctors to use as reference. The generation AI receives the abnormality as input, searches for similar past case data, and provides it as reference information. This makes it possible to provide information that doctors can use as reference.

[0032] The diagnostic support unit can simultaneously analyze not only medical image data but also the patient's vital sign data to provide comprehensive diagnostic support. For example, the diagnostic support unit uses a generation AI to simultaneously analyze X-ray images and the patient's vital sign data to provide comprehensive diagnostic support. For example, it combines and analyzes lung X-ray images with heart rate and blood pressure data. The generation AI receives medical image data and vital sign data as input and provides comprehensive diagnostic results. This makes it possible to provide comprehensive diagnostic support.

[0033] The diagnostic support unit promotes data sharing between different medical institutions, allowing the generating AI to integrate and analyze information from multiple data sources. For example, the diagnostic support unit allows the generating AI to integrate X-ray image data from different medical institutions and perform a comprehensive analysis. For example, it analyzes lung X-ray images from multiple hospitals to improve the accuracy of abnormality detection. The generating AI receives multiple data sources as input and performs an integrated analysis. This improves the accuracy of abnormality detection.

[0034] The personalized healthcare unit uses generative AI to perform risk assessments that combine a patient's genetic information and environmental factors and propose preventive measures. For example, the personalized healthcare unit uses generative AI to analyze a patient's genetic information and environmental factors to perform a risk assessment of cardiovascular disease. For example, it considers family history and lifestyle habits and proposes exercise programs and dietary advice as preventive measures. The generative AI receives genetic information and environmental factors as input and generates risk assessments and preventive measures. This makes it possible to provide individualized preventive measures to patients.

[0035] The personalized healthcare unit collects a patient's lifestyle data in real time, and the generating AI can continuously update health management advice based on that data. For example, the personalized healthcare unit allows the generating AI to collect a patient's lifestyle data in real time and continuously update health management advice. For example, it monitors the amount of exercise and dietary content and provides appropriate advice. The generating AI receives lifestyle data as input and continuously updates its advice. This allows for continuous support for the patient's health management.

[0036] The personalized healthcare unit can share advice with family and caregivers to support team-based health management. For example, the personalized healthcare unit will build a system to share personalized healthcare advice provided by the generative AI with family and caregivers. For example, a patient's health management plan can be shared with family members to provide joint support. The generative AI receives advice as input and shares it with family and caregivers. This can support team-based health management.

[0037] The personalized healthcare unit works in conjunction with different health management applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, the personalized healthcare unit works in conjunction with different health management applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, data from a fitness app and a diet management app is integrated and analyzed. The generation AI receives multiple data sources as input and generates integrated advice. This makes it possible to provide integrated advice from multiple data sources.

[0038] When assessing a user's mental health status, the mental health care unit allows the generation AI to compare it with past mental health history to detect abnormalities. For example, the mental health care unit allows the generation AI to compare the user's current mental health status with past history to detect abnormalities. For example, it detects abnormalities by comparing it with past stress levels or anxiety scores. The generation AI receives the mental health status and past history as input and detects abnormalities. This allows it to detect abnormalities by comparing it with past history.

[0039] The mental healthcare unit can integrate mental healthcare data with the user's physical health data to provide comprehensive health management. For example, the generation AI can integrate mental healthcare data and physical health data to provide comprehensive health management. For example, it can combine and analyze stress level and heart rate data. The generation AI receives mental healthcare data and physical health data as input and provides comprehensive health management. This allows for comprehensive health management.

[0040] The mental healthcare unit works in conjunction with different mental healthcare applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, the mental healthcare unit works in conjunction with different mental healthcare applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, data from a stress management app and a meditation app is integrated and analyzed. The generation AI receives multiple data sources as input and generates integrated advice. This makes it possible to provide integrated advice from multiple data sources.

[0041] The drug discovery unit uses generative AI to simulate the effects of new drug candidates based on the results of compound data analysis, improving prediction accuracy. For example, the drug discovery unit uses generative AI to analyze compound data and simulate the effects of new drug candidates. For example, it simulates how a specific compound acts on cancer cells. The generative AI receives compound data as input and simulates the effects of drug candidates. This makes it possible to simulate the effects of new drug candidates and improve prediction accuracy.

[0042] The drug discovery unit can automatically search past clinical trial data for drug candidates analyzed by the generation AI and provide it as reference information. For example, the drug discovery unit has the generation AI analyze a new drug candidate and automatically search past clinical trial data. For example, it can provide information on the results of past clinical trials of a specific compound. The generation AI receives a drug candidate as input, searches past clinical trial data, and provides it as reference information. This makes it possible to provide past clinical trial data as reference information.

[0043] The drug discovery department can support personalized drug development by simultaneously analyzing not only compound data but also the patient's genetic information and medical history data. For example, the drug discovery department's generative AI can simultaneously analyze compound data and the patient's genetic information to support personalized drug development. For example, it can identify drugs that are effective for patients with specific genetic backgrounds. The generative AI receives compound data, genetic information, and medical history data as input to support personalized drug development. This can support personalized drug development.

[0044] The drug discovery department promotes data sharing between different research institutions, allowing the generation AI to integrate and analyze information from multiple data sources. In the drug discovery department, for example, the generation AI integrates compound data from different research institutions and performs comprehensive analysis. For example, it analyzes data from multiple research institutions to identify new drug candidates. The generation AI receives multiple data sources as input and performs integrated analysis. This allows information from multiple data sources to be integrated and analyzed.

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

[0046] The diagnostic support unit uses generative AI to analyze medical data for early detection and diagnosis of diseases. For example, the diagnostic support unit uses generative AI to analyze X-ray images and MRI images to detect abnormalities. The generative AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the generative AI's analysis results. The personalized healthcare unit uses AI to analyze each patient's genetic information, lifestyle, medical history, etc., and provide optimal treatment and health management advice for that individual. For example, the personalized healthcare unit uses generative AI to analyze a patient's genetic information and lifestyle data and propose optimal diet plans and exercise programs. The generative AI receives individual patient data as input and generates optimal advice. The mental healthcare unit uses AI to perform text analysis and speech recognition to understand the user's mental health status. For example, the mental healthcare unit uses generative AI to analyze a user's statements and writings to detect signs of stress and anxiety. The generative AI receives a user's text data and voice data as input and evaluates their mental health status. The drug discovery unit uses AI to analyze massive amounts of compound data and discover new drug candidates. For example, the drug discovery unit uses generation AI to analyze existing drug data and identify potential effects on other diseases. The generation AI receives compound data as input and identifies new drug candidates. This enables the medical support system according to the embodiment to efficiently perform early detection and diagnosis of diseases, personalized health management, mental health assessment, and discovery of new drug candidates.

[0047] The diagnostic support unit can use generative AI to analyze medical image data and display the probability of detecting an abnormality in real time. For example, generative AI can analyze X-ray images and display the probability of an abnormality in real time. For example, in a lung X-ray image, the probability of the presence of a nodule or tumor can be quantified, allowing doctors to make an immediate judgment. Generative AI receives medical image data as input, identifies abnormalities, and displays their probability. This allows doctors to make an immediate judgment.

[0048] The diagnostic support unit can automatically search for similar past case data for abnormalities analyzed using the generation AI and provide it as reference information. For example, the generation AI analyzes abnormalities in an X-ray image and automatically searches for similar past case data. For example, if a lung nodule is detected, similar past case data is provided for doctors to refer to. The generation AI receives the abnormality as input, searches for similar past case data, and provides it as reference information. This makes it possible to provide information that doctors can use as a reference.

[0049] The diagnostic support unit can simultaneously analyze not only medical image data but also the patient's vital sign data to provide comprehensive diagnostic support. For example, the generation AI can simultaneously analyze X-ray images and the patient's vital sign data to provide comprehensive diagnostic support. For example, it can combine and analyze lung X-ray images with heart rate and blood pressure data. The generation AI receives medical image data and vital sign data as input and provides comprehensive diagnostic results. This makes it possible to provide comprehensive diagnostic support.

[0050] The diagnostic support unit promotes data sharing between different medical institutions, allowing the generating AI to integrate and analyze information from multiple data sources. For example, the generating AI integrates X-ray image data from different medical institutions and performs a comprehensive analysis. For example, it analyzes lung X-ray images from multiple hospitals to improve the accuracy of abnormality detection. The generating AI receives multiple data sources as input and performs an integrated analysis. This improves the accuracy of abnormality detection.

[0051] The personalized healthcare department can use generative AI to perform risk assessments that combine a patient's genetic information and environmental factors and suggest preventive measures. For example, generative AI can analyze a patient's genetic information and environmental factors to assess their risk of cardiovascular disease. For example, it can take into account family history and lifestyle habits and suggest exercise programs and dietary advice as preventive measures. Generative AI receives genetic information and environmental factors as input and generates risk assessments and preventive measures. This makes it possible to provide patients with personalized preventive measures.

[0052] The personalized healthcare unit collects a patient's lifestyle data in real time, and the generating AI can continuously update health management advice based on that data. For example, the generating AI collects a patient's lifestyle data in real time and continuously updates health management advice. For example, it monitors the amount of exercise and dietary content and provides appropriate advice. The generating AI receives lifestyle data as input and continuously updates its advice. This allows for continuous support in the patient's health management.

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

[0054] Step 1: The diagnostic support unit uses AI to analyze medical data and perform early detection and diagnosis of disease. For example, generative AI (text generation AI or multimodal generation AI) is used to analyze X-ray images and MRI images to detect abnormalities. The generative AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the results of the generative AI's analysis. Step 2: The personalized healthcare department uses AI to analyze each patient's genetic information, lifestyle, medical history, etc., and provides optimal treatment and health management advice. For example, generative AI can be used to analyze a patient's genetic information and lifestyle data to suggest optimal diet plans and exercise programs. Generative AI receives individual patient data as input and generates optimal advice. Step 3: The Mental Healthcare Department uses AI to analyze text and recognize voice to understand the user's mental health. For example, it uses generation AI to analyze the user's statements and writings to detect signs of stress or anxiety. The generation AI receives the user's text and voice data as input and evaluates their mental health. Step 4: The Drug Discovery Department uses AI to analyze vast amounts of compound data and discover new drug candidates. For example, generative AI can be used to analyze existing drug data and identify potential effects on other diseases. Generative AI takes compound data as input and identifies new drug candidates.

[0055] (Example 2) A medical support system according to an embodiment of the present invention utilizes AI technology to provide various support functions in the medical field. This system provides four main functions: AI diagnostic support, personalized healthcare, mental healthcare, and drug discovery. This enables the medical support system to efficiently perform early detection and diagnosis of diseases, individualized health management, mental health assessment, and discovery of new drug candidates.

[0056] A medical support system according to an embodiment includes a diagnostic support unit, a personalized healthcare unit, a mental healthcare unit, and a drug discovery unit. The diagnostic support unit uses AI to analyze medical data and perform early detection and diagnosis of diseases. For example, the diagnostic support unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze X-ray images and MRI images and detect abnormalities. The generation AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the analysis results of the generation AI. The personalized healthcare unit uses AI to analyze the genetic information, lifestyle, medical history, etc. of individual patients and provide optimal treatment and health management advice for each individual. For example, the personalized healthcare unit uses generation AI to analyze the patient's genetic information and lifestyle data and propose optimal diet plans and exercise programs. The generation AI receives individual patient data as input and generates optimal advice. The mental healthcare unit uses AI to perform text analysis and speech recognition to understand the user's mental health status. For example, the mental healthcare unit uses a generative AI to analyze a user's statements and writings to detect signs of stress and anxiety. The generative AI receives the user's text data and voice data as input and evaluates their mental health state. The drug discovery unit uses AI to analyze massive amounts of compound data and discover new drug candidates. For example, the drug discovery unit uses a generative AI to analyze existing drug data and identify drugs that may be effective against other diseases. The generative AI receives compound data as input and identifies new drug candidates. As a result, the medical support system according to the embodiment can efficiently perform early detection and diagnosis of diseases, personalized health management, mental health evaluation, and discovery of new drug candidates.

[0057] The diagnostic support unit uses the generating AI to analyze medical image data and display the probability of detecting an abnormality in real time. For example, the diagnostic support unit uses the generating AI to analyze X-ray images and display the probability of abnormalities in real time. For example, in a lung X-ray image, the probability of the presence of a nodule or tumor can be quantified, allowing doctors to make an immediate judgment. The generating AI receives medical image data as input, identifies abnormalities, and displays their probability. This allows doctors to make an immediate judgment.

[0058] The diagnostic support unit can automatically search for similar past case data for abnormalities analyzed using the generation AI and provide it as reference information. For example, the diagnostic support unit uses the generation AI to analyze abnormalities in an X-ray image and automatically search for similar past case data. For example, if a lung nodule is detected, similar past case data is provided for doctors to use as reference. The generation AI receives the abnormality as input, searches for similar past case data, and provides it as reference information. This makes it possible to provide information that doctors can use as reference.

[0059] The diagnostic support unit uses the emotion estimation function to monitor the doctor's stress level during diagnosis, and can provide additional support information if stress is high. For example, the diagnostic support unit uses the emotion estimation function to monitor the doctor's stress level when diagnosing an X-ray image. If stress is high, the generative AI provides additional reference information and diagnostic support. The emotion estimation function receives the doctor's facial expressions and voice data as input and evaluates the stress level. This can reduce the doctor's stress and improve the accuracy of diagnosis.

[0060] The diagnostic support unit can simultaneously analyze not only medical image data but also the patient's vital sign data to provide comprehensive diagnostic support. For example, the diagnostic support unit uses a generation AI to simultaneously analyze X-ray images and the patient's vital sign data to provide comprehensive diagnostic support. For example, it combines and analyzes lung X-ray images with heart rate and blood pressure data. The generation AI receives medical image data and vital sign data as input and provides comprehensive diagnostic results. This makes it possible to provide comprehensive diagnostic support.

[0061] The diagnostic support unit promotes data sharing between different medical institutions, allowing the generating AI to integrate and analyze information from multiple data sources. For example, the diagnostic support unit allows the generating AI to integrate X-ray image data from different medical institutions and perform a comprehensive analysis. For example, it analyzes lung X-ray images from multiple hospitals to improve the accuracy of abnormality detection. The generating AI receives multiple data sources as input and performs an integrated analysis. This improves the accuracy of abnormality detection.

[0062] The diagnostic support unit can use the emotion estimation function to provide counseling information to reduce the patient's anxiety and fear. For example, the diagnostic support unit uses the emotion estimation function to monitor the patient's anxiety and fear when undergoing an X-ray examination, and the generation AI provides counseling information. For example, it provides relaxation techniques and detailed explanations of the examination. The emotion estimation function receives the patient's facial expressions and voice data as input and evaluates their anxiety and fear. This helps to reduce the patient's anxiety and fear.

[0063] The personalized healthcare unit uses generative AI to perform risk assessments that combine a patient's genetic information and environmental factors and propose preventive measures. For example, the personalized healthcare unit uses generative AI to analyze a patient's genetic information and environmental factors to perform a risk assessment of cardiovascular disease. For example, it considers family history and lifestyle habits and proposes exercise programs and dietary advice as preventive measures. The generative AI receives genetic information and environmental factors as input and generates risk assessments and preventive measures. This makes it possible to provide individualized preventive measures to patients.

[0064] The personalized healthcare unit collects a patient's lifestyle data in real time, and the generating AI can continuously update health management advice based on that data. For example, the personalized healthcare unit allows the generating AI to collect a patient's lifestyle data in real time and continuously update health management advice. For example, it monitors the amount of exercise and dietary content and provides appropriate advice. The generating AI receives lifestyle data as input and continuously updates its advice. This allows for continuous support for the patient's health management.

[0065] The personalized healthcare unit can use the emotion estimation function to evaluate the patient's motivation level and provide an encouraging message if the patient's motivation is low. The personalized healthcare unit, for example, uses the emotion estimation function to evaluate the patient's motivation level and provide an encouraging message if the patient's motivation is low. For example, it can send a message encouraging the patient to continue an exercise program. The emotion estimation function receives the patient's facial expression and voice data as input and evaluates the motivation level. This makes it possible to maintain the patient's motivation and support health management.

[0066] The personalized healthcare unit can share advice with family and caregivers to support team-based health management. For example, the personalized healthcare unit will build a system to share personalized healthcare advice provided by the generative AI with family and caregivers. For example, a patient's health management plan can be shared with family members to provide joint support. The generative AI receives advice as input and shares it with family and caregivers. This can support team-based health management.

[0067] The personalized healthcare unit works in conjunction with different health management applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, the personalized healthcare unit works in conjunction with different health management applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, data from a fitness app and a diet management app is integrated and analyzed. The generation AI receives multiple data sources as input and generates integrated advice. This makes it possible to provide integrated advice from multiple data sources.

[0068] The personalized healthcare unit can use the emotion estimation function to suggest relaxation methods and stress relief methods according to the patient's emotional state. The personalized healthcare unit, for example, uses the emotion estimation function to suggest relaxation methods according to the patient's emotional state. For example, if stress is high, it can suggest deep breathing or meditation methods. The emotion estimation function receives the patient's facial expressions and voice data as input and evaluates the emotional state. This makes it possible to suggest relaxation methods and stress relief methods according to the patient's emotional state.

[0069] The mental healthcare department uses generative AI to analyze patterns of emotional fluctuations from a user's text data and voice data, making it possible to grasp long-term mental health trends. For example, the mental healthcare department uses generative AI to analyze a user's text data and grasp patterns of emotional fluctuations. For example, it analyzes diary entries and social media posts to grasp long-term mental health trends. The generative AI receives text data and voice data as input and analyzes patterns of emotional fluctuations. This makes it possible to grasp long-term mental health trends.

[0070] When assessing a user's mental health status, the mental health care unit allows the generation AI to compare it with past mental health history to detect abnormalities. For example, the mental health care unit allows the generation AI to compare the user's current mental health status with past history to detect abnormalities. For example, it detects abnormalities by comparing it with past stress levels or anxiety scores. The generation AI receives the mental health status and past history as input and detects abnormalities. This allows it to detect abnormalities by comparing it with past history.

[0071] The mental healthcare unit can use the emotion estimation function to provide relaxation music or meditation guides according to the user's emotional state. For example, the mental healthcare unit can use the emotion estimation function to provide relaxation music according to the user's emotional state. For example, if the user is under high stress, the mental healthcare unit can suggest relaxing music. The emotion estimation function receives the user's facial expression and voice data as input and evaluates the user's emotional state. This makes it possible to provide relaxation music or meditation guides according to the user's emotional state.

[0072] The mental healthcare unit can integrate mental healthcare data with the user's physical health data to provide comprehensive health management. For example, the generation AI can integrate mental healthcare data and physical health data to provide comprehensive health management. For example, it can combine and analyze stress level and heart rate data. The generation AI receives mental healthcare data and physical health data as input and provides comprehensive health management. This allows for comprehensive health management.

[0073] The mental healthcare unit works in conjunction with different mental healthcare applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, the mental healthcare unit works in conjunction with different mental healthcare applications, allowing the generation AI to provide integrated advice from multiple data sources. For example, data from a stress management app and a meditation app is integrated and analyzed. The generation AI receives multiple data sources as input and generates integrated advice. This makes it possible to provide integrated advice from multiple data sources.

[0074] The mental healthcare unit can automatically adjust the schedule of a counseling session according to the emotional state of the user using the emotion estimation function. The mental healthcare unit, for example, uses the emotion estimation function to automatically adjust the schedule of a counseling session according to the emotional state of the user. For example, if stress is high, it may suggest an earlier session. The emotion estimation function receives the user's facial expression and voice data as input and evaluates the emotional state. This makes it possible to automatically adjust the schedule of a counseling session according to the user's emotional state.

[0075] The drug discovery unit uses generative AI to simulate the effects of new drug candidates based on the results of compound data analysis, improving prediction accuracy. For example, the drug discovery unit uses generative AI to analyze compound data and simulate the effects of new drug candidates. For example, it simulates how a specific compound acts on cancer cells. The generative AI receives compound data as input and simulates the effects of drug candidates. This makes it possible to simulate the effects of new drug candidates and improve prediction accuracy.

[0076] The drug discovery unit can automatically search past clinical trial data for drug candidates analyzed by the generation AI and provide it as reference information. For example, the drug discovery unit has the generation AI analyze a new drug candidate and automatically search past clinical trial data. For example, it can provide information on the results of past clinical trials of a specific compound. The generation AI receives a drug candidate as input, searches past clinical trial data, and provides it as reference information. This makes it possible to provide past clinical trial data as reference information.

[0077] The drug discovery unit uses the emotion estimation function to monitor the researcher's stress level and can provide additional support information if stress is high. The drug discovery unit, for example, uses the emotion estimation function to monitor the researcher's stress level and, if stress is high, the generation AI provides additional support information. For example, it provides relaxation methods and advice on stress management. The emotion estimation function receives the researcher's facial expressions and voice data as input and evaluates their stress level. This makes it possible to monitor the researcher's stress level and provide additional support information.

[0078] The drug discovery department can support personalized drug development by simultaneously analyzing not only compound data but also the patient's genetic information and medical history data. For example, the drug discovery department's generative AI can simultaneously analyze compound data and the patient's genetic information to support personalized drug development. For example, it can identify drugs that are effective for patients with specific genetic backgrounds. The generative AI receives compound data, genetic information, and medical history data as input to support personalized drug development. This can support personalized drug development.

[0079] The drug discovery department promotes data sharing between different research institutions, allowing the generation AI to integrate and analyze information from multiple data sources. In the drug discovery department, for example, the generation AI integrates compound data from different research institutions and performs comprehensive analysis. For example, it analyzes data from multiple research institutions to identify new drug candidates. The generation AI receives multiple data sources as input and performs integrated analysis. This allows information from multiple data sources to be integrated and analyzed.

[0080] The drug discovery unit can use the emotion estimation function to provide encouraging messages and relaxation methods to maintain the motivation of researchers. The drug discovery unit, for example, uses the emotion estimation function to provide encouraging messages to maintain the motivation of researchers. For example, it sends positive feedback according to the progress of research. The emotion estimation function receives facial expressions and voice data of researchers as input and evaluates their motivation. This makes it possible to provide encouraging messages and relaxation methods to maintain the motivation of researchers.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The diagnostic support unit uses generative AI to analyze medical data for early detection and diagnosis of diseases. For example, the diagnostic support unit uses generative AI to analyze X-ray images and MRI images to detect abnormalities. The generative AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the generative AI's analysis results. The personalized healthcare unit uses AI to analyze each patient's genetic information, lifestyle, medical history, etc., and provide optimal treatment and health management advice for that individual. For example, the personalized healthcare unit uses generative AI to analyze a patient's genetic information and lifestyle data and propose optimal diet plans and exercise programs. The generative AI receives individual patient data as input and generates optimal advice. The mental healthcare unit uses AI to perform text analysis and speech recognition to understand the user's mental health status. For example, the mental healthcare unit uses generative AI to analyze a user's statements and writings to detect signs of stress and anxiety. The generative AI receives a user's text data and voice data as input and evaluates their mental health status. The drug discovery unit uses AI to analyze massive amounts of compound data and discover new drug candidates. For example, the drug discovery unit uses generation AI to analyze existing drug data and identify potential effects on other diseases. The generation AI receives compound data as input and identifies new drug candidates. This enables the medical support system according to the embodiment to efficiently perform early detection and diagnosis of diseases, personalized health management, mental health assessment, and discovery of new drug candidates.

[0083] The diagnostic support unit can use generative AI to analyze medical image data and display the probability of detecting an abnormality in real time. For example, generative AI can analyze X-ray images and display the probability of an abnormality in real time. For example, in a lung X-ray image, the probability of the presence of a nodule or tumor can be quantified, allowing doctors to make an immediate judgment. Generative AI receives medical image data as input, identifies abnormalities, and displays their probability. This allows doctors to make an immediate judgment.

[0084] The diagnostic support unit can automatically search for similar past case data for abnormalities analyzed using the generation AI and provide it as reference information. For example, the generation AI analyzes abnormalities in an X-ray image and automatically searches for similar past case data. For example, if a lung nodule is detected, similar past case data is provided for doctors to refer to. The generation AI receives the abnormality as input, searches for similar past case data, and provides it as reference information. This makes it possible to provide information that doctors can use as a reference.

[0085] The diagnostic support unit uses the emotion estimation function to monitor the doctor's stress level during diagnosis and can provide additional support information if stress is high. For example, the emotion estimation function can be used to monitor the doctor's stress level when diagnosing an X-ray image. If stress is high, the generative AI can provide additional reference information and diagnostic support. The emotion estimation function receives the doctor's facial expressions and voice data as input and evaluates their stress level. This can reduce the doctor's stress and improve the accuracy of diagnoses.

[0086] The diagnostic support unit can simultaneously analyze not only medical image data but also the patient's vital sign data to provide comprehensive diagnostic support. For example, the generation AI can simultaneously analyze X-ray images and the patient's vital sign data to provide comprehensive diagnostic support. For example, it can combine and analyze lung X-ray images with heart rate and blood pressure data. The generation AI receives medical image data and vital sign data as input and provides comprehensive diagnostic results. This makes it possible to provide comprehensive diagnostic support.

[0087] The diagnostic support unit promotes data sharing between different medical institutions, allowing the generating AI to integrate and analyze information from multiple data sources. For example, the generating AI integrates X-ray image data from different medical institutions and performs a comprehensive analysis. For example, it analyzes lung X-ray images from multiple hospitals to improve the accuracy of abnormality detection. The generating AI receives multiple data sources as input and performs an integrated analysis. This improves the accuracy of abnormality detection.

[0088] The diagnostic support unit can use the emotion estimation function to provide counseling information to reduce the patient's anxiety and fear. For example, the emotion estimation function can be used to monitor the patient's anxiety and fear when undergoing an X-ray examination, and the generation AI can provide counseling information, such as relaxation techniques and detailed explanations of the examination. The emotion estimation function receives the patient's facial expressions and voice data as input and evaluates their anxiety and fear. This can reduce the patient's anxiety and fear.

[0089] The personalized healthcare department can use generative AI to perform risk assessments that combine a patient's genetic information and environmental factors and suggest preventive measures. For example, generative AI can analyze a patient's genetic information and environmental factors to assess their risk of cardiovascular disease. For example, it can take into account family history and lifestyle habits and suggest exercise programs and dietary advice as preventive measures. Generative AI receives genetic information and environmental factors as input and generates risk assessments and preventive measures. This makes it possible to provide patients with personalized preventive measures.

[0090] The personalized healthcare unit collects a patient's lifestyle data in real time, and the generating AI can continuously update health management advice based on that data. For example, the generating AI collects a patient's lifestyle data in real time and continuously updates health management advice. For example, it monitors the amount of exercise and dietary content and provides appropriate advice. The generating AI receives lifestyle data as input and continuously updates its advice. This allows for continuous support in the patient's health management.

[0091] The personalized healthcare unit can use the emotion estimation function to assess a patient's motivation level and provide an encouraging message if their motivation is low. For example, the emotion estimation function can be used to assess a patient's motivation level and provide an encouraging message if their motivation is low. For example, it can send a message encouraging them to continue their exercise program. The emotion estimation function receives the patient's facial expressions and voice data as input and evaluates their motivation level. This can help maintain the patient's motivation and support their health management.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The diagnostic support unit uses AI to analyze medical data and perform early detection and diagnosis of disease. For example, generative AI (text generation AI or multimodal generation AI) is used to analyze X-ray images and MRI images to detect abnormalities. The generative AI receives medical image data as input and identifies abnormalities. The diagnostic support unit can also display the probability of abnormality detection in real time based on the results of the generative AI's analysis. Step 2: The personalized healthcare department uses AI to analyze each patient's genetic information, lifestyle, medical history, etc., and provides optimal treatment and health management advice. For example, generative AI can be used to analyze a patient's genetic information and lifestyle data to suggest optimal diet plans and exercise programs. Generative AI receives individual patient data as input and generates optimal advice. Step 3: The Mental Healthcare Department uses AI to analyze text and recognize voice to understand the user's mental health. For example, it uses generation AI to analyze the user's statements and writings to detect signs of stress or anxiety. The generation AI receives the user's text and voice data as input and evaluates their mental health. Step 4: The Drug Discovery Department uses AI to analyze vast amounts of compound data and discover new drug candidates. For example, generative AI can be used to analyze existing drug data and identify potential effects on other diseases. Generative AI takes compound data as input and identifies new drug candidates.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 diagnostic support department; Personalized Healthcare Department and Mental Health Care Department and a drug discovery unit; A system characterized by:

2. The diagnosis support unit Analyzes not only medical image data but also vital sign data simultaneously to provide comprehensive diagnostic support 2. The system of claim 1.

3. The personalized healthcare department Generative AI is used to assess risk by combining a patient's genetic information and environmental factors, and to suggest preventative measures.

2. The system of claim 1.

4. The mental health care department: Using generative AI to analyze emotional patterns from users' text and voice data to understand long-term mental health trends 2. The system of claim 1.

5. The drug discovery unit comprises: Using generative AI to simulate the effects of new drug candidates based on the analysis of compound data, improving prediction accuracy 2. The system of claim 1.

6. The diagnosis support unit Emotion estimation function is used to monitor the stress level of doctors during diagnosis, and if the stress level is high, additional support information is provided.

2. The system of claim 1.

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