system

Abstract

The system according to this embodiment aims to propose appropriate diagnoses and countermeasures when a physician examines a patient outside their area of ​​expertise. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit uploads the patient's medical history and medical images. The analysis unit analyzes the information uploaded by the reception unit. The search unit searches a medical knowledge database and a medical image database based on the information analyzed by the analysis unit. The proposal unit proposes a suspected disease name and countermeasures based on the information obtained by the search unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a doctor to find an appropriate diagnosis and countermeasure when examining a patient outside their specialty.

[0005] The system according to the embodiment aims to propose an appropriate diagnosis and countermeasure when a doctor examines a patient outside their specialty.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit uploads the patient's medical history and medical images. The analysis unit analyzes the information uploaded by the reception unit. The search unit searches a medical knowledge database and a medical image database based on the information analyzed by the analysis unit. The proposal unit proposes a suspected disease name and countermeasures based on the information obtained by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose appropriate diagnoses and countermeasures when a physician examines a patient outside of their specialty. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is a system for solving the problems that doctors face when examining patients outside their specialty while on call. This medical support system allows a doctor to upload a patient's medical history and medical images such as X-rays, and a generating AI then suggests a suspected diagnosis and countermeasures. For example, a doctor uploads a patient's medical history and medical images to this system. For example, if a patient falls and severely injures their right elbow, the doctor uploads the medical history and X-ray images. This information is input into the generating AI. Next, the generating AI analyzes the uploaded information. The generating AI searches a medical knowledge database created based on medical books and papers, and a medical image database provided by specialists, to identify similar cases. For example, based on an X-ray image of the right elbow, it identifies the possibility of a radial head fracture. Based on the identified case, the generating AI suggests a suspected diagnosis and countermeasures. For example, if a diagnosis of a right radial head fracture is made, it suggests treatment methods such as splinting or open reduction and internal fixation. This system allows doctors to perform rapid and accurate diagnoses and treatments even in areas outside their specialty, maximizing the patient's benefit. Furthermore, reducing oversights also mitigates the risk of medical malpractice lawsuits. Moreover, this system is expected to improve physicians' clinical skills. By referring to the suspected diagnoses and countermeasures provided by the generating AI, physicians can enhance their diagnostic abilities. For example, by implementing the treatment methods suggested by the generating AI and receiving feedback on the results, physicians can accumulate their own knowledge and experience. Thus, this system is a groundbreaking solution that addresses the challenges physicians face when examining patients outside their specialty and ensures accurate diagnoses. By utilizing the generating AI, physicians can provide rapid and accurate diagnoses and treatments, maximizing patient benefits. In this way, the medical support system solves the challenges physicians face when examining patients outside their specialty and ensures accurate diagnoses.

[0029] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit uploads the patient's medical history and medical images. The patient's medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. Medical images include, but are not limited to, X-ray images, MRI images, and CT scan images. The reception unit can, for example, obtain and upload diagnoses and treatment history received by the patient from the electronic medical record. The reception unit can also scan medical images brought by the patient using a scanner and upload them as digital data. For example, the reception unit scans an X-ray image brought by the patient using a high-resolution scanner and saves it as digital data. The analysis unit analyzes the information uploaded by the reception unit using a generation AI. The analysis is performed based on, for example, an image analysis algorithm or text analysis technology, but is not limited to such examples. For example, the analysis unit uses a generation AI to analyze the uploaded X-ray image and detect the presence or absence of a fracture. Furthermore, the analysis unit can use generative AI to analyze uploaded diagnostic results and treatment history to understand the patient's medical history. For example, the analysis unit can use generative AI to extract and analyze the patient's medical history from diagnostic results and treatment history. The search unit uses generative AI to search the medical knowledge database and medical image database based on the information analyzed by the analysis unit. The search is performed based on, for example, the method of generating search queries and the search algorithm, but is not limited to such examples. For example, the search unit uses generative AI to search the medical knowledge database based on analyzed X-ray images to identify similar cases. The search unit can also use generative AI to search the medical image database based on analyzed diagnostic results and treatment history. For example, the search unit uses generative AI to search the medical image database based on diagnostic results and treatment history to identify relevant medical images. The proposal unit uses generative AI to propose suspected diagnoses and countermeasures based on the information obtained by the search unit. The proposal is made based on, for example, the priority of the proposals and the evaluation criteria for the proposed content, but is not limited to such examples. For example, the proposal department uses generative AI to suggest suspected diagnoses and countermeasures based on the searched cases.Furthermore, the suggestion unit can also use a generation AI to propose treatment methods based on the retrieved medical images. For example, the suggestion unit can use the generation AI to propose treatment methods such as splinting or open reduction and internal fixation based on the retrieved medical images. This allows the medical support system according to the embodiment to solve the problems that doctors face when examining patients outside their specialty and to ensure thorough examinations without overlooking anything. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, for example, or without using a generation AI. For example, the suggestion unit can input the information obtained by the search unit into the generation AI and have the generation AI propose suspected diagnoses and countermeasures.

[0030] The reception desk uploads the patient's medical history and medical images. The patient's medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. Specifically, past diagnoses and treatment history are automatically retrieved from the electronic medical record system. This allows the reception desk to retrieve and upload the patient's diagnoses and treatment history from the electronic medical record. Furthermore, medical images brought in by the patient can be scanned and uploaded as digital data. For example, X-ray images brought in by the patient can be scanned with a high-resolution scanner and saved as digital data. Similarly, MRI and CT scan images are also scanned and saved as digital data. This allows the reception desk to centrally manage patient medical history and medical images, making them accessible to the analysis and search departments. The reception desk can also encrypt data and control access to protect patient privacy. For example, uploaded data can be encrypted and stored, and only authenticated users can access it. This allows the reception desk to securely manage patient data and improve the overall reliability of the system.

[0031] The analysis unit uses generative AI to analyze information uploaded by the reception unit. Analysis is performed based on, for example, image analysis algorithms and text analysis technologies, but is not limited to these examples. Specifically, the generative AI analyzes uploaded X-ray images to detect the presence or absence of fractures. Image analysis algorithms detect changes in bone shape and density, identifying abnormal areas. The generative AI can also analyze uploaded diagnostic results and treatment history to understand the patient's medical history. For example, the generative AI uses natural language processing technology to extract and analyze the patient's medical history from diagnostic results and treatment history. This allows the analysis unit to quickly and accurately analyze collected data and understand the patient's condition. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the progression pattern of a specific disease based on past diagnostic results and treatment history, and formulate future treatment plans. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The search unit uses generative AI to search medical knowledge databases and medical image databases based on information analyzed by the analysis unit. The search is performed based on, for example, the method of generating search queries and the search algorithm, but is not limited to these examples. Specifically, the generative AI searches the medical knowledge database based on analyzed X-ray images to identify similar cases. For example, the generative AI extracts image features and generates queries to search for similar cases. The generative AI can also search the medical image database based on analyzed diagnostic results and treatment history. For example, the generative AI uses natural language processing techniques to extract relevant keywords from diagnostic results and treatment history and searches the medical image database. This allows the search unit to quickly identify relevant medical images and case information and provide it to the suggestion unit. Furthermore, the search unit can introduce a feedback loop to improve the accuracy of search results. For example, by collecting physician feedback on search results and using it as training data for the generative AI, the accuracy of the search algorithm can be continuously improved. This allows the search unit to always provide highly accurate search results based on the latest information, supporting physicians' diagnoses.

[0033] The suggestion unit uses generative AI to propose suspected diagnoses and countermeasures based on information obtained by the search unit. Suggestions are made based on, for example, priority and evaluation criteria for the suggestions, but are not limited to these examples. Specifically, the generative AI proposes suspected diagnoses and countermeasures based on the searched cases. For example, the generative AI identifies the most likely diagnose and proposes a countermeasure based on information obtained from similar cases. The generative AI can also propose treatment methods based on the searched medical images. For example, based on image analysis results, the generative AI proposes treatment methods such as splinting or open reduction and internal fixation. This allows the suggestion unit to address the concerns of physicians when examining patients outside their specialty, enabling thorough examinations without overlooking diagnoses. Furthermore, the suggestion unit can introduce a feedback loop to improve the accuracy of its suggestions. For example, by collecting physician feedback on the suggestions and using it as training data for the generative AI, the accuracy of the suggestion algorithm can be continuously improved. The suggestion unit also prioritizes and presents multiple suggestions to facilitate the selection of the optimal treatment method for physicians. This allows the proposal department to strongly support physicians' diagnoses and maximize the effectiveness of patient treatment.

[0034] The suggestion unit can propose suspected diagnoses and countermeasures using a generative AI. For example, the suggestion unit can use the generative AI to identify suspected diagnoses based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow. The suggestion unit can also use the generative AI to propose countermeasures based on the identified suspected diagnose. For example, if a radial head fracture is diagnosed, the suggestion unit can use the generative AI to propose treatment methods such as splinting or open reduction and internal fixation. The suggestion unit can also use the generative AI to evaluate the effectiveness of the proposed countermeasures. For example, the suggestion unit can use the generative AI to simulate the effectiveness of the proposed treatment and propose the optimal treatment. This improves the accuracy of suggesting suspected diagnoses and countermeasures by using the generative AI. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or not. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI propose suspected diagnoses and countermeasures.

[0035] The search unit can search medical knowledge databases and medical image databases using generative AI. For example, the search unit can use generative AI to search the medical knowledge database based on a patient's medical history and medical images. For example, the search unit can use generative AI to search for medical knowledge about radial head fractures based on an X-ray image of the right elbow. The search unit can also use generative AI to search the medical image database and identify similar cases. For example, the search unit can use generative AI to identify similar fracture cases based on an X-ray image of the right elbow. The search unit can also use generative AI to evaluate search results and provide the most relevant information. For example, the search unit can use generative AI to evaluate the reliability of search results and provide the most reliable information. This improves the accuracy of the search by using generative AI. Some or all of the above-described processes in the search unit may be performed using generative AI, or they may not. For example, the search unit can input a patient's medical history and medical images into the generative AI and have the generative AI perform searches of the medical knowledge database and medical image database.

[0036] The analysis unit can analyze information uploaded by the generative AI. For example, the analysis unit can use the generative AI to analyze a patient's medical history and medical images. For example, the analysis unit can use the generative AI to analyze an X-ray image of the right elbow and detect the presence or absence of a fracture. The analysis unit can also use the generative AI to analyze a patient's diagnosis and treatment history to understand their medical history. For example, the analysis unit can use the generative AI to extract and analyze a patient's medical history from diagnosis and treatment history. The analysis unit can also use the generative AI to evaluate the analysis results and provide the most optimal information. For example, the analysis unit can use the generative AI to evaluate the reliability of the analysis results and provide the most reliable information. This improves the accuracy of the analysis by using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may be performed without the generative AI. For example, the analysis unit can input the patient's medical history and medical images into the generative AI and have the generative AI perform the analysis.

[0037] The reception desk can upload the patient's medical history and medical images. For example, the reception desk can retrieve and upload the patient's diagnosis and treatment history from the electronic medical record. For example, the reception desk can scan medical images brought by the patient and upload them as digital data. For example, the reception desk can scan X-ray images brought by the patient with a high-resolution scanner and save them as digital data. This allows for the efficient uploading of the patient's medical history and medical images. Some or all of the above processing at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's medical history and medical images into the AI ​​and have the AI ​​perform the upload.

[0038] The suggestion unit enables physicians to improve their clinical skills based on information suggested by the generative AI. For example, the suggestion unit uses the generative AI to suggest suspected diagnoses and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit uses the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggests treatment methods such as splinting or open reduction and internal fixation. Physicians can improve their diagnostic skills by implementing the treatment methods suggested by the generative AI and receiving feedback on the results. For example, the suggestion unit uses the generative AI to evaluate the effectiveness of the suggested treatment methods and provides feedback to the physician. This improves the physician's clinical skills. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or without using the generative AI. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI execute suggestions for improving clinical skills.

[0039] The suggestion unit can mitigate the risk of medical malpractice lawsuits based on information suggested by the generative AI. For example, the suggestion unit can use the generative AI to suggest a suspected diagnosis and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggest treatment methods such as splinting or open reduction and internal fixation. The physician can mitigate the risk of medical malpractice lawsuits by implementing the treatment method suggested by the generative AI and providing feedback on the results. For example, the suggestion unit can use the generative AI to evaluate the effectiveness of the suggested treatment method and provide feedback to the physician. This reduces the risk of medical malpractice lawsuits. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or not. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI execute suggestions to mitigate the risk of medical malpractice lawsuits.

[0040] The suggestion unit can provide feedback on the information suggested by the generative AI. For example, the suggestion unit can use the generative AI to suggest a suspected diagnosis and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggest treatment methods such as splinting or open reduction and internal fixation. The physician can improve the accuracy of the system by implementing the treatment method suggested by the generative AI and providing feedback on the results. For example, the suggestion unit can use the generative AI to evaluate the effectiveness of the suggested treatment method and provide feedback. This improves the accuracy of the system as the suggested information is fed back. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or without using the generative AI. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI perform the feedback.

[0041] The reception desk can analyze the patient's past medical history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods the patient has used in the past. For example, the reception desk may suggest the optimal method based on the upload methods the patient has used in the past. The reception desk can also recommend uploading in a specific format based on the patient's medical history. For example, the reception desk may recommend uploading in a specific format such as PDF or image format based on the patient's medical history. The reception desk can also simplify the upload process by automatically extracting necessary information from the patient's past medical history. For example, the reception desk extracts necessary information from the patient's past medical history to simplify the upload process. This streamlines the upload process by selecting the optimal upload method based on the patient's past medical history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's past medical history into an AI and have the AI ​​select the optimal upload method.

[0042] The reception desk can filter the uploaded medical history and medical images based on the patient's current health status and medical history. For example, the reception desk can upload only the necessary information based on the patient's current health status. For example, the reception desk can select and upload only the necessary information based on the patient's current health status. The reception desk can also prioritize uploading relevant information based on the patient's medical history. For example, the reception desk can prioritize uploading relevant information based on the patient's medical history. The reception desk can also prioritize uploading information of high urgency if the patient's health status is deteriorating. For example, the reception desk can prioritize uploading information of high urgency if the patient's health status is deteriorating. This enables efficient information management by uploading only the necessary information based on the patient's health status and medical history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's health status and medical history into the AI ​​and have the AI ​​perform the filtering.

[0043] The reception desk can prioritize uploading highly relevant information based on the patient's geographical location when uploading medical history and medical images. For example, the reception desk can prioritize uploading information related to region-specific diseases based on the patient's place of residence. The reception desk can also prioritize uploading medical information for the patient's travel destination if the patient is traveling. The reception desk can also upload information about the nearest medical institution based on the patient's geographical location. This enables efficient information management by prioritizing the uploading of highly relevant information based on the patient's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's geographical location into the AI ​​and have the AI ​​perform the uploading of highly relevant information.

[0044] The reception desk can analyze a patient's social media activity and upload relevant information when uploading medical history and medical images. For example, the reception desk can extract and upload health-related information from the patient's social media posts. The reception desk can also infer the patient's recent health status from their social media activity and upload relevant information. The reception desk can also analyze health-related questions and comments on the patient's social media and upload relevant information. This enables efficient information management by uploading relevant information based on the patient's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the patient's social media activity into the AI ​​and have the AI ​​perform the uploading of relevant information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical history and medical images during the analysis. For example, the analysis unit can perform a detailed analysis on important medical history and medical images. The analysis unit can also perform a simplified analysis on information of lower importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the importance of the medical history and medical images into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of medical history and medical images during analysis. For example, the analysis unit can apply a fracture detection algorithm to an X-ray image of a fracture. The analysis unit can also apply a heart rate abnormality detection algorithm to electrocardiogram data. The analysis unit can also apply an abnormal value detection algorithm to blood test results. By applying different analysis algorithms depending on the category of medical history and medical images, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of medical history and medical images into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the submission date of medical history and medical images during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted medical history and medical images. The analysis unit can also lower the priority of analysis for older information. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the submission dates of medical history and medical images into a generating AI and have the generating AI determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relationship between medical history and medical images during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also lower the priority of analysis for less relevant information. The analysis unit can also adjust the analysis schedule based on relevance. This allows for efficient analysis by adjusting the order of analysis based on the relationship between medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relationship between medical history and medical images into the generative AI and have the generative AI adjust the order of analysis.

[0049] The search unit can improve the accuracy of searches by considering the relationship between medical history and medical images during the search process. For example, the search unit can provide highly accurate search results based on the relationship between medical history and medical images. The search unit can also analyze the relationship between medical history and medical images and provide optimal search results. The search unit can also determine the priority of search results based on the relationship between medical history and medical images. This improves the accuracy of searches by considering the relationship between medical history and medical images. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the relationship between medical history and medical images into a generative AI and have the generative AI perform the search accuracy improvement.

[0050] The search unit can perform searches while considering the medical history and attribute information of the submitter of medical images. For example, the search unit can provide highly relevant search results based on the submitter's area of ​​expertise. The search unit can also provide highly reliable search results based on the submitter's years of experience. The search unit can also provide optimal search results based on the submitter's attribute information. By considering the submitter's attribute information, highly relevant search results can be provided. Some or all of the above processing in the search unit may be performed using, for example, a generating AI, or without a generating AI. For example, the search unit can input the submitter's attribute information into a generating AI and have the generating AI perform the search.

[0051] The search unit can perform searches while considering the geographical distribution of medical history and medical images. For example, the search unit can prioritize searching for geographically close information. The search unit can also search for highly relevant information based on geographical distribution. The search unit can also provide optimal search results by considering geographical distribution. This allows for the provision of highly relevant search results by considering geographical distribution. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the geographical distribution of medical history and medical images into a generative AI and have the generative AI perform the search.

[0052] The search unit can improve the accuracy of its search by referring to relevant literature on medical history and medical images during the search. For example, the search unit can provide highly accurate search results based on relevant literature. The search unit can also provide optimal search results by referring to relevant literature. The search unit can also determine the priority of search results based on relevant literature. This improves the accuracy of the search by referring to relevant literature. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant literature on medical history and medical images into a generative AI and have the generative AI perform the search accuracy improvement.

[0053] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the medical history and medical images. For example, the suggestion unit can provide detailed suggestions for important medical history and medical images. The suggestion unit can also provide simplified suggestions for less important information. The suggestion unit can also prioritize suggestions according to their importance. This allows for efficient suggestions by adjusting the level of detail of suggestions based on the importance of the medical history and medical images. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the importance of the medical history and medical images into the generative AI and have the generative AI adjust the level of detail of the suggestions.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of medical history and medical images during the suggestion process. For example, the suggestion unit can apply a fracture treatment algorithm to an X-ray image of a fracture. The suggestion unit can also apply a heart rate abnormality treatment algorithm to electrocardiogram data. The suggestion unit can also apply an abnormal value treatment algorithm to blood test results. By applying different suggestion algorithms depending on the category of medical history and medical images, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the categories of medical history and medical images into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0055] The proposal unit can determine the priority of proposals based on the submission timing of medical history and medical images. For example, the proposal unit may prioritize recently submitted medical history and medical images. The proposal unit can also lower the priority of older information. The proposal unit can also adjust the proposal schedule based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of medical history and medical images. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the submission timing of medical history and medical images into a generative AI and have the generative AI determine the priority of proposals.

[0056] The suggestion unit can adjust the order of suggestions based on the relationship between the medical history and medical images. For example, the suggestion unit can prioritize suggesting information that is highly relevant. The suggestion unit can also lower the priority of suggestions for information that is less relevant. The suggestion unit can also adjust the schedule of suggestions based on their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relationship between the medical history and medical images. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the relationship between the medical history and medical images into a generative AI and have the generative AI adjust the order of suggestions.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] Medical support systems can further improve diagnostic accuracy by acquiring patient lifestyle data. For example, by collecting data on patients' diet, exercise, and sleep patterns and incorporating this information into analysis, a more comprehensive diagnosis becomes possible. Furthermore, based on lifestyle data, preventative measures and lifestyle improvements can be suggested. For instance, information useful for maintaining patient health, such as recommendations for dietary improvements and exercise, can be provided. Additionally, lifestyle data can be used to predict the risk of specific diseases, promoting early detection and treatment. This leads to more effective patient health management.

[0059] Medical support systems can further acquire patients' genetic information and improve the accuracy of diagnoses. For example, they can analyze a patient's genetic data to identify genetic risks. They can also predict the effectiveness of specific treatments based on genetic information and suggest the optimal treatment plan. Furthermore, they can use genetic information to predict future health risks and suggest preventive measures. This allows for the provision of personalized medical care to each individual patient.

[0060] The medical support system can further consider the patient's socioeconomic background when proposing diagnoses and treatments. For example, it can suggest cost-effective treatments based on the patient's income and insurance status. It can also suggest feasible preventive measures and lifestyle improvements based on the patient's living environment. Furthermore, if social support is needed, it can refer the patient to appropriate support organizations. This ensures that optimal medical care is provided according to the individual circumstances of each patient.

[0061] Medical support systems can further analyze patients' past treatment outcomes to improve the accuracy of diagnosis and treatment. For example, they can suggest the optimal treatment method for similar cases based on past treatment results. They can also use past treatment results as feedback to improve the system's learning. Furthermore, they can use past treatment results to predict the effectiveness of treatment and explain it to the patient. This allows for the provision of highly accurate medical care that utilizes past data.

[0062] The medical support system can further monitor patients' health data in real time and issue alerts if abnormalities are detected. For example, it can continuously monitor the patient's heart rate, blood pressure, and blood sugar levels, and notify doctors if abnormal values ​​are detected. It can also suggest prompt countermeasures when abnormalities are detected. Furthermore, it can issue alerts to the patient themselves to encourage early intervention. This enables real-time health management.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk uploads the patient's medical history and medical images. The patient's medical history includes past diagnoses, treatment history, and allergy information. Medical images include X-ray images, MRI images, and CT scan images. The reception desk retrieves the patient's diagnoses and treatment history from the electronic medical record and uploads them. It is also possible to scan medical images brought in by the patient and upload them as digital data. Step 2: The analysis unit uses a generation AI to analyze the information uploaded by the reception unit. The analysis is performed based on image analysis algorithms and text analysis technologies. For example, it analyzes uploaded X-ray images to detect the presence or absence of fractures. It also analyzes diagnostic results and treatment history to understand the patient's medical history. Step 3: The search unit uses generation AI to search the medical knowledge database and medical image database based on the information analyzed by the analysis unit. The search is performed based on the method of generating the search query and the search algorithm. For example, based on the analyzed X-ray images, the medical knowledge database is searched to identify similar cases. Also, based on the diagnosis results and treatment history, the medical image database is searched to identify relevant medical images. Step 4: The suggestion unit uses generational AI to propose suspected diagnoses and countermeasures based on the information obtained by the search unit. The suggestions are made based on the priority of the suggestions and the evaluation criteria for the suggested content. For example, it can propose suspected diagnoses and countermeasures based on the cases that have been searched. It can also propose treatment methods based on the medical images that have been searched.

[0065] (Example of form 2) The medical support system according to an embodiment of the present invention is a system for solving the problems that doctors face when examining patients outside their specialty while on call. This medical support system allows a doctor to upload a patient's medical history and medical images such as X-rays, and a generating AI then suggests a suspected diagnosis and countermeasures. For example, a doctor uploads a patient's medical history and medical images to this system. For example, if a patient falls and severely injures their right elbow, the doctor uploads the medical history and X-ray images. This information is input into the generating AI. Next, the generating AI analyzes the uploaded information. The generating AI searches a medical knowledge database created based on medical books and papers, and a medical image database provided by specialists, to identify similar cases. For example, based on an X-ray image of the right elbow, it identifies the possibility of a radial head fracture. Based on the identified case, the generating AI suggests a suspected diagnosis and countermeasures. For example, if a diagnosis of a right radial head fracture is made, it suggests treatment methods such as splinting or open reduction and internal fixation. This system allows doctors to perform rapid and accurate diagnoses and treatments even in areas outside their specialty, maximizing the patient's benefit. Furthermore, reducing oversights also mitigates the risk of medical malpractice lawsuits. Moreover, this system is expected to improve physicians' clinical skills. By referring to the suspected diagnoses and countermeasures provided by the generating AI, physicians can enhance their diagnostic abilities. For example, by implementing the treatment methods suggested by the generating AI and receiving feedback on the results, physicians can accumulate their own knowledge and experience. Thus, this system is a groundbreaking solution that addresses the challenges physicians face when examining patients outside their specialty and ensures accurate diagnoses. By utilizing the generating AI, physicians can provide rapid and accurate diagnoses and treatments, maximizing patient benefits. In this way, the medical support system solves the challenges physicians face when examining patients outside their specialty and ensures accurate diagnoses.

[0066] The medical support system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit uploads the patient's medical history and medical images. The patient's medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. Medical images include, but are not limited to, X-ray images, MRI images, and CT scan images. The reception unit can, for example, obtain and upload diagnoses and treatment history received by the patient from the electronic medical record. The reception unit can also scan medical images brought by the patient using a scanner and upload them as digital data. For example, the reception unit scans an X-ray image brought by the patient using a high-resolution scanner and saves it as digital data. The analysis unit analyzes the information uploaded by the reception unit using a generation AI. The analysis is performed based on, for example, an image analysis algorithm or text analysis technology, but is not limited to such examples. For example, the analysis unit uses a generation AI to analyze the uploaded X-ray image and detect the presence or absence of a fracture. Furthermore, the analysis unit can use generative AI to analyze uploaded diagnostic results and treatment history to understand the patient's medical history. For example, the analysis unit can use generative AI to extract and analyze the patient's medical history from diagnostic results and treatment history. The search unit uses generative AI to search the medical knowledge database and medical image database based on the information analyzed by the analysis unit. The search is performed based on, for example, the method of generating search queries and the search algorithm, but is not limited to such examples. For example, the search unit uses generative AI to search the medical knowledge database based on analyzed X-ray images to identify similar cases. The search unit can also use generative AI to search the medical image database based on analyzed diagnostic results and treatment history. For example, the search unit uses generative AI to search the medical image database based on diagnostic results and treatment history to identify relevant medical images. The proposal unit uses generative AI to propose suspected diagnoses and countermeasures based on the information obtained by the search unit. The proposal is made based on, for example, the priority of the proposals and the evaluation criteria for the proposed content, but is not limited to such examples. For example, the proposal department uses generative AI to suggest suspected diagnoses and countermeasures based on the searched cases.Furthermore, the suggestion unit can also use a generation AI to propose treatment methods based on the retrieved medical images. For example, the suggestion unit can use the generation AI to propose treatment methods such as splinting or open reduction and internal fixation based on the retrieved medical images. This allows the medical support system according to the embodiment to solve the problems that doctors face when examining patients outside their specialty and to ensure thorough examinations without overlooking anything. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, for example, or without using a generation AI. For example, the suggestion unit can input the information obtained by the search unit into the generation AI and have the generation AI propose suspected diagnoses and countermeasures.

[0067] The reception desk uploads the patient's medical history and medical images. The patient's medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. Specifically, past diagnoses and treatment history are automatically retrieved from the electronic medical record system. This allows the reception desk to retrieve and upload the patient's diagnoses and treatment history from the electronic medical record. Furthermore, medical images brought in by the patient can be scanned and uploaded as digital data. For example, X-ray images brought in by the patient can be scanned with a high-resolution scanner and saved as digital data. Similarly, MRI and CT scan images are also scanned and saved as digital data. This allows the reception desk to centrally manage patient medical history and medical images, making them accessible to the analysis and search departments. The reception desk can also encrypt data and control access to protect patient privacy. For example, uploaded data can be encrypted and stored, and only authenticated users can access it. This allows the reception desk to securely manage patient data and improve the overall reliability of the system.

[0068] The analysis unit uses generative AI to analyze information uploaded by the reception unit. Analysis is performed based on, for example, image analysis algorithms and text analysis technologies, but is not limited to these examples. Specifically, the generative AI analyzes uploaded X-ray images to detect the presence or absence of fractures. Image analysis algorithms detect changes in bone shape and density, identifying abnormal areas. The generative AI can also analyze uploaded diagnostic results and treatment history to understand the patient's medical history. For example, the generative AI uses natural language processing technology to extract and analyze the patient's medical history from diagnostic results and treatment history. This allows the analysis unit to quickly and accurately analyze collected data and understand the patient's condition. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the progression pattern of a specific disease based on past diagnostic results and treatment history, and formulate future treatment plans. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The search unit uses generative AI to search medical knowledge databases and medical image databases based on information analyzed by the analysis unit. The search is performed based on, for example, the method of generating search queries and the search algorithm, but is not limited to these examples. Specifically, the generative AI searches the medical knowledge database based on analyzed X-ray images to identify similar cases. For example, the generative AI extracts image features and generates queries to search for similar cases. The generative AI can also search the medical image database based on analyzed diagnostic results and treatment history. For example, the generative AI uses natural language processing techniques to extract relevant keywords from diagnostic results and treatment history and searches the medical image database. This allows the search unit to quickly identify relevant medical images and case information and provide it to the suggestion unit. Furthermore, the search unit can introduce a feedback loop to improve the accuracy of search results. For example, by collecting physician feedback on search results and using it as training data for the generative AI, the accuracy of the search algorithm can be continuously improved. This allows the search unit to always provide highly accurate search results based on the latest information, supporting physicians' diagnoses.

[0070] The suggestion unit uses generative AI to propose suspected diagnoses and countermeasures based on information obtained by the search unit. Suggestions are made based on, for example, priority and evaluation criteria for the suggestions, but are not limited to these examples. Specifically, the generative AI proposes suspected diagnoses and countermeasures based on the searched cases. For example, the generative AI identifies the most likely diagnose and proposes a countermeasure based on information obtained from similar cases. The generative AI can also propose treatment methods based on the searched medical images. For example, based on image analysis results, the generative AI proposes treatment methods such as splinting or open reduction and internal fixation. This allows the suggestion unit to address the concerns of physicians when examining patients outside their specialty, enabling thorough examinations without overlooking diagnoses. Furthermore, the suggestion unit can introduce a feedback loop to improve the accuracy of its suggestions. For example, by collecting physician feedback on the suggestions and using it as training data for the generative AI, the accuracy of the suggestion algorithm can be continuously improved. The suggestion unit also prioritizes and presents multiple suggestions to facilitate the selection of the optimal treatment method for physicians. This allows the proposal department to strongly support physicians' diagnoses and maximize the effectiveness of patient treatment.

[0071] The suggestion unit can propose suspected diagnoses and countermeasures using a generative AI. For example, the suggestion unit can use the generative AI to identify suspected diagnoses based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow. The suggestion unit can also use the generative AI to propose countermeasures based on the identified suspected diagnose. For example, if a radial head fracture is diagnosed, the suggestion unit can use the generative AI to propose treatment methods such as splinting or open reduction and internal fixation. The suggestion unit can also use the generative AI to evaluate the effectiveness of the proposed countermeasures. For example, the suggestion unit can use the generative AI to simulate the effectiveness of the proposed treatment and propose the optimal treatment. This improves the accuracy of suggesting suspected diagnoses and countermeasures by using the generative AI. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or not. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI propose suspected diagnoses and countermeasures.

[0072] The search unit can search medical knowledge databases and medical image databases using generative AI. For example, the search unit can use generative AI to search the medical knowledge database based on a patient's medical history and medical images. For example, the search unit can use generative AI to search for medical knowledge about radial head fractures based on an X-ray image of the right elbow. The search unit can also use generative AI to search the medical image database and identify similar cases. For example, the search unit can use generative AI to identify similar fracture cases based on an X-ray image of the right elbow. The search unit can also use generative AI to evaluate search results and provide the most relevant information. For example, the search unit can use generative AI to evaluate the reliability of search results and provide the most reliable information. This improves the accuracy of the search by using generative AI. Some or all of the above-described processes in the search unit may be performed using generative AI, or they may not. For example, the search unit can input a patient's medical history and medical images into the generative AI and have the generative AI perform searches of the medical knowledge database and medical image database.

[0073] The analysis unit can analyze information uploaded by the generative AI. For example, the analysis unit can use the generative AI to analyze a patient's medical history and medical images. For example, the analysis unit can use the generative AI to analyze an X-ray image of the right elbow and detect the presence or absence of a fracture. The analysis unit can also use the generative AI to analyze a patient's diagnosis and treatment history to understand their medical history. For example, the analysis unit can use the generative AI to extract and analyze a patient's medical history from diagnosis and treatment history. The analysis unit can also use the generative AI to evaluate the analysis results and provide the most optimal information. For example, the analysis unit can use the generative AI to evaluate the reliability of the analysis results and provide the most reliable information. This improves the accuracy of the analysis by using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may be performed without the generative AI. For example, the analysis unit can input the patient's medical history and medical images into the generative AI and have the generative AI perform the analysis.

[0074] The reception desk can upload the patient's medical history and medical images. For example, the reception desk can retrieve and upload the patient's diagnosis and treatment history from the electronic medical record. For example, the reception desk can scan medical images brought by the patient and upload them as digital data. For example, the reception desk can scan X-ray images brought by the patient with a high-resolution scanner and save them as digital data. This allows for the efficient uploading of the patient's medical history and medical images. Some or all of the above processing at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's medical history and medical images into the AI ​​and have the AI ​​perform the upload.

[0075] The suggestion unit enables physicians to improve their clinical skills based on information suggested by the generative AI. For example, the suggestion unit uses the generative AI to suggest suspected diagnoses and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit uses the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggests treatment methods such as splinting or open reduction and internal fixation. Physicians can improve their diagnostic skills by implementing the treatment methods suggested by the generative AI and receiving feedback on the results. For example, the suggestion unit uses the generative AI to evaluate the effectiveness of the suggested treatment methods and provides feedback to the physician. This improves the physician's clinical skills. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or without using the generative AI. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI execute suggestions for improving clinical skills.

[0076] The suggestion unit can mitigate the risk of medical malpractice lawsuits based on information suggested by the generative AI. For example, the suggestion unit can use the generative AI to suggest a suspected diagnosis and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggest treatment methods such as splinting or open reduction and internal fixation. The physician can mitigate the risk of medical malpractice lawsuits by implementing the treatment method suggested by the generative AI and providing feedback on the results. For example, the suggestion unit can use the generative AI to evaluate the effectiveness of the suggested treatment method and provide feedback to the physician. This reduces the risk of medical malpractice lawsuits. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or not. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI execute suggestions to mitigate the risk of medical malpractice lawsuits.

[0077] The suggestion unit can provide feedback on the information suggested by the generative AI. For example, the suggestion unit can use the generative AI to suggest a suspected diagnosis and countermeasures based on the patient's medical history and medical images. For example, the suggestion unit can use the generative AI to identify the possibility of a radial head fracture based on an X-ray image of the right elbow and suggest treatment methods such as splinting or open reduction and internal fixation. The physician can improve the accuracy of the system by implementing the treatment method suggested by the generative AI and providing feedback on the results. For example, the suggestion unit can use the generative AI to evaluate the effectiveness of the suggested treatment method and provide feedback. This improves the accuracy of the system as the suggested information is fed back. Some or all of the above processing in the suggestion unit may be performed using the generative AI, or without using the generative AI. For example, the suggestion unit can input the patient's medical history and medical images into the generative AI and have the generative AI perform the feedback.

[0078] The reception desk can estimate the patient's emotions and adjust the timing of uploading medical history and images based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk can prompt them to upload at a time when they can relax. For example, the reception desk can prompt the patient to upload when they are relaxed. The reception desk can also simplify the interface to allow for quick uploads if the patient is in a hurry. For example, the reception desk can make uploading easy when the patient is in a hurry. The reception desk can also prompt the patient to upload after a break if they are tired. For example, the reception desk can prompt the patient to upload after a break when they are tired. This reduces the burden on the patient by adjusting the upload timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's emotions into the AI ​​and have the AI ​​adjust the timing of uploads.

[0079] The reception desk can analyze the patient's past medical history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods the patient has used in the past. For example, the reception desk may suggest the optimal method based on the upload methods the patient has used in the past. The reception desk can also recommend uploading in a specific format based on the patient's medical history. For example, the reception desk may recommend uploading in a specific format such as PDF or image format based on the patient's medical history. The reception desk can also simplify the upload process by automatically extracting necessary information from the patient's past medical history. For example, the reception desk extracts necessary information from the patient's past medical history to simplify the upload process. This streamlines the upload process by selecting the optimal upload method based on the patient's past medical history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's past medical history into an AI and have the AI ​​select the optimal upload method.

[0080] The reception desk can filter the uploaded medical history and medical images based on the patient's current health status and medical history. For example, the reception desk can upload only the necessary information based on the patient's current health status. For example, the reception desk can select and upload only the necessary information based on the patient's current health status. The reception desk can also prioritize uploading relevant information based on the patient's medical history. For example, the reception desk can prioritize uploading relevant information based on the patient's medical history. The reception desk can also prioritize uploading information of high urgency if the patient's health status is deteriorating. For example, the reception desk can prioritize uploading information of high urgency if the patient's health status is deteriorating. This enables efficient information management by uploading only the necessary information based on the patient's health status and medical history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's health status and medical history into the AI ​​and have the AI ​​perform the filtering.

[0081] The reception desk can estimate the patient's emotions and determine the priority of information to upload based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk will prioritize uploading important information. For example, if the patient is feeling anxious, the reception desk will prioritize uploading important information. The reception desk can also upload detailed information if the patient is relaxed. For example, if the patient is relaxed, the reception desk will upload detailed information. The reception desk can also upload only the most important information if the patient is in a hurry. For example, if the patient is in a hurry, the reception desk will upload only the most important information. In this way, important information is uploaded preferentially by prioritizing information according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input patients' emotions into the AI ​​and have the AI ​​prioritize the information it receives.

[0082] The reception desk can prioritize uploading highly relevant information based on the patient's geographical location when uploading medical history and medical images. For example, the reception desk can prioritize uploading information related to region-specific diseases based on the patient's place of residence. The reception desk can also prioritize uploading medical information for the patient's travel destination if the patient is traveling. The reception desk can also upload information about the nearest medical institution based on the patient's geographical location. This enables efficient information management by prioritizing the uploading of highly relevant information based on the patient's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's geographical location into the AI ​​and have the AI ​​perform the uploading of highly relevant information.

[0083] The reception desk can analyze a patient's social media activity and upload relevant information when uploading medical history and medical images. For example, the reception desk can extract and upload health-related information from the patient's social media posts. The reception desk can also infer the patient's recent health status from their social media activity and upload relevant information. The reception desk can also analyze health-related questions and comments on the patient's social media and upload relevant information. This enables efficient information management by uploading relevant information based on the patient's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the patient's social media activity into the AI ​​and have the AI ​​perform the uploading of relevant information.

[0084] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit will use concise and easy-to-understand language. The analysis unit can also provide detailed analysis results if the patient is relaxed. The analysis unit can also provide concise and easy-to-understand language if the patient is in a hurry. By adjusting the presentation of the analysis according to the patient's emotions, the analysis unit can provide results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the patient's emotions into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical history and medical images during the analysis. For example, the analysis unit can perform a detailed analysis on important medical history and medical images. The analysis unit can also perform a simplified analysis on information of lower importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the importance of the medical history and medical images into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of medical history and medical images during analysis. For example, the analysis unit can apply a fracture detection algorithm to an X-ray image of a fracture. The analysis unit can also apply a heart rate abnormality detection algorithm to electrocardiogram data. The analysis unit can also apply an abnormal value detection algorithm to blood test results. By applying different analysis algorithms depending on the category of medical history and medical images, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of medical history and medical images into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is feeling anxious, the analysis unit can provide a short, concise analysis. For example, if the patient is feeling anxious, the analysis unit can provide a short, concise analysis. The analysis unit can also provide a detailed analysis when the patient is relaxed. For example, if the patient is relaxed, the analysis unit can provide a detailed analysis. The analysis unit can also provide a concise analysis when the patient is in a hurry. For example, if the patient is in a hurry, the analysis unit can provide a concise analysis. By adjusting the length of the analysis according to the patient's emotions, the analysis results can be made easier for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the patient's emotions into the generating AI and have the generating AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the submission date of medical history and medical images during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted medical history and medical images. The analysis unit can also lower the priority of analysis for older information. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the submission dates of medical history and medical images into a generating AI and have the generating AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relationship between medical history and medical images during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also lower the priority of analysis for less relevant information. The analysis unit can also adjust the analysis schedule based on relevance. This allows for efficient analysis by adjusting the order of analysis based on the relationship between medical history and medical images. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relationship between medical history and medical images into the generative AI and have the generative AI adjust the order of analysis.

[0090] The search unit can estimate the patient's emotions and adjust the search criteria based on the estimated emotions. For example, if the patient is feeling anxious, the search unit can provide concise and easy-to-understand search results. For example, if the patient is feeling anxious, the search unit can provide concise and easy-to-understand search results. The search unit can also provide detailed search results if the patient is relaxed. For example, if the patient is relaxed, the search unit can provide detailed search results. The search unit can also provide concise and to the point if the patient is in a hurry. For example, if the patient is in a hurry, the search unit can provide concise and to the point. By adjusting the search criteria according to the patient's emotions, it is possible to provide search results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not using generative AI. For example, the search unit can input the patient's emotions into the generating AI and have the generating AI adjust the search criteria.

[0091] The search unit can improve the accuracy of searches by considering the relationship between medical history and medical images during the search process. For example, the search unit can provide highly accurate search results based on the relationship between medical history and medical images. The search unit can also analyze the relationship between medical history and medical images and provide optimal search results. The search unit can also determine the priority of search results based on the relationship between medical history and medical images. This improves the accuracy of searches by considering the relationship between medical history and medical images. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the relationship between medical history and medical images into a generative AI and have the generative AI perform the search accuracy improvement.

[0092] The search unit can perform searches while considering the medical history and attribute information of the submitter of medical images. For example, the search unit can provide highly relevant search results based on the submitter's area of ​​expertise. The search unit can also provide highly reliable search results based on the submitter's years of experience. The search unit can also provide optimal search results based on the submitter's attribute information. By considering the submitter's attribute information, highly relevant search results can be provided. Some or all of the above processing in the search unit may be performed using, for example, a generating AI, or without a generating AI. For example, the search unit can input the submitter's attribute information into a generating AI and have the generating AI perform the search.

[0093] The search unit can estimate the patient's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the patient is feeling anxious, the search unit will prioritize displaying important information. For example, if the patient is feeling anxious, the search unit will prioritize displaying important information. The search unit can also display detailed information if the patient is relaxed. For example, if the patient is relaxed, the search unit will display detailed information. The search unit can also display only the most important information if the patient is in a hurry. For example, if the patient is in a hurry, the search unit will display only the most important information. By adjusting the order in which search results are displayed according to the patient's emotions, search results that are easy for the patient to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can input the patient's emotions into the generating AI and have the generating AI adjust the display order of the search results.

[0094] The search unit can perform searches while considering the geographical distribution of medical history and medical images. For example, the search unit can prioritize searching for geographically close information. The search unit can also search for highly relevant information based on geographical distribution. The search unit can also provide optimal search results by considering geographical distribution. This allows for the provision of highly relevant search results by considering geographical distribution. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the geographical distribution of medical history and medical images into a generative AI and have the generative AI perform the search.

[0095] The search unit can improve the accuracy of its search by referring to relevant literature on medical history and medical images during the search. For example, the search unit can provide highly accurate search results based on relevant literature. The search unit can also provide optimal search results by referring to relevant literature. The search unit can also determine the priority of search results based on relevant literature. This improves the accuracy of the search by referring to relevant literature. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant literature on medical history and medical images into a generative AI and have the generative AI perform the search accuracy improvement.

[0096] The suggestion unit can estimate the patient's emotions and adjust the way it expresses its suggestions based on those emotions. For example, if the patient is feeling anxious, the suggestion unit will use concise and easy-to-understand language. For example, if the patient is feeling anxious, the suggestion unit will use concise and easy-to-understand language. The suggestion unit can also provide detailed suggestions if the patient is relaxed. For example, if the patient is relaxed, the suggestion unit will provide detailed suggestions. The suggestion unit can also provide concise suggestions if the patient is in a hurry. For example, if the patient is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way it expresses its suggestions according to the patient's emotions, it is possible to provide suggestions that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit can input the patient's emotions into the generation AI and have the generation AI adjust the way the proposal is expressed.

[0097] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the medical history and medical images. For example, the suggestion unit can provide detailed suggestions for important medical history and medical images. The suggestion unit can also provide simplified suggestions for less important information. The suggestion unit can also prioritize suggestions according to their importance. This allows for efficient suggestions by adjusting the level of detail of suggestions based on the importance of the medical history and medical images. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the importance of the medical history and medical images into the generative AI and have the generative AI adjust the level of detail of the suggestions.

[0098] The suggestion unit can apply different suggestion algorithms depending on the category of medical history and medical images during the suggestion process. For example, the suggestion unit can apply a fracture treatment algorithm to an X-ray image of a fracture. The suggestion unit can also apply a heart rate abnormality treatment algorithm to electrocardiogram data. The suggestion unit can also apply an abnormal value treatment algorithm to blood test results. By applying different suggestion algorithms depending on the category of medical history and medical images, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the categories of medical history and medical images into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0099] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the patient is feeling anxious, the suggestion unit can provide a short, concise suggestion. For example, if the patient is feeling anxious, the suggestion unit can provide a short, concise suggestion. The suggestion unit can also provide a detailed suggestion if the patient is relaxed. For example, if the patient is relaxed, the suggestion unit can provide a detailed suggestion. The suggestion unit can also provide a concise suggestion if the patient is in a hurry. For example, if the patient is in a hurry, the suggestion unit can provide a concise suggestion. By adjusting the length of the suggestion according to the patient's emotions, the suggestion unit can provide suggestions that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit can input the patient's emotions into the generating AI and have the AI ​​adjust the length of the proposal.

[0100] The proposal unit can determine the priority of proposals based on the submission timing of medical history and medical images. For example, the proposal unit may prioritize recently submitted medical history and medical images. The proposal unit can also lower the priority of older information. The proposal unit can also adjust the proposal schedule based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of medical history and medical images. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the submission timing of medical history and medical images into a generative AI and have the generative AI determine the priority of proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relationship between the medical history and medical images. For example, the suggestion unit can prioritize suggesting information that is highly relevant. The suggestion unit can also lower the priority of suggestions for information that is less relevant. The suggestion unit can also adjust the schedule of suggestions based on their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relationship between the medical history and medical images. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the relationship between the medical history and medical images into a generative AI and have the generative AI adjust the order of suggestions.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] Medical support systems can further improve diagnostic accuracy by acquiring patient lifestyle data. For example, by collecting data on patients' diet, exercise, and sleep patterns and incorporating this information into analysis, a more comprehensive diagnosis becomes possible. Furthermore, based on lifestyle data, preventative measures and lifestyle improvements can be suggested. For instance, information useful for maintaining patient health, such as recommendations for dietary improvements and exercise, can be provided. Additionally, lifestyle data can be used to predict the risk of specific diseases, promoting early detection and treatment. This leads to more effective patient health management.

[0104] The medical support system can further estimate the patient's emotions and adjust the feedback method of the diagnostic results based on those estimated emotions. For example, if the patient is feeling anxious, gentle language and reassuring expressions can be used. If the patient is relaxed, detailed explanations can be provided. Furthermore, if the patient is in a hurry, concise communication of key points can enable a quick response. This provides appropriate feedback tailored to the patient's emotions, improving patient satisfaction.

[0105] Medical support systems can further acquire patients' genetic information and improve the accuracy of diagnoses. For example, they can analyze a patient's genetic data to identify genetic risks. They can also predict the effectiveness of specific treatments based on genetic information and suggest the optimal treatment plan. Furthermore, they can use genetic information to predict future health risks and suggest preventive measures. This allows for the provision of personalized medical care to each individual patient.

[0106] The medical support system can further estimate the patient's emotions and adjust treatment suggestions based on those emotions. For example, if the patient is feeling anxious, it will prioritize suggesting low-risk treatments. If the patient is relaxed, it can provide detailed explanations of treatment options. Furthermore, if the patient is in a hurry, it can suggest treatments that can be implemented quickly, thus responding to the patient's needs. This ensures that the optimal treatment is provided in accordance with the patient's emotions.

[0107] The medical support system can further consider the patient's socioeconomic background when proposing diagnoses and treatments. For example, it can suggest cost-effective treatments based on the patient's income and insurance status. It can also suggest feasible preventive measures and lifestyle improvements based on the patient's living environment. Furthermore, if social support is needed, it can refer the patient to appropriate support organizations. This ensures that optimal medical care is provided according to the individual circumstances of each patient.

[0108] The medical support system can further estimate the patient's emotions and adjust the appointment schedule based on those emotions. For example, if a patient is feeling anxious, an earlier appointment can be prioritized. If the patient is relaxed, the appointment can proceed according to the normal schedule. Furthermore, if the patient is in a hurry, a special schedule can be created for a rapid appointment. This provides a flexible appointment schedule that is tailored to the patient's emotions.

[0109] Medical support systems can further analyze patients' past treatment outcomes to improve the accuracy of diagnosis and treatment. For example, they can suggest the optimal treatment method for similar cases based on past treatment results. They can also use past treatment results as feedback to improve the system's learning. Furthermore, they can use past treatment results to predict the effectiveness of treatment and explain it to the patient. This allows for the provision of highly accurate medical care that utilizes past data.

[0110] The medical support system can further estimate the patient's emotions and determine treatment priorities based on those emotions. For example, if a patient is feeling anxious, they will be given priority treatment. Conversely, if a patient is relaxed, they can be treated in the usual order. Furthermore, if a patient is in a hurry, special arrangements can be made to provide treatment quickly. This ensures that appropriate medical care is provided in accordance with the patient's emotions.

[0111] The medical support system can further monitor patients' health data in real time and issue alerts if abnormalities are detected. For example, it can continuously monitor the patient's heart rate, blood pressure, and blood sugar levels, and notify doctors if abnormal values ​​are detected. It can also suggest prompt countermeasures when abnormalities are detected. Furthermore, it can issue alerts to the patient themselves to encourage early intervention. This enables real-time health management.

[0112] The medical support system can further estimate the patient's emotions and adjust the way medical information is delivered based on those estimated emotions. For example, if a patient is feeling anxious, it can provide reassuring information. If the patient is relaxed, it can provide more detailed information. Furthermore, if the patient is in a hurry, it can provide concise information to enable a quick response. This ensures that appropriate medical information is provided in accordance with the patient's emotions, leading to a deeper understanding of their condition.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk uploads the patient's medical history and medical images. The patient's medical history includes past diagnoses, treatment history, and allergy information. Medical images include X-ray images, MRI images, and CT scan images. The reception desk retrieves the patient's diagnoses and treatment history from the electronic medical record and uploads them. It is also possible to scan medical images brought in by the patient and upload them as digital data. Step 2: The analysis unit uses a generation AI to analyze the information uploaded by the reception unit. The analysis is performed based on image analysis algorithms and text analysis technologies. For example, it analyzes uploaded X-ray images to detect the presence or absence of fractures. It also analyzes diagnostic results and treatment history to understand the patient's medical history. Step 3: The search unit uses generation AI to search the medical knowledge database and medical image database based on the information analyzed by the analysis unit. The search is performed based on the method of generating the search query and the search algorithm. For example, based on the analyzed X-ray images, the medical knowledge database is searched to identify similar cases. Also, based on the diagnosis results and treatment history, the medical image database is searched to identify relevant medical images. Step 4: The suggestion unit uses generational AI to propose suspected diagnoses and countermeasures based on the information obtained by the search unit. The suggestions are made based on the priority of the suggestions and the evaluation criteria for the suggested content. For example, it can propose suspected diagnoses and countermeasures based on the cases that have been searched. It can also propose treatment methods based on the medical images that have been searched.

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

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and uploads the patient's medical history and medical images. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information using a generating AI. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches the medical knowledge database and the medical image database. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes suspected diagnoses and countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and uploads the patient's medical history and medical images. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information using generating AI. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and searches the medical knowledge database and the medical image database. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes suspected diagnoses and countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and uploads the patient's medical history and medical images. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information using a generating AI. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches the medical knowledge database and the medical image database. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes suspected diagnoses and countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and uploads the patient's medical history and medical images. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information using a generating AI. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches the medical knowledge database and the medical image database. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes suspected diagnoses and countermeasures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception area where patients upload their medical history and medical images, An analysis unit analyzes the information uploaded by the reception unit, A search unit searches a medical knowledge database and a medical image database based on the information analyzed by the aforementioned analysis unit. The system includes a suggestion unit that proposes a suspected disease name and countermeasures based on the information obtained by the search unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, The AI ​​generates a suspected diagnosis and suggests appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Search medical knowledge databases and medical image databases using generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The AI ​​generates and analyzes the uploaded information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Upload the patient's medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Doctors can improve their clinical skills based on information suggested by AI-generated data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Reduce the risk of medical malpractice lawsuits based on information suggested by generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, Provide feedback on the information suggested by the generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the patient's emotions and adjusts the timing of uploading medical history and images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the patient's past medical history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading medical history and medical images, filtering is performed based on the patient's current health status and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the patient's emotions and prioritizes the information to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When uploading medical history and images, the system prioritizes uploading highly relevant information based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When uploading medical history and images, the system analyzes the patient's social media activity and uploads relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the patient history and the category of medical images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the patient's medical history and the timing of medical image submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationship between medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, Estimate the patient's emotions and adjust the search criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, When searching, consider the relationship between medical history and medical images to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, During the search, the system takes into account the patient's medical history and the attributes of the person who submitted the medical images. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, It estimates the patient's emotions and adjusts the order in which search results are displayed based on the estimated patient emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, When performing a search, the search should take into account the patient's medical history and the geographical distribution of medical images. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned search unit, During searches, we refer to relevant literature related to medical history and medical images to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the patient's medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of submission of the medical history and medical images. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of medical history and medical images. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where patients upload their medical history and medical images, An analysis unit analyzes the information uploaded by the reception unit, A search unit searches a medical knowledge database and a medical image database based on the information analyzed by the aforementioned analysis unit. The system includes a suggestion unit that proposes a suspected disease name and countermeasures based on the information obtained by the search unit. A system characterized by the following features.

2. The aforementioned proposal section is, The AI ​​generates a suspected diagnosis and suggests appropriate countermeasures. The system according to feature 1.

3. The aforementioned search unit, Search medical knowledge databases and medical image databases using generated AI. The system according to feature 1.

4. The aforementioned analysis unit, The generated AI analyzes the uploaded information. The system according to feature 1.

5. The aforementioned reception unit is Upload the patient's medical history and medical images. The system according to feature 1.

6. The aforementioned proposal section is, Doctors can improve their clinical skills based on information suggested by AI-generated data. The system according to feature 1.

7. The aforementioned proposal section is, Reduce the risk of medical malpractice lawsuits based on information suggested by generational AI. The system according to feature 1.

8. The aforementioned proposal section is, The information proposed by the generation AI is fed back. The system according to feature 1.

Images