System

Abstract

An object of a system according to an embodiment is to propose an appropriate treatment method on the basis of an analysis result of a medical image and to provide advice on drug prescription and treatment.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects a medical image. The analysis unit analyzes the medical image collected by the collection unit. The proposal unit proposes an appropriate treatment method on the basis of the result analyzed by the analysis unit. The providing unit provides an advice related to a prescription of a drug or an operation based on the treatment method proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately propose appropriate treatment methods based on the results of analyzing medical images.

[0005] The system according to the embodiment aims to propose an appropriate treatment method based on the analysis results of medical images and provide advice on drug prescriptions and treatments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects medical images. The analysis unit analyzes the medical images collected by the collection unit. The suggestion unit proposes an appropriate treatment method based on the results of the analysis by the analysis unit. The provision unit provides advice on drug prescriptions and treatments based on the treatment method proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an appropriate treatment method based on the analysis results of medical images and provide advice on prescription of medicines and treatment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A diagnostic support system according to an embodiment of the present invention utilizes a generative AI to analyze X-ray, CT, and MRI images to support the diagnosis of illnesses and injuries. In this diagnostic support system, the generative AI learns the treatment results of past diagnostic imaging, analyzes X-ray, CT, and MRI images to detect illnesses and injuries at an early stage, and proposes appropriate treatment methods. Furthermore, in this diagnostic support system, the generative AI provides advice to human physicians regarding medication prescriptions and procedures. For example, in this diagnostic support system, the generative AI learns the treatment results of past diagnostic imaging. For example, data on medical images such as X-rays, CT scans, and MRI scans and their corresponding treatment methods is collected and the generative AI learns from them. Next, in this diagnostic support system, the generative AI analyzes the X-ray, CT, and MRI images. The generative AI analyzes the input medical images to detect illnesses and injuries at an early stage. For example, the generative AI can analyze CT images to detect the presence of tumors. Furthermore, in this diagnostic support system, the generative AI proposes appropriate treatment methods based on the analysis results. The generative AI proposes the optimal treatment method corresponding to the analysis results based on past treatment results. For example, if a generative AI analyzes an MRI image and detects a brain abnormality, it can suggest the optimal treatment for that abnormality. In addition, in a diagnostic support system, the generative AI provides advice to human doctors regarding medication prescriptions and treatments. Based on the analysis results, the generative AI provides information on medication prescriptions and treatments, which the human doctor can use as a reference for treatment. For example, if a generative AI analyzes an X-ray image and detects a fracture, it can advise the doctor on the appropriate treatment method for that fracture. This allows the diagnostic support system to use generative AI to analyze X-ray, CT, and MRI images and support the diagnosis of illnesses and injuries. This enables the diagnostic support system to provide appropriate treatment methods to patients around the world from the early detection stage. In addition, by providing advice on medication prescriptions and treatments to human doctors, the generative AI supports doctors' diagnoses and enables more effective treatment.

[0029] A diagnostic support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects medical images. Examples of medical images include, but are not limited to, X-ray images, CT scan images, and MRI images. The collection unit collects, for example, X-ray images in digital format. The collection unit can also collect CT scan images. The collection unit can also collect MRI images. For example, the collection unit directly acquires data from medical equipment to collect medical images. The analysis unit uses a generative AI to analyze the medical images. The analysis is performed using, for example, image recognition technology using deep learning, but is not limited to, for example. For example, the analysis unit uses the generative AI to analyze medical images and perform early detection of illness or injury. The analysis unit can also use the generative AI to detect abnormalities in medical images. The analysis unit can also use the generative AI to extract features of medical images. For example, the analysis unit uses a deep learning model to detect abnormalities in medical images. The suggestion unit uses the generation AI to suggest an appropriate treatment method based on the results of the analysis by the analysis unit. The suggestion is made, for example, based on past treatment results, but is not limited to this example. For example, the suggestion unit uses the generation AI to suggest an optimal treatment method corresponding to the analysis results. The suggestion unit can also use the generation AI to present treatment method options. The suggestion unit can also use the generation AI to predict the effectiveness of a treatment method. For example, the suggestion unit suggests an optimal treatment method based on past treatment data. The provision unit provides advice on drug prescriptions and treatments based on the treatment method proposed by the generation AI. The advice includes, for example, information on drug prescriptions and treatment recommendations, but is not limited to this example. For example, the provision unit provides advice on drug prescriptions based on the treatment method proposed by the generation AI. The provision unit can also provide advice on treatments based on the treatment method proposed by the generation AI. The provision unit can also develop a treatment plan based on the treatment method proposed by the generation AI. For example, the provision unit provides information for prescribing drugs based on the treatment method proposed by the generation AI.As a result, the diagnosis support system according to the embodiment can consistently perform processes from collecting medical images to analyzing them, proposing treatment methods, and providing advice.

[0030] The acquisition unit can acquire at least one medical image from among X-rays, CT scans, and MRIs. Examples of medical images include, but are not limited to, X-ray images, CT scan images, and MRI images. For example, the acquisition unit can acquire X-ray images in a digital format. The acquisition unit can also acquire CT scan images. The acquisition unit can also acquire MRI images. For example, the acquisition unit can acquire data directly from a medical device and acquire medical images. This allows a variety of medical images to be acquired.

[0031] The analysis unit can use the generative AI to analyze medical images and perform early detection of illness or injury. Examples of generative AI include, but are not limited to, deep learning models and natural language processing models. The analysis unit can also use the generative AI to analyze medical images and perform early detection of illness or injury. The analysis unit can also use the generative AI to detect abnormalities in medical images. The analysis unit can also use the generative AI to extract features of medical images. For example, the analysis unit can use a deep learning model to detect abnormalities in medical images. This makes it possible to perform early detection of illness or injury using the generative AI.

[0032] The suggestion unit can suggest an appropriate treatment method based on the analysis results using the generative AI. Examples of the generative AI include, but are not limited to, deep learning models and natural language processing models. The suggestion unit can suggest an appropriate treatment method based on the analysis results using the generative AI. The suggestion unit can also use the generative AI to present treatment method options. The suggestion unit can also use the generative AI to predict the effectiveness of the treatment method. For example, the suggestion unit can suggest an optimal treatment method based on past treatment data. In this way, the generative AI can suggest an appropriate treatment method.

[0033] The providing unit can provide advice on drug prescriptions and treatments based on the treatment method proposed by the generating AI. Examples of the generating AI include, but are not limited to, deep learning models and natural language processing models. For example, the providing unit can provide advice on drug prescriptions based on the treatment method proposed by the generating AI. The providing unit can also provide advice on treatments based on the treatment method proposed by the generating AI. The providing unit can also create a treatment plan based on the treatment method proposed by the generating AI. For example, the providing unit provides information for prescribing drug prescriptions based on the treatment method proposed by the generating AI. This makes it possible to provide advice on drug prescriptions and treatments based on the treatment method proposed by the generating AI.

[0034] The analysis unit can analyze medical images using image recognition technology that uses deep learning. Deep learning includes, but is not limited to, for example, CNN (convolutional neural network) and RNN (recurrent neural network). The analysis unit analyzes medical images using, for example, image recognition technology that uses deep learning. The analysis unit can also detect abnormalities in medical images using deep learning. The analysis unit can also extract features of medical images using deep learning. For example, the analysis unit detects abnormalities in medical images using CNN. As a result, the use of deep learning improves the accuracy of medical image analysis.

[0035] The collection unit can analyze the past medical image collection history and select an appropriate collection method. For example, the collection unit preferentially selects the collection method that the user found most effective in the past. The collection unit can also avoid collection methods that the user found uncomfortable in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. For example, the collection unit analyzes the past medical image collection history and selects the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past collection history.

[0036] When collecting medical images, the collection unit can perform filtering based on the patient's current health condition and medical history. For example, the collection unit prioritizes collection of images of specific body parts based on the patient's medical history. The collection unit can also collect only necessary images based on the patient's current health condition. The collection unit can also determine the type of images to collect by referring to the patient's past diagnostic results. For example, the collection unit prioritizes collection of images of specific body parts based on the patient's medical history. This makes it possible to collect necessary medical images based on the patient's health condition and medical history.

[0037] When collecting medical images, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient desires voice input, the collection unit collects medical images upon receiving voice instructions. Furthermore, if the patient desires text input, the collection unit can also collect medical images upon receiving text instructions. Furthermore, if the patient desires image input, the collection unit can also collect medical images upon receiving image instructions. For example, if the patient desires voice input, the collection unit collects medical images upon receiving voice instructions. This makes it possible to select the optimal collection means depending on the patient's input method.

[0038] When collecting medical images, the collection unit can prioritize collecting highly relevant images by taking into account the geographical location information of the patient. For example, if the patient lives in a specific area, the collection unit can prioritize collecting images related to diseases that are likely to occur in that area. Furthermore, if the patient is traveling, the collection unit can also prioritize collecting images related to health risks at the patient's travel destination. Furthermore, the collection unit can collect the most relevant medical images based on the geographical location information of the patient. For example, if the patient lives in a specific area, the collection unit can prioritize collecting images related to diseases that are likely to occur in that area. In this way, highly relevant medical images can be collected by taking into account the geographical location information of the patient.

[0039] When collecting medical images, the collection unit can analyze the patient's social media activity and collect related images. For example, if the patient posts about health on social media, the collection unit collects medical images related to the content. The collection unit can also analyze the patient's social media activity and collect related medical images. The collection unit can also collect related medical images by referring to the activity of the patient's friends on social media. For example, if the patient posts about health on social media, the collection unit collects medical images related to the content. In this way, it is possible to analyze the patient's social media activity and collect related medical images.

[0040] The acquisition unit can customize the acquisition method by reflecting the patient's past feedback when acquiring medical images. For example, the acquisition unit selects the optimal acquisition method based on feedback provided by the patient in the past. The acquisition unit can also improve the acquisition procedure by reflecting the patient's past feedback. The acquisition unit can also introduce a method to reduce discomfort during acquisition based on the patient's feedback. For example, the acquisition unit selects the optimal acquisition method based on feedback provided by the patient in the past. This allows the acquisition method to be customized by reflecting the patient's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the medical image. For example, the analysis unit performs a detailed analysis on important medical images. The analysis unit can also perform a simplified analysis on medical images with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the medical image. For example, the analysis unit performs a detailed analysis on important medical images. This allows the level of detail of the analysis to be adjusted based on the importance of the medical image.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the medical image. For example, the analysis unit applies a specific algorithm to X-ray images for analysis. The analysis unit can also apply a different algorithm to CT images for analysis. The analysis unit can also apply a still different algorithm to MRI images for analysis. For example, the analysis unit applies a specific algorithm to X-ray images for analysis. This makes it possible to apply an appropriate analysis algorithm depending on the category of the medical image.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the patient's past analysis results. For example, the analysis unit corrects the current analysis result based on the patient's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the patient's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the medical images were taken. For example, the analysis unit prioritizes analyzing the most recent medical images. The analysis unit can also postpone analyzing older medical images. The analysis unit can also determine the priority of analysis based on the time when the medical images were taken. For example, the analysis unit prioritizes analyzing the most recent medical images. This makes it possible to determine the priority of analysis based on the time when the medical images were taken.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the medical images. For example, the analysis unit prioritizes analysis of highly relevant medical images. The analysis unit can also postpone analysis of less relevant medical images. The analysis unit can also adjust the order of analysis based on the relevance of the medical images. For example, the analysis unit prioritizes analysis of highly relevant medical images. This makes it possible to adjust the order of analysis based on the relevance of the medical images.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terms. Also, if the patient does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terms. This makes it possible to adjust the use of technical terms in the analysis according to the patient's level of expertise.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the treatment method when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important treatment method. The suggestion unit can also make a simplified suggestion for a treatment method with a low level of importance. The suggestion unit can also determine the priority of the suggestion based on the importance of the treatment method. For example, the suggestion unit makes a detailed suggestion for an important treatment method. This makes it possible to adjust the level of detail of the suggestion based on the importance of the treatment method.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the treatment method. For example, the proposal unit applies a specific algorithm to drug therapy and makes a proposal. The proposal unit can also apply a different algorithm to surgical therapy and make a proposal. The proposal unit can also apply an even different algorithm to rehabilitation and make a proposal. For example, the proposal unit applies a specific algorithm to drug therapy and makes a proposal. This makes it possible to apply an appropriate proposal algorithm depending on the category of the treatment method.

[0049] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the patient's past proposal results. The proposal unit, for example, corrects the current proposal based on the patient's past proposal results. The proposal unit can also adjust the proposal algorithm by referring to the patient's past proposal results. The proposal unit can also improve the accuracy of the proposal by using the patient's past proposal results. For example, the proposal unit corrects the current proposal based on the patient's past proposal results. This makes it possible to improve the accuracy of the proposal by referring to the patient's past proposal results.

[0050] At the time of proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the treatment method. For example, the proposal unit preferentially proposes a treatment method with a high urgency. The proposal unit can also preferentially propose a treatment method with an early submission time. The proposal unit can also determine the priority of the proposal based on the time of submission. For example, the proposal unit preferentially proposes a treatment method with a high urgency. In this way, the priority of the proposal can be determined based on the time of submission of the treatment method.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the treatment methods when making suggestions. For example, the suggestion unit preferentially suggests highly relevant treatment methods. The suggestion unit can also postpone less relevant treatment methods. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatment methods. For example, the suggestion unit preferentially suggests highly relevant treatment methods. This makes it possible to adjust the order of suggestions based on the relevance of the treatment methods.

[0052] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the patient's level of expertise. For example, if the patient has technical expertise, the suggestion unit uses detailed technical terms. Also, if the patient does not have technical expertise, the suggestion unit can explain the suggestion result in simple terms. Also, the suggestion unit can adjust the use of technical terms in the suggestion according to the patient's level of expertise. For example, if the patient has technical expertise, the suggestion unit uses detailed technical terms. This makes it possible to adjust the use of technical terms in the suggestion according to the patient's level of expertise.

[0053] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the treatment method. For example, the providing unit provides detailed advice for an important treatment method. The providing unit can also provide simplified advice for a treatment method with a low level of importance. The providing unit can also determine the priority of advice according to the importance of the treatment method. For example, the providing unit provides detailed advice for an important treatment method. This makes it possible to adjust the level of detail of advice according to the importance of the treatment method.

[0054] When providing advice, the providing unit can apply different advice algorithms depending on the category of the treatment method. For example, the providing unit applies a specific algorithm to drug therapy to provide advice. The providing unit can also apply a different algorithm to surgical therapy to provide advice. The providing unit can also apply an even different algorithm to rehabilitation to provide advice. For example, the providing unit applies a specific algorithm to drug therapy to provide advice. This makes it possible to apply an appropriate advice algorithm depending on the category of the treatment method.

[0055] When providing advice, the providing unit can improve the accuracy of the advice by referring to the patient's past advice results. The providing unit, for example, corrects the current advice based on the patient's past advice results. The providing unit can also adjust the advice algorithm by referring to the patient's past advice results. The providing unit can also improve the accuracy of the advice by using the patient's past advice results. For example, the providing unit corrects the current advice based on the patient's past advice results. This makes it possible to improve the accuracy of the advice by referring to the patient's past advice results.

[0056] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the treatment method. For example, the providing unit gives priority to advice on a treatment method with a high urgency. The providing unit can also give priority to advice on a treatment method with an early submission time. The providing unit can also determine the priority of advice based on the time of submission. For example, the providing unit gives priority to advice on a treatment method with a high urgency. In this way, the priority of advice can be determined based on the time of submission of the treatment method.

[0057] When providing advice, the providing unit can adjust the order of advice based on the relevance of the treatment methods. For example, the providing unit gives priority to advice on highly relevant treatment methods. The providing unit can also postpone less relevant treatment methods. The providing unit can also adjust the order of advice based on the relevance of the treatment methods. For example, the providing unit gives priority to advice on highly relevant treatment methods. This makes it possible to adjust the order of advice based on the relevance of the treatment methods.

[0058] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. Also, if the patient does not have technical expertise, the providing unit can explain the advice results in simple terms. Also, the providing unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. In this way, the use of technical terms in the advice can be adjusted depending on the patient's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The diagnostic support system can further collect lifestyle data of the patient and use the data in the analysis unit to evaluate the risk of disease. For example, the collection unit collects data such as the patient's diet, exercise, and sleep patterns. The analysis unit can analyze this data and evaluate how lifestyle habits affect the risk of disease. Furthermore, the suggestion unit can provide advice regarding lifestyle improvements. For example, it can suggest dietary improvements and exercise recommendations. This allows the diagnostic support system to evaluate the risk of disease based on the patient's lifestyle and provide preventive advice.

[0061] The diagnostic support system can further collect genetic information of the patient and use the data in the analysis unit to evaluate the risk of disease. For example, the collection unit collects genetic test results of the patient. The analysis unit can analyze this data and evaluate how genetic factors affect the risk of disease. Furthermore, the suggestion unit can suggest a treatment method based on the genetic information. For example, it can suggest a treatment method for a specific gene mutation. This allows the diagnostic support system to evaluate the risk of disease based on the patient's genetic information and provide an individualized treatment method.

[0062] The diagnostic support system can further collect environmental data about the patient and use the data in the analysis unit to assess the risk of disease. For example, the collection unit collects data about the patient's living environment and work environment. The analysis unit can analyze this data and assess how environmental factors affect the risk of disease. Furthermore, the suggestion unit can provide advice on improving the environment. For example, it can suggest improving air quality or stress management. This allows the diagnostic support system to assess the risk of disease based on the patient's environment and provide preventive advice.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The acquisition unit acquires medical images. Medical images include X-ray images, CT scan images, MRI images, etc. The acquisition unit obtains data directly from medical devices and collects these medical images in digital format. Step 2: The analysis unit uses the generative AI to analyze the medical images collected by the collection unit. The analysis is carried out using image recognition technology with deep learning to detect early signs of illness or injury, detect abnormalities in medical images, and extract features. Step 3: The proposal unit uses the generative AI to propose an appropriate treatment method based on the results of the analysis by the analysis unit. The proposal is based on past treatment results, presents optimal treatment options, and predicts the effectiveness of the treatment methods. Step 4: The provider provides advice on medication and treatment based on the treatment method proposed by the generative AI. The advice includes information on medication prescriptions, treatment recommendations, and the development of a treatment plan.

[0065] (Example 2) A diagnostic support system according to an embodiment of the present invention utilizes a generative AI to analyze X-ray, CT, and MRI images to support the diagnosis of illnesses and injuries. In this diagnostic support system, the generative AI learns the treatment results of past diagnostic imaging, analyzes X-ray, CT, and MRI images to detect illnesses and injuries at an early stage, and proposes appropriate treatment methods. Furthermore, in this diagnostic support system, the generative AI provides advice to human physicians regarding medication prescriptions and procedures. For example, in this diagnostic support system, the generative AI learns the treatment results of past diagnostic imaging. For example, data on medical images such as X-rays, CT scans, and MRI scans and their corresponding treatment methods is collected and the generative AI learns from them. Next, in this diagnostic support system, the generative AI analyzes the X-ray, CT, and MRI images. The generative AI analyzes the input medical images to detect illnesses and injuries at an early stage. For example, the generative AI can analyze CT images to detect the presence of tumors. Furthermore, in this diagnostic support system, the generative AI proposes appropriate treatment methods based on the analysis results. The generative AI proposes the optimal treatment method corresponding to the analysis results based on past treatment results. For example, if a generative AI analyzes an MRI image and detects a brain abnormality, it can suggest the optimal treatment for that abnormality. In addition, in a diagnostic support system, the generative AI provides advice to human doctors regarding medication prescriptions and treatments. Based on the analysis results, the generative AI provides information on medication prescriptions and treatments, which the human doctor can use as a reference for treatment. For example, if a generative AI analyzes an X-ray image and detects a fracture, it can advise the doctor on the appropriate treatment method for that fracture. This allows the diagnostic support system to use generative AI to analyze X-ray, CT, and MRI images and support the diagnosis of illnesses and injuries. This enables the diagnostic support system to provide appropriate treatment methods to patients around the world from the early detection stage. In addition, by providing advice on medication prescriptions and treatments to human doctors, the generative AI supports doctors' diagnoses and enables more effective treatment.

[0066] A diagnostic support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects medical images. Examples of medical images include, but are not limited to, X-ray images, CT scan images, and MRI images. The collection unit collects, for example, X-ray images in digital format. The collection unit can also collect CT scan images. The collection unit can also collect MRI images. For example, the collection unit directly acquires data from medical equipment to collect medical images. The analysis unit uses a generative AI to analyze the medical images. The analysis is performed using, for example, image recognition technology using deep learning, but is not limited to, for example. For example, the analysis unit uses the generative AI to analyze medical images and perform early detection of illness or injury. The analysis unit can also use the generative AI to detect abnormalities in medical images. The analysis unit can also use the generative AI to extract features of medical images. For example, the analysis unit uses a deep learning model to detect abnormalities in medical images. The suggestion unit uses the generation AI to suggest an appropriate treatment method based on the results of the analysis by the analysis unit. The suggestion is made, for example, based on past treatment results, but is not limited to this example. For example, the suggestion unit uses the generation AI to suggest an optimal treatment method corresponding to the analysis results. The suggestion unit can also use the generation AI to present treatment method options. The suggestion unit can also use the generation AI to predict the effectiveness of a treatment method. For example, the suggestion unit suggests an optimal treatment method based on past treatment data. The provision unit provides advice on drug prescriptions and treatments based on the treatment method proposed by the generation AI. The advice includes, for example, information on drug prescriptions and treatment recommendations, but is not limited to this example. For example, the provision unit provides advice on drug prescriptions based on the treatment method proposed by the generation AI. The provision unit can also provide advice on treatments based on the treatment method proposed by the generation AI. The provision unit can also develop a treatment plan based on the treatment method proposed by the generation AI. For example, the provision unit provides information for prescribing drugs based on the treatment method proposed by the generation AI.As a result, the diagnosis support system according to the embodiment can consistently perform processes from collecting medical images to analyzing them, proposing treatment methods, and providing advice.

[0067] The acquisition unit can acquire at least one medical image from among X-rays, CT scans, and MRIs. Examples of medical images include, but are not limited to, X-ray images, CT scan images, and MRI images. For example, the acquisition unit can acquire X-ray images in a digital format. The acquisition unit can also acquire CT scan images. The acquisition unit can also acquire MRI images. For example, the acquisition unit can acquire data directly from a medical device and acquire medical images. This allows a variety of medical images to be acquired.

[0068] The analysis unit can use the generative AI to analyze medical images and perform early detection of illness or injury. Examples of generative AI include, but are not limited to, deep learning models and natural language processing models. The analysis unit can also use the generative AI to analyze medical images and perform early detection of illness or injury. The analysis unit can also use the generative AI to detect abnormalities in medical images. The analysis unit can also use the generative AI to extract features of medical images. For example, the analysis unit can use a deep learning model to detect abnormalities in medical images. This makes it possible to perform early detection of illness or injury using the generative AI.

[0069] The suggestion unit can suggest an appropriate treatment method based on the analysis results using the generative AI. Examples of the generative AI include, but are not limited to, deep learning models and natural language processing models. The suggestion unit can suggest an appropriate treatment method based on the analysis results using the generative AI. The suggestion unit can also use the generative AI to present treatment method options. The suggestion unit can also use the generative AI to predict the effectiveness of the treatment method. For example, the suggestion unit can suggest an optimal treatment method based on past treatment data. In this way, the generative AI can suggest an appropriate treatment method.

[0070] The providing unit can provide advice on drug prescriptions and treatments based on the treatment method proposed by the generating AI. Examples of the generating AI include, but are not limited to, deep learning models and natural language processing models. For example, the providing unit can provide advice on drug prescriptions based on the treatment method proposed by the generating AI. The providing unit can also provide advice on treatments based on the treatment method proposed by the generating AI. The providing unit can also create a treatment plan based on the treatment method proposed by the generating AI. For example, the providing unit provides information for prescribing drug prescriptions based on the treatment method proposed by the generating AI. This makes it possible to provide advice on drug prescriptions and treatments based on the treatment method proposed by the generating AI.

[0071] The analysis unit can analyze medical images using image recognition technology that uses deep learning. Deep learning includes, but is not limited to, for example, CNN (convolutional neural network) and RNN (recurrent neural network). The analysis unit analyzes medical images using, for example, image recognition technology that uses deep learning. The analysis unit can also detect abnormalities in medical images using deep learning. The analysis unit can also extract features of medical images using deep learning. For example, the analysis unit detects abnormalities in medical images using CNN. As a result, the use of deep learning improves the accuracy of medical image analysis.

[0072] The collection unit can estimate the user's emotions and adjust the timing of medical image collection based on the estimated user's emotions. For example, if the user is nervous, the collection unit delays collection of medical images until the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately start collecting medical images. Furthermore, if the user is feeling anxious, the collection unit can allow time for medical staff to reassure the user before collecting medical images. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and adjusts the collection timing. This allows the timing of medical image collection to be adjusted according to the user's emotions, thereby enabling images to be collected at a more appropriate time.

[0073] The collection unit can analyze the past medical image collection history and select an appropriate collection method. For example, the collection unit preferentially selects the collection method that the user found most effective in the past. The collection unit can also avoid collection methods that the user found uncomfortable in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. For example, the collection unit analyzes the past medical image collection history and selects the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past collection history.

[0074] When collecting medical images, the collection unit can perform filtering based on the patient's current health condition and medical history. For example, the collection unit prioritizes collection of images of specific body parts based on the patient's medical history. The collection unit can also collect only necessary images based on the patient's current health condition. The collection unit can also determine the type of images to collect by referring to the patient's past diagnostic results. For example, the collection unit prioritizes collection of images of specific body parts based on the patient's medical history. This makes it possible to collect necessary medical images based on the patient's health condition and medical history.

[0075] When collecting medical images, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient desires voice input, the collection unit collects medical images upon receiving voice instructions. Furthermore, if the patient desires text input, the collection unit can also collect medical images upon receiving text instructions. Furthermore, if the patient desires image input, the collection unit can also collect medical images upon receiving image instructions. For example, if the patient desires voice input, the collection unit collects medical images upon receiving voice instructions. This makes it possible to select the optimal collection means depending on the patient's input method.

[0076] The collection unit can estimate the user's emotions and determine the priority of medical images to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting the most important medical images. Furthermore, if the user is relaxed, the collection unit can collect medical images in order. Furthermore, if the user is nervous, the collection unit can prioritize medical images that can be collected in a short time. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on changes in facial expressions and determine the priority of medical images to be collected. In this way, the priority of medical images to be collected can be determined according to the user's emotions.

[0077] When collecting medical images, the collection unit can prioritize collecting highly relevant images by taking into account the geographical location information of the patient. For example, if the patient lives in a specific area, the collection unit can prioritize collecting images related to diseases that are likely to occur in that area. Furthermore, if the patient is traveling, the collection unit can also prioritize collecting images related to health risks at the patient's travel destination. Furthermore, the collection unit can collect the most relevant medical images based on the geographical location information of the patient. For example, if the patient lives in a specific area, the collection unit can prioritize collecting images related to diseases that are likely to occur in that area. In this way, highly relevant medical images can be collected by taking into account the geographical location information of the patient.

[0078] When collecting medical images, the collection unit can analyze the patient's social media activity and collect related images. For example, if the patient posts about health on social media, the collection unit collects medical images related to the content. The collection unit can also analyze the patient's social media activity and collect related medical images. The collection unit can also collect related medical images by referring to the activity of the patient's friends on social media. For example, if the patient posts about health on social media, the collection unit collects medical images related to the content. In this way, it is possible to analyze the patient's social media activity and collect related medical images.

[0079] The acquisition unit can customize the acquisition method by reflecting the patient's past feedback when acquiring medical images. For example, the acquisition unit selects the optimal acquisition method based on feedback provided by the patient in the past. The acquisition unit can also improve the acquisition procedure by reflecting the patient's past feedback. The acquisition unit can also introduce a method to reduce discomfort during acquisition based on the patient's feedback. For example, the acquisition unit selects the optimal acquisition method based on feedback provided by the patient in the past. This allows the acquisition method to be customized by reflecting the patient's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the method of presentation of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide an analysis result that gives a sense of security. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the method of presentation of the analysis. This allows the method of presentation of the analysis to be adjusted according to the user's emotions.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the medical image. For example, the analysis unit performs a detailed analysis on important medical images. The analysis unit can also perform a simplified analysis on medical images with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the medical image. For example, the analysis unit performs a detailed analysis on important medical images. This allows the level of detail of the analysis to be adjusted based on the importance of the medical image.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the medical image. For example, the analysis unit applies a specific algorithm to X-ray images for analysis. The analysis unit can also apply a different algorithm to CT images for analysis. The analysis unit can also apply a still different algorithm to MRI images for analysis. For example, the analysis unit applies a specific algorithm to X-ray images for analysis. This makes it possible to apply an appropriate analysis algorithm depending on the category of the medical image.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the patient's past analysis results. For example, the analysis unit corrects the current analysis result based on the patient's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the patient's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide analysis results in a short time. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is feeling anxious, the analysis unit can provide analysis results that give the user a sense of security. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expression and adjust the length of the analysis. This allows the length of the analysis to be adjusted according to the user's emotions.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time when the medical images were taken. For example, the analysis unit prioritizes analyzing the most recent medical images. The analysis unit can also postpone analyzing older medical images. The analysis unit can also determine the priority of analysis based on the time when the medical images were taken. For example, the analysis unit prioritizes analyzing the most recent medical images. This makes it possible to determine the priority of analysis based on the time when the medical images were taken.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the medical images. For example, the analysis unit prioritizes analysis of highly relevant medical images. The analysis unit can also postpone analysis of less relevant medical images. The analysis unit can also adjust the order of analysis based on the relevance of the medical images. For example, the analysis unit prioritizes analysis of highly relevant medical images. This makes it possible to adjust the order of analysis based on the relevance of the medical images.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terms. Also, if the patient does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses detailed technical terms. This makes it possible to adjust the use of technical terms in the analysis according to the patient's level of expertise.

[0088] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is feeling anxious, the suggestion unit can also provide suggestions that give the user a sense of security. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression and adjust the way suggestions are presented. This makes it possible to adjust the way suggestions are presented according to the user's emotions.

[0089] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the treatment method when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important treatment method. The suggestion unit can also make a simplified suggestion for a treatment method with a low level of importance. The suggestion unit can also determine the priority of the suggestion based on the importance of the treatment method. For example, the suggestion unit makes a detailed suggestion for an important treatment method. This makes it possible to adjust the level of detail of the suggestion based on the importance of the treatment method.

[0090] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the treatment method. For example, the proposal unit applies a specific algorithm to drug therapy and makes a proposal. The proposal unit can also apply a different algorithm to surgical therapy and make a proposal. The proposal unit can also apply an even different algorithm to rehabilitation and make a proposal. For example, the proposal unit applies a specific algorithm to drug therapy and makes a proposal. This makes it possible to apply an appropriate proposal algorithm depending on the category of the treatment method.

[0091] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the patient's past proposal results. The proposal unit, for example, corrects the current proposal based on the patient's past proposal results. The proposal unit can also adjust the proposal algorithm by referring to the patient's past proposal results. The proposal unit can also improve the accuracy of the proposal by using the patient's past proposal results. For example, the proposal unit corrects the current proposal based on the patient's past proposal results. This makes it possible to improve the accuracy of the proposal by referring to the patient's past proposal results.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide a suggestion result in a short time. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion result. Furthermore, if the user is feeling anxious, the suggestion unit can provide a suggestion result that gives a sense of security. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the suggestion unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression and adjust the length of the suggestions. In this way, the length of the suggestions can be adjusted according to the user's emotions.

[0093] At the time of proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the treatment method. For example, the proposal unit preferentially proposes a treatment method with a high urgency. The proposal unit can also preferentially propose a treatment method with an early submission time. The proposal unit can also determine the priority of the proposal based on the time of submission. For example, the proposal unit preferentially proposes a treatment method with a high urgency. In this way, the priority of the proposal can be determined based on the time of submission of the treatment method.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the treatment methods when making suggestions. For example, the suggestion unit preferentially suggests highly relevant treatment methods. The suggestion unit can also postpone less relevant treatment methods. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatment methods. For example, the suggestion unit preferentially suggests highly relevant treatment methods. This makes it possible to adjust the order of suggestions based on the relevance of the treatment methods.

[0095] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the patient's level of expertise. For example, if the patient has technical expertise, the suggestion unit uses detailed technical terms. Also, if the patient does not have technical expertise, the suggestion unit can explain the suggestion result in simple terms. Also, the suggestion unit can adjust the use of technical terms in the suggestion according to the patient's level of expertise. For example, if the patient has technical expertise, the suggestion unit uses detailed technical terms. This makes it possible to adjust the use of technical terms in the suggestion according to the patient's level of expertise.

[0096] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is feeling anxious, the providing unit can provide advice that gives the user a sense of security. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression and adjust the way in which advice is presented. This makes it possible to adjust the way in which advice is presented according to the user's emotions.

[0097] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the treatment method. For example, the providing unit provides detailed advice for an important treatment method. The providing unit can also provide simplified advice for a treatment method with a low level of importance. The providing unit can also determine the priority of advice according to the importance of the treatment method. For example, the providing unit provides detailed advice for an important treatment method. This makes it possible to adjust the level of detail of advice according to the importance of the treatment method.

[0098] When providing advice, the providing unit can apply different advice algorithms depending on the category of the treatment method. For example, the providing unit applies a specific algorithm to drug therapy to provide advice. The providing unit can also apply a different algorithm to surgical therapy to provide advice. The providing unit can also apply an even different algorithm to rehabilitation to provide advice. For example, the providing unit applies a specific algorithm to drug therapy to provide advice. This makes it possible to apply an appropriate advice algorithm depending on the category of the treatment method.

[0099] When providing advice, the providing unit can improve the accuracy of the advice by referring to the patient's past advice results. The providing unit, for example, corrects the current advice based on the patient's past advice results. The providing unit can also adjust the advice algorithm by referring to the patient's past advice results. The providing unit can also improve the accuracy of the advice by using the patient's past advice results. For example, the providing unit corrects the current advice based on the patient's past advice results. This makes it possible to improve the accuracy of the advice by referring to the patient's past advice results.

[0100] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide advice results in a short time. Furthermore, if the user is relaxed, the providing unit can provide detailed advice results. Furthermore, if the user is feeling anxious, the providing unit can provide advice results that give the user a sense of security. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression and adjust the length of advice. In this way, the length of advice can be adjusted according to the user's emotions.

[0101] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the treatment method. For example, the providing unit gives priority to advice on a treatment method with a high urgency. The providing unit can also give priority to advice on a treatment method with an early submission time. The providing unit can also determine the priority of advice based on the time of submission. For example, the providing unit gives priority to advice on a treatment method with a high urgency. In this way, the priority of advice can be determined based on the time of submission of the treatment method.

[0102] When providing advice, the providing unit can adjust the order of advice based on the relevance of the treatment methods. For example, the providing unit gives priority to advice on highly relevant treatment methods. The providing unit can also postpone less relevant treatment methods. The providing unit can also adjust the order of advice based on the relevance of the treatment methods. For example, the providing unit gives priority to advice on highly relevant treatment methods. This makes it possible to adjust the order of advice based on the relevance of the treatment methods.

[0103] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. Also, if the patient does not have technical expertise, the providing unit can explain the advice results in simple terms. Also, the providing unit can adjust the use of technical terms in the advice depending on the patient's level of expertise. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. In this way, the use of technical terms in the advice can be adjusted depending on the patient's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect medical images using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the medical images using generative AI. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate treatment method based on the analysis results. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides advice on drug prescriptions and treatments. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect medical images using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the medical images using generative AI. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate treatment method based on the analysis results. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides advice regarding drug prescriptions and treatments. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect medical images using the camera 42 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the medical images using generative AI. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate treatment method based on the analysis results. For example, the provision unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides advice on drug prescriptions and treatments. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect medical images using the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the medical images using generative AI. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate treatment method based on the analysis results. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides advice on drug prescriptions and treatments.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The diagnostic support system can further collect lifestyle data of the patient and use the data in the analysis unit to evaluate the risk of disease. For example, the collection unit collects data such as the patient's diet, exercise, and sleep patterns. The analysis unit can analyze this data and evaluate how lifestyle habits affect the risk of disease. Furthermore, the suggestion unit can provide advice regarding lifestyle improvements. For example, it can suggest dietary improvements and exercise recommendations. This allows the diagnostic support system to evaluate the risk of disease based on the patient's lifestyle and provide preventive advice.

[0106] The diagnostic support system can further collect genetic information of the patient and use the data in the analysis unit to evaluate the risk of disease. For example, the collection unit collects genetic test results of the patient. The analysis unit can analyze this data and evaluate how genetic factors affect the risk of disease. Furthermore, the suggestion unit can suggest a treatment method based on the genetic information. For example, it can suggest a treatment method for a specific gene mutation. This allows the diagnostic support system to evaluate the risk of disease based on the patient's genetic information and provide an individualized treatment method.

[0107] The diagnostic support system can further collect environmental data about the patient and use the data in the analysis unit to assess the risk of disease. For example, the collection unit collects data about the patient's living environment and work environment. The analysis unit can analyze this data and assess how environmental factors affect the risk of disease. Furthermore, the suggestion unit can provide advice on improving the environment. For example, it can suggest improving air quality or stress management. This allows the diagnostic support system to assess the risk of disease based on the patient's environment and provide preventive advice.

[0108] The diagnostic support system can further evaluate the patient's psychological state and use the data in the analysis unit to assess the risk of disease. For example, the collection unit collects data on the patient's stress level and psychological health state. The analysis unit can analyze this data and evaluate how the psychological state affects the risk of disease. Furthermore, the suggestion unit can provide advice to improve psychological health. For example, it can suggest relaxation techniques or counseling recommendations. This allows the diagnostic support system to assess the risk of disease based on the patient's psychological state and provide preventive advice.

[0109] The diagnostic support system can further evaluate the patient's social connections and use the data in the analysis unit to assess the risk of disease. For example, the collection unit collects data on the patient's family and friendship relationships. The analysis unit can analyze this data and assess how social connections affect the risk of disease. Furthermore, the suggestion unit can provide advice for strengthening social connections. For example, it can suggest joining a support group or recommend community activities. This allows the diagnostic support system to assess the risk of disease based on the patient's social connections and provide preventive advice.

[0110] The diagnostic support system can further estimate the patient's emotions and adjust the proposed treatment method based on the estimated emotions. For example, if the patient feels anxious, the suggestion unit can suggest a treatment method that gives the patient a sense of security. Furthermore, if the patient feels relaxed, the suggestion unit can also suggest a detailed treatment method. Furthermore, if the patient feels nervous, the suggestion unit can also suggest a simple and easy-to-understand treatment method. In this way, the diagnostic support system can adjust the proposed treatment method based on the patient's emotions and provide more effective treatment.

[0111] The diagnostic support system can further estimate the patient's emotions and present treatment options based on the estimated emotions. For example, if the patient feels anxious, the suggestion unit can preferentially present low-risk treatment options. If the patient feels relaxed, the suggestion unit can also present multiple treatment options. Furthermore, if the patient feels nervous, the suggestion unit can briefly present the most effective treatment option. In this way, the diagnostic support system can present treatment options based on the patient's emotions and provide the optimal treatment option for the patient.

[0112] The diagnostic support system can further estimate the patient's emotions and predict the effectiveness of a treatment method based on the estimated emotions. For example, if the patient is feeling anxious, the suggestion unit can predict the effectiveness of a treatment method that will reduce the anxiety. Also, if the patient is relaxed, the suggestion unit can predict the effectiveness of a treatment method that will maintain that state. Furthermore, if the patient is tense, the suggestion unit can predict the effectiveness of a treatment method that will relieve the tension. In this way, the diagnostic support system can predict the effectiveness of a treatment method based on the patient's emotions and provide more effective treatment.

[0113] The diagnostic support system can further estimate the patient's emotions and create a treatment plan based on the estimated emotions. For example, if the patient feels anxious, the providing unit can create a treatment plan that provides a sense of security. If the patient feels relaxed, the providing unit can also create a detailed treatment plan. Furthermore, if the patient feels nervous, the providing unit can also create a concise and easy-to-understand treatment plan. This allows the diagnostic support system to create a treatment plan based on the patient's emotions and provide more effective treatment.

[0114] The diagnosis support system can further estimate the patient's emotions and provide treatment advice based on the estimated emotions. For example, if the patient feels anxious, the providing unit can provide reassuring advice. If the patient feels relaxed, the providing unit can also provide detailed advice. If the patient feels nervous, the providing unit can also provide concise and easy-to-understand advice. This allows the diagnosis support system to provide treatment advice based on the patient's emotions and provide more effective treatment.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The acquisition unit acquires medical images. Medical images include X-ray images, CT scan images, MRI images, etc. The acquisition unit obtains data directly from medical devices and collects these medical images in digital format. Step 2: The analysis unit uses the generative AI to analyze the medical images collected by the collection unit. The analysis is carried out using image recognition technology with deep learning to detect early signs of illness or injury, detect abnormalities in medical images, and extract features. Step 3: The proposal unit uses the generative AI to propose an appropriate treatment method based on the results of the analysis by the analysis unit. The proposal is based on past treatment results, presents optimal treatment options, and predicts the effectiveness of the treatment methods. Step 4: The provider provides advice on medication and treatment based on the treatment method proposed by the generative AI. The advice includes information on medication prescriptions, treatment recommendations, and the development of a treatment plan.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring medical images; an analysis unit that analyzes the medical images acquired by the acquisition unit; a suggestion unit that suggests an appropriate treatment method based on the results of the analysis by the analysis unit; a providing unit that provides advice on drug prescriptions and treatments based on the treatment method proposed by the suggesting unit; Equipped with A system characterized by:

2. The collecting unit Collect at least one medical image from the following sources: X-ray, CT, and MRI 2. The system of claim 1.

3. The analysis unit Using generative AI to analyze medical images for early detection of illness and injury 2. The system of claim 1.

4. The proposal unit Using generative AI to propose appropriate treatment methods based on analysis results 2. The system of claim 1.

5. The providing unit Providing advice on medication prescriptions and treatments based on treatment methods proposed by generative AI 2. The system of claim 1.

6. The analysis unit Analyzing medical images using deep learning image recognition technology 2. The system of claim 1.

7. The collecting unit Estimating user emotions and adjusting timing of medical image acquisition based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past medical image acquisition history and select the appropriate acquisition method 2. The system of claim 1.

Images