System

Abstract

An object of a system according to an embodiment is to quickly and accurately analyze medical image data and identify an abnormal portion.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection part collects medical image data. The analysis unit analyzes the data collected by the collection unit and identifies an abnormal location. The generation unit generates a 3D model based on the abnormal portion specified by the analysis unit. The provision unit provides the 3D model generated by the generation 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] With conventional technology, analyzing medical image data and identifying abnormalities takes time, and there is a demand for faster diagnosis.

[0005] The system according to the embodiment aims to quickly and accurately analyze medical image data and identify abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects medical image data. The analysis unit analyzes the data collected by the collection unit and identifies abnormalities. The generation unit generates a 3D model based on the abnormalities identified by the analysis unit. The provision unit provides the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately analyze medical image data and identify abnormalities. [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 medical support system according to an embodiment of the present invention automatically collects medical image data, analyzes it using a generation AI, identifies abnormalities, and generates and provides a 3D model. The medical support system collects medical image data, analyzes it using a generation AI, identifies abnormalities, and then generates a 3D model to provide to medical staff. For example, the medical support system collects a patient's medical image data, including image data such as CT scans and MRIs. The medical support system then uses a generation AI to analyze the collected image data and identify abnormalities. The generation AI uses image recognition technology to quickly and accurately detect abnormalities, such as tumors and vascular abnormalities. The medical support system then generates a 3D model based on the identified abnormalities. The generation AI creates a detailed 3D model based on the shape and position of the identified abnormalities. This 3D model is provided in a format that is visually easy for medical staff to understand. For example, it may include the size and location of the tumor and the detailed structure of vascular abnormalities. Furthermore, the medical support system uses the generated 3D model to promote mutual understanding between the patient and medical staff. Medical staff use the 3D model to explain the diagnosis to the patient and propose a treatment plan. This allows patients to better understand their condition and facilitates communication with medical staff. Finally, the medical support system creates an accurate treatment plan based on the generated 3D model. Medical staff use the 3D model to simulate treatment and select the optimal treatment method. For example, this includes planning surgery and setting radiation therapy targets. This allows the medical support system to help medical staff deal with complex diagnoses, promote mutual understanding with patients, and develop accurate treatment plans. For example, medical staff can use the generated 3D model to explain diagnosis results to patients and propose treatment plans. This allows patients to better understand their condition and facilitates communication with medical staff.Medical staff can also use the 3D model to simulate treatment and select the optimal treatment method, improving the accuracy of treatment and reducing the burden on patients.

[0029] A medical support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects medical image data. Examples of medical image data include, but are not limited to, CT scans, MRIs, and X-ray images. The collection unit collects medical image data using, for example, a CT scanning device. The collection unit can also collect medical image data using an MRI device. The collection unit can also collect medical image data using an X-ray device. For example, the collection unit can acquire cross-sectional images of a patient's body using a CT scanning device. Detailed images of the patient's body can also be acquired using an MRI device. Images of bones and organs can also be acquired using an X-ray device. The analysis unit uses a generation AI to analyze the medical image data collected by the collection unit and identify abnormalities. Examples of abnormalities include, but are not limited to, tumors, inflammation, and fractures. For example, the generation AI can analyze the medical image data using deep learning technology and identify abnormalities. The generation AI can also analyze the medical image data using machine learning technology and identify abnormalities. The generation AI can also analyze medical image data using a neural network to identify abnormalities. For example, the generation AI can use deep learning technology to identify the location and size of a tumor. It can also use machine learning technology to identify the extent of inflammation. It can also use a neural network to identify the location of a fracture. The generation unit generates a 3D model based on the shape and location of the abnormality identified by the analysis unit. The 3D model includes, but is not limited to, the size and location of a tumor and the detailed structure of vascular abnormalities. For example, the generation unit generates a 3D model based on the shape and location of the identified tumor. It can also generate a 3D model based on the shape and location of the identified vascular abnormality. It can also generate a 3D model based on the shape and location of the identified fracture. For example, the generation unit generates a 3D model that shows the size and location of a tumor in detail. It can also generate a 3D model that shows the detailed structure of vascular abnormalities. It can also generate a 3D model that shows the location and shape of a fracture. The provision unit provides the 3D model generated by the generation unit to medical staff.The providing unit, for example, displays the generated 3D model on a medical staff member's computer. The providing unit can also display the generated 3D model on a medical staff member's tablet. The providing unit can also display the generated 3D model on a medical staff member's smartphone. For example, the providing unit displays the generated 3D model on a medical staff member's computer screen. The providing unit can also display the generated 3D model on a tablet screen. The providing unit can also display the generated 3D model on a smartphone screen. This enables the medical support system according to the embodiment to support medical staff in dealing with difficult diagnoses. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can optimize the display content using AI when displaying the generated 3D model on the medical staff member's computer.

[0030] The acquisition unit can acquire medical image data using CT scans or MRIs. CT scans include, but are not limited to, helical CTs and multi-slice CTs. MRIs include, but are not limited to, T1-weighted images, T2-weighted images, and diffusion-weighted images. The acquisition unit can acquire helical CTs using a CT scanning device. The acquisition unit can also acquire multi-slice CTs. The acquisition unit can also acquire T1-weighted images using an MRI device. For example, the acquisition unit can acquire cross-sectional images of the patient's body using a CT scanning device. An MRI device can also acquire detailed images of the patient's body. By collecting medical image data such as CT scans and MRIs, detailed medical information can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input medical image data acquired using a CT scanning device into a generation AI and have the generation AI analyze the image data.

[0031] The analysis unit can analyze medical image data using AI and identify abnormalities. Examples of AI include, but are not limited to, deep learning, machine learning, and neural networks. The analysis unit can analyze medical image data using, for example, deep learning technology and identify abnormalities. The analysis unit can also analyze medical image data using machine learning technology and identify abnormalities. The analysis unit can also analyze medical image data using neural networks and identify abnormalities. For example, the analysis unit can use deep learning technology to identify the location and size of a tumor. Machine learning technology can also identify the extent of inflammation. Neural networks can also identify the location of a fracture. As a result, the use of AI improves the accuracy of medical image data analysis, allowing abnormalities to be identified quickly and accurately. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input medical image data to the generation AI and have the generation AI identify abnormalities.

[0032] The generation unit can generate a 3D model based on the shape and position of the identified abnormality. Examples of shapes include, but are not limited to, size and shape type (e.g., spherical, elliptical, etc.). Examples of positions include, but are not limited to, coordinate systems and reference points. The generation unit can generate a 3D model based on, for example, the shape and position of an identified tumor. The generation unit can also generate a 3D model based on the shape and position of an identified vascular abnormality. The generation unit can also generate a 3D model based on the shape and position of an identified fracture. For example, the generation unit can generate a 3D model that shows the size and position of a tumor in detail. It can also generate a 3D model that shows the detailed structure of a vascular abnormality. It can also generate a 3D model that shows the position and shape of a fracture. By generating a 3D model based on the shape and position of an abnormality, medical staff can be provided with information in a form that is visually easy to understand. Some or all of the above-described processing by the generation unit can be performed, for example, using a generation AI, or without using a generation AI. For example, the generation unit can input data on the identified abnormality location into the generation AI and cause the generation AI to generate a 3D model.

[0033] The providing unit can provide the generated 3D model to medical staff to promote mutual understanding with the patient. Mutual understanding includes, but is not limited to, communication means and methods for assessing the level of understanding. For example, the providing unit can display the generated 3D model on the medical staff's computer. The providing unit can also display the generated 3D model on the medical staff's tablet. The providing unit can also display the generated 3D model on the medical staff's smartphone. For example, the providing unit can display the generated 3D model on the medical staff's computer screen, on a tablet screen, or on a smartphone screen. Providing the generated 3D model to the medical staff promotes mutual understanding with the patient and enables the creation of an accurate treatment plan. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can optimize the display content when displaying the generated 3D model on the medical staff's computer.

[0034] The providing unit can perform a treatment simulation based on the generated 3D model and select an appropriate treatment method. The treatment simulation can include, but is not limited to, the software used and the accuracy of the simulation. The appropriate treatment method can include, but is not limited to, the effect of the treatment and the patient's condition. The providing unit can also perform a treatment simulation based on the generated 3D model. The providing unit can also select an optimal treatment method based on the simulation results. For example, the providing unit can create a surgery plan based on the generated 3D model. It can also set targets for radiation therapy. Thus, by performing a treatment simulation based on the generated 3D model, an optimal treatment method can be selected. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, when performing a treatment simulation based on the generated 3D model, the providing unit can optimize the simulation results using AI.

[0035] The collection unit can analyze the patient's past medical history and select the optimal collection method. Past medical history includes, but is not limited to, medical records and test results. The optimal collection method includes, but is not limited to, collection means and collection conditions. For example, the collection unit can analyze the patient's past CT scan history and select the optimal imaging angle. The collection unit can also analyze the patient's past MRI history and select the optimal imaging time. The collection unit can also analyze the patient's past medical history and select the optimal collection device. For example, the collection unit can select the optimal imaging angle based on the patient's past CT scan history. The collection unit can also select the optimal imaging time based on the MRI history. In this way, the optimal collection method can be selected by analyzing the patient's past medical history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's past medical history data into the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can filter medical image data based on the patient's current health condition and symptoms when collecting the data. Examples of filtering include, but are not limited to, filtering conditions and algorithms. For example, the collection unit evaluates the patient's current health condition and collects only necessary image data. The collection unit can also prioritize collection of image data of specific areas based on the patient's symptoms. The collection unit can also adjust the resolution of the image data to be collected according to the patient's health condition. For example, the collection unit evaluates the patient's current health condition and collects only necessary image data. The collection unit can also prioritize collection of image data of specific areas based on the symptoms. This enables efficient data collection by collecting only necessary image data based on the patient's current health condition and symptoms. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the patient's health condition data into the generation AI and have the generation AI perform filtering.

[0037] When collecting medical image data, the collection unit can select an appropriate collection means depending on the patient's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of collection means include, but are not limited to, the device used and the collection method. For example, if the patient desires voice input, the collection unit can select the collection means using voice recognition technology. Furthermore, if the patient desires text input, the collection unit can select the collection means using text analysis technology. Furthermore, if the patient desires image input, the collection unit can select the collection means using image recognition technology. For example, if the patient desires voice input, the collection unit can select the collection means using voice recognition technology. If the patient desires text input, the collection unit can select the collection means using text analysis technology. This improves patient convenience by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's voice data into a generation AI and have the generation AI select the collection means.

[0038] When collecting medical image data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical data related to that area. Furthermore, if the patient is traveling, the collection unit can prioritize collecting medical data from the travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting medical data related to the new residence. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical data related to that area. If the patient is traveling, the collection unit can prioritize collecting medical data from the travel destination. In this way, highly relevant data can be prioritized by taking the patient's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the patient's social media activity and collect relevant data when collecting medical image data. Social media activity includes, but is not limited to, the content of posts and the frequency of activity. For example, the collection unit may collect relevant medical data based on health information shared by the patient on social media. The collection unit may also identify health concerns from the patient's social media activity and collect relevant data. The collection unit may also collect relevant medical data based on the activity of the patient's friends on social media. For example, the collection unit may collect relevant medical data based on health information shared by the patient on social media. Health concerns may also be identified from the social media activity and collected relevant data. Thus, relevant medical data can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the patient's social media data into a generation AI and cause the generation AI to collect relevant data.

[0040] When collecting medical image data, the collection unit can customize the collection method by reflecting past patient feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, the collection unit can adjust the collection method based on feedback previously provided by the patient. The collection unit can also optimize the collection method based on the patient's past feedback. The collection unit can also select the type of image data to collect by reflecting the patient's feedback. For example, the collection unit can adjust the collection method based on feedback previously provided by the patient. The collection unit can also optimize the collection method based on the past feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input patient feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the abnormality. Examples of importance include, but are not limited to, a doctor's judgment or a diagnosis result. Examples of detail include, but are not limited to, the depth of analysis and the accuracy of display. For example, the analysis unit provides detailed analysis results for important abnormalities. The analysis unit can also provide simplified analysis results for less important abnormalities. The analysis unit can also determine the priority of the analysis based on the importance of the abnormality. For example, the analysis unit can provide detailed analysis results for important abnormalities. The analysis unit can also provide simplified analysis results for less important abnormalities. By adjusting the level of detail of the analysis based on the importance of the abnormality, important information can be provided preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the abnormality to the generation AI and have the generation AI adjust the level of detail.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of medical image data. Examples of categories include, but are not limited to, types of disease and types of images. Examples of analysis algorithms include, but are not limited to, deep learning and machine learning. For example, the analysis unit applies a specific algorithm to CT scan data for analysis. The analysis unit can also apply a different algorithm to MRI data for analysis. The analysis unit can also apply a dedicated algorithm to ultrasound data for analysis. For example, the analysis unit can apply a specific algorithm to CT scan data for analysis. The analysis unit can also apply a different algorithm to MRI data for analysis. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of medical image data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input medical image data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. Past analysis results include, but are not limited to, diagnostic history, test results, etc. The analysis unit, for example, corrects the current analysis results based on the patient's past analysis results. The analysis unit can also extract specific patterns from the patient's past analysis results and reflect them in the current analysis. The analysis unit can also optimize the analysis algorithm by referring to the patient's past analysis results. For example, the analysis unit corrects the current analysis results based on the patient's past analysis results. It can also extract specific patterns from the past analysis results and reflect them in the current analysis. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the acquisition date of the medical image data. Examples of acquisition dates include, but are not limited to, the latest data and past data. For example, the analysis unit prioritizes analysis of the latest medical image data. Furthermore, the analysis unit can also analyze older medical image data as needed. Furthermore, the analysis unit can determine the order of analysis based on the acquisition date of the medical image data. For example, the analysis unit prioritizes analysis of the latest medical image data. Furthermore, the analysis unit can also analyze older medical image data as needed. Thus, by determining the analysis priority based on the acquisition date of the medical image data, the latest information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the acquisition date of the medical image data into the generation AI and have the generation AI determine the priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the medical image data. Relevance includes, but is not limited to, correlation and causal relationships. For example, the analysis unit prioritizes analysis of highly relevant medical image data. The analysis unit can also postpone analysis of less relevant medical image data. The analysis unit can also adjust the order of analysis based on the relevance of the medical image data. For example, the analysis unit prioritizes analysis of highly relevant medical image data. The analysis unit can also postpone analysis of less relevant medical image data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the medical image data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the medical image data into the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, medical knowledge, technical knowledge, etc. Examples of technical terminology include, but are not limited to, medical terms, technical terms, etc. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terminology. Furthermore, if the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the patient's level of expertise. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terminology. If the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. By adjusting the use of technical terminology in the analysis according to the patient's level of expertise, analysis results that are easy for the patient to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0047] When generating a 3D model, the generation unit can adjust the level of detail of the generated 3D model based on the shape and position of the anomaly. The level of detail includes, but is not limited to, the depth of generation and the accuracy of display. For example, the generation unit generates a detailed 3D model for an important anomaly. The generation unit can also generate a simplified 3D model for an anomaly of low importance. The generation unit can also adjust the level of detail of the 3D model based on the shape and position of the anomaly. For example, the generation unit generates a detailed 3D model for an important anomaly. The generation unit can also generate a simplified 3D model for an anomaly of low importance. By adjusting the level of detail of the generated 3D model based on the shape and position of the anomaly, important information can be provided in detail. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data of the anomaly to the generation AI and cause the generation AI to adjust the level of detail.

[0048] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the abnormality. Examples of categories include, but are not limited to, types of disease and types of images. Examples of generation algorithms include, but are not limited to, deep learning and machine learning. For example, the generation unit applies a specific generation algorithm to a tumor to generate a 3D model. The generation unit can also apply a different generation algorithm to a vascular abnormality to generate a 3D model. The generation unit can also apply a dedicated generation algorithm to a bone abnormality to generate a 3D model. For example, the generation unit can apply a specific generation algorithm to a tumor to generate a 3D model. The generation unit can also apply a different generation algorithm to a vascular abnormality to generate a 3D model. This improves the accuracy of generation by applying different generation algorithms depending on the category of the abnormality. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the abnormality into the generation AI and cause the generation AI to apply the generation algorithm.

[0049] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to past 3D model generation results for the patient. Past 3D model generation results include, but are not limited to, generation history, evaluation results, etc. For example, the generation unit corrects the current 3D model based on the past 3D model generation results for the patient. The generation unit can also extract specific patterns from the past 3D model generation results for the patient and reflect them in the current 3D model. The generation unit can also optimize the generation algorithm by referring to the past 3D model generation results for the patient. For example, the generation unit corrects the current 3D model based on the past 3D model generation results for the patient. The generation unit can also extract specific patterns from the past 3D model generation results and reflect them in the current 3D model. This improves the accuracy of the generation by referring to the past 3D model generation results for the patient. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can input the patient's past 3D model generation result data into the generation AI and have the generation AI improve the accuracy of the generation.

[0050] When generating a 3D model, the generation unit can determine the generation priority based on the acquisition date of the medical image data. Examples of acquisition dates include, but are not limited to, the latest data and past data. For example, the generation unit prioritizes generating a 3D model based on the latest medical image data. The generation unit can also generate 3D models for older medical image data as needed. The generation unit can also determine the order of 3D model generation based on the acquisition date of the medical image data. For example, the generation unit prioritizes generating a 3D model based on the latest medical image data. For older medical image data, the generation unit can also generate 3D models as needed. By determining the generation priority based on the acquisition date of the medical image data, the latest information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the acquisition date of the medical image data into the generation AI and have the generation AI determine the priority.

[0051] When generating a 3D model, the generation unit can adjust the generation order based on the relevance of the medical image data. Relevance includes, but is not limited to, correlation and causal relationships. For example, the generation unit prioritizes generating 3D models based on highly relevant medical image data. The generation unit can also postpone generating less relevant medical image data. The generation unit can also adjust the generation order of 3D models based on the relevance of the medical image data. For example, the generation unit prioritizes generating 3D models based on highly relevant medical image data. The generation unit can also postpone generating less relevant medical image data. This enables efficient generation by adjusting the generation order based on the relevance of the medical image data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input relevance data of the medical image data into the generation AI and have the generation AI adjust the generation order.

[0052] When generating a 3D model, the generation unit can adjust the use of technical terminology in the generation according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, medical knowledge, technical knowledge, etc. Examples of technical terminology include, but are not limited to, medical terms, technical terms, etc. For example, if the patient has technical knowledge, the generation unit generates a 3D model using detailed technical terminology. Alternatively, if the patient does not have technical knowledge, the generation unit can generate a 3D model using simple language. The generation unit can also adjust the way the 3D model is expressed according to the patient's level of expertise. For example, if the patient has technical knowledge, the generation unit can generate a 3D model using detailed technical terminology. Alternatively, if the patient does not have technical knowledge, the generation unit can generate a 3D model using simple language. By adjusting the use of technical terminology in the generation according to the patient's level of expertise, a 3D model that is easy for the patient to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit can input the patient's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0053] When providing a 3D model, the providing unit can select the optimal delivery method by referring to the patient's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. The providing unit can, for example, adjust the delivery method based on feedback previously provided by the patient. The providing unit can also select the optimal delivery means from the patient's past feedback. The providing unit can also select the type of information to be provided by reflecting the patient's feedback. For example, the providing unit can adjust the delivery method based on feedback previously provided by the patient. The optimal delivery means can also be selected from the past feedback. This allows the optimal delivery method to be selected by referring to the patient's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to select the delivery method.

[0054] When providing a 3D model, the providing unit can customize the provided content based on the patient's current health condition. Examples of health conditions include, but are not limited to, diagnostic results and symptoms. For example, the providing unit can evaluate the patient's current health condition and provide only the necessary information. The providing unit can also prioritize providing information on specific body parts based on the patient's symptoms. The providing unit can also adjust the level of detail of the provided information depending on the patient's health condition. For example, the providing unit can evaluate the patient's current health condition and provide only the necessary information. It can also prioritize providing information on specific body parts based on the symptoms. This allows the provided content to be customized based on the patient's current health condition, thereby providing the necessary information appropriately. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the patient's health condition data into the generating AI and cause the generating AI to customize the provided content.

[0055] When providing a 3D model, the providing unit can improve the providing method by reflecting patient feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, when a patient provides feedback on the provided information, the providing unit improves the providing method based on that feedback. The providing unit can also optimize the providing means based on the patient feedback. The providing unit can also select the type of information to be provided by reflecting patient feedback. For example, when a patient provides feedback on the provided information, the providing unit improves the providing method based on that feedback. The providing means can also be optimized based on the feedback. Thus, the providing method can be optimized by reflecting patient feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into a generating AI and cause the generating AI to improve the providing method.

[0056] When providing a 3D model, the providing unit can select the optimal providing method by taking into account the patient's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, if the patient lives in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the patient is traveling, the providing unit can prioritize providing information about the patient's travel destination. Furthermore, if the patient is planning to move, the providing unit can prioritize providing information related to the patient's new residence. For example, if the patient lives in a specific area, the providing unit can prioritize providing information related to that area. If the patient is traveling, the providing unit can prioritize providing information about the patient's travel destination. This allows for providing highly relevant information by taking the patient's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the patient's geographical location information into the generating AI and cause the generating AI to select the optimal providing method.

[0057] When providing the 3D model, the providing unit can analyze the patient's social media activity to customize the provided content. Social media activity includes, but is not limited to, for example, the content of posts and the frequency of activity. For example, the providing unit can provide relevant information based on health information shared by the patient on social media. The providing unit can also identify health concerns from the patient's social media activity and provide relevant information. The providing unit can also provide relevant information based on the activity of the patient's friends on social media. For example, the providing unit can provide relevant information based on health information shared by the patient on social media. Health concerns can also be identified from social media activity and provided relevant information. In this way, relevant information can be provided by analyzing the patient's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the patient's social media data into the generating AI and have the generating AI customize the provided content.

[0058] When providing a 3D model, the providing unit can customize the providing method by reflecting the patient's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, the providing unit can adjust the providing method based on feedback previously provided by the patient. The providing unit can also optimize the providing means based on the patient's past feedback. The providing unit can also select the type of information to provide by reflecting the patient's feedback. For example, the providing unit can adjust the providing method based on feedback previously provided by the patient. The providing unit can also optimize the providing means based on the past feedback. In this way, the providing method can be optimized by reflecting the patient's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to customize the providing method.

[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 collection unit can collect vital sign data of a patient in real time and provide it to the analysis unit. For example, the collection unit collects vital sign data such as heart rate, blood pressure, and body temperature. The collection unit can also collect the patient's respiratory rate and oxygen saturation. Furthermore, the collection unit can collect the patient's exercise amount and sleep patterns. In this way, the collection unit can collect vital sign data of a patient in real time and provide it to the analysis unit, thereby enabling a more detailed understanding of the patient's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vital sign data to a generation AI and have the generation AI improve the accuracy of the analysis.

[0061] When generating a 3D model of an abnormality, the generation unit can improve the accuracy of the model by taking into account the patient's past treatment history. For example, the generation unit can more accurately reflect the current shape of the abnormality based on the patient's past surgical history. The generation unit can also precisely identify the location of the abnormality based on data from past radiation therapy. Furthermore, the generation unit can also reflect changes in the abnormality in the model by taking into account the effects of past drug treatments. In this way, the generation unit can generate a more accurate 3D model by taking into account the patient's past treatment history. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the patient's treatment history data into the generation AI and have the generation AI improve the accuracy of the model.

[0062] The collection unit can collect lifestyle habit data of the patient and provide it to the analysis unit. For example, the collection unit can collect the patient's dietary habits and exercise habits. The collection unit can also collect the patient's sleep patterns and stress levels. Furthermore, the collection unit can collect the patient's smoking and drinking habits. In this way, the collection unit can collect the patient's lifestyle habit data and provide it to the analysis unit, thereby enabling a more comprehensive understanding of the patient's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the lifestyle habit data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0063] When generating a 3D model of an abnormality, the generation unit can improve the accuracy of the model by taking into account the patient's genetic information. For example, the generation unit evaluates the risk of abnormality occurrence based on the patient's genetic data and reflects the evaluation in the model. The generation unit can also precisely identify the shape and location of abnormalities caused by genetic factors. Furthermore, the generation unit can also reflect a predicted progression of the abnormality in the model by taking into account the genetic information. This allows the generation unit to generate a more accurate 3D model by taking into account the patient's genetic information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the patient's genetic information data into the generation AI and cause the generation AI to improve the accuracy of the model.

[0064] The collection unit can collect environmental data about the patient and provide it to the analysis unit. For example, the collection unit collects data about the patient's living environment and work environment. The collection unit can also collect data about the patient's surroundings, such as temperature, humidity, and air quality. Furthermore, the collection unit can collect noise levels and light intensity in the patient's living environment. By collecting environmental data about the patient and providing it to the analysis unit, the collection unit can obtain a more comprehensive understanding of the patient's health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit acquires medical image data. Medical image data includes, for example, CT scans, MRIs, and X-ray images. The acquisition unit uses a CT scan device, an MRI device, or an X-ray device to obtain cross-sectional and detailed images of the patient's body, as well as images of bones and organs. Step 2: The analysis unit uses the generation AI to analyze the medical image data collected by the collection unit and identify abnormalities. Abnormalities include, for example, tumors, inflammation, and fractures. The generation AI analyzes the medical image data using deep learning technology, machine learning technology, and neural networks to identify abnormalities. Step 3: The generator generates a 3D model based on the shape and location of the abnormality identified by the analyzer. The 3D model includes, for example, the size and location of the tumor and the detailed structure of the vascular abnormality. The generator generates a 3D model based on the shape and location of the identified tumor, vascular abnormality, and fracture. Step 4: The providing unit provides the 3D model generated by the generating unit to medical staff. The providing unit displays the generated 3D model on the medical staff's computer, tablet, or smartphone, thereby supporting medical staff in dealing with difficult diagnoses.

[0067] (Example 2) A medical support system according to an embodiment of the present invention automatically collects medical image data, analyzes it using a generation AI, identifies abnormalities, and generates and provides a 3D model. The medical support system collects medical image data, analyzes it using a generation AI, identifies abnormalities, and then generates a 3D model to provide to medical staff. For example, the medical support system collects a patient's medical image data, including image data such as CT scans and MRIs. The medical support system then uses a generation AI to analyze the collected image data and identify abnormalities. The generation AI uses image recognition technology to quickly and accurately detect abnormalities, such as tumors and vascular abnormalities. The medical support system then generates a 3D model based on the identified abnormalities. The generation AI creates a detailed 3D model based on the shape and position of the identified abnormalities. This 3D model is provided in a format that is visually easy for medical staff to understand. For example, it may include the size and location of the tumor and the detailed structure of vascular abnormalities. Furthermore, the medical support system uses the generated 3D model to promote mutual understanding between the patient and medical staff. Medical staff use the 3D model to explain the diagnosis to the patient and propose a treatment plan. This allows patients to better understand their condition and facilitates communication with medical staff. Finally, the medical support system creates an accurate treatment plan based on the generated 3D model. Medical staff use the 3D model to simulate treatment and select the optimal treatment method. For example, this includes planning surgery and setting radiation therapy targets. This allows the medical support system to help medical staff deal with complex diagnoses, promote mutual understanding with patients, and develop accurate treatment plans. For example, medical staff can use the generated 3D model to explain diagnosis results to patients and propose treatment plans. This allows patients to better understand their condition and facilitates communication with medical staff.Medical staff can also use the 3D model to simulate treatment and select the optimal treatment method, improving the accuracy of treatment and reducing the burden on patients.

[0068] A medical support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects medical image data. Examples of medical image data include, but are not limited to, CT scans, MRIs, and X-ray images. The collection unit collects medical image data using, for example, a CT scanning device. The collection unit can also collect medical image data using an MRI device. The collection unit can also collect medical image data using an X-ray device. For example, the collection unit can acquire cross-sectional images of a patient's body using a CT scanning device. Detailed images of the patient's body can also be acquired using an MRI device. Images of bones and organs can also be acquired using an X-ray device. The analysis unit uses a generation AI to analyze the medical image data collected by the collection unit and identify abnormalities. Examples of abnormalities include, but are not limited to, tumors, inflammation, and fractures. For example, the generation AI can analyze the medical image data using deep learning technology and identify abnormalities. The generation AI can also analyze the medical image data using machine learning technology and identify abnormalities. The generation AI can also analyze medical image data using a neural network to identify abnormalities. For example, the generation AI can use deep learning technology to identify the location and size of a tumor. It can also use machine learning technology to identify the extent of inflammation. It can also use a neural network to identify the location of a fracture. The generation unit generates a 3D model based on the shape and location of the abnormality identified by the analysis unit. The 3D model includes, but is not limited to, the size and location of a tumor and the detailed structure of vascular abnormalities. For example, the generation unit generates a 3D model based on the shape and location of the identified tumor. It can also generate a 3D model based on the shape and location of the identified vascular abnormality. It can also generate a 3D model based on the shape and location of the identified fracture. For example, the generation unit generates a 3D model that shows the size and location of a tumor in detail. It can also generate a 3D model that shows the detailed structure of vascular abnormalities. It can also generate a 3D model that shows the location and shape of a fracture. The provision unit provides the 3D model generated by the generation unit to medical staff.The providing unit, for example, displays the generated 3D model on a medical staff member's computer. The providing unit can also display the generated 3D model on a medical staff member's tablet. The providing unit can also display the generated 3D model on a medical staff member's smartphone. For example, the providing unit displays the generated 3D model on a medical staff member's computer screen. The providing unit can also display the generated 3D model on a tablet screen. The providing unit can also display the generated 3D model on a smartphone screen. This enables the medical support system according to the embodiment to support medical staff in dealing with difficult diagnoses. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can optimize the display content using AI when displaying the generated 3D model on the medical staff member's computer.

[0069] The acquisition unit can acquire medical image data using CT scans or MRIs. CT scans include, but are not limited to, helical CTs and multi-slice CTs. MRIs include, but are not limited to, T1-weighted images, T2-weighted images, and diffusion-weighted images. The acquisition unit can acquire helical CTs using a CT scanning device. The acquisition unit can also acquire multi-slice CTs. The acquisition unit can also acquire T1-weighted images using an MRI device. For example, the acquisition unit can acquire cross-sectional images of the patient's body using a CT scanning device. An MRI device can also acquire detailed images of the patient's body. By collecting medical image data such as CT scans and MRIs, detailed medical information can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input medical image data acquired using a CT scanning device into a generation AI and have the generation AI analyze the image data.

[0070] The analysis unit can analyze medical image data using AI and identify abnormalities. Examples of AI include, but are not limited to, deep learning, machine learning, and neural networks. The analysis unit can analyze medical image data using, for example, deep learning technology and identify abnormalities. The analysis unit can also analyze medical image data using machine learning technology and identify abnormalities. The analysis unit can also analyze medical image data using neural networks and identify abnormalities. For example, the analysis unit can use deep learning technology to identify the location and size of a tumor. Machine learning technology can also identify the extent of inflammation. Neural networks can also identify the location of a fracture. As a result, the use of AI improves the accuracy of medical image data analysis, allowing abnormalities to be identified quickly and accurately. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input medical image data to the generation AI and have the generation AI identify abnormalities.

[0071] The generation unit can generate a 3D model based on the shape and position of the identified abnormality. Examples of shapes include, but are not limited to, size and shape type (e.g., spherical, elliptical, etc.). Examples of positions include, but are not limited to, coordinate systems and reference points. The generation unit can generate a 3D model based on, for example, the shape and position of an identified tumor. The generation unit can also generate a 3D model based on the shape and position of an identified vascular abnormality. The generation unit can also generate a 3D model based on the shape and position of an identified fracture. For example, the generation unit can generate a 3D model that shows the size and position of a tumor in detail. It can also generate a 3D model that shows the detailed structure of a vascular abnormality. It can also generate a 3D model that shows the position and shape of a fracture. By generating a 3D model based on the shape and position of an abnormality, medical staff can be provided with information in a form that is visually easy to understand. Some or all of the above-described processing by the generation unit can be performed, for example, using a generation AI, or without using a generation AI. For example, the generation unit can input data on the identified abnormality location into the generation AI and cause the generation AI to generate a 3D model.

[0072] The providing unit can provide the generated 3D model to medical staff to promote mutual understanding with the patient. Mutual understanding includes, but is not limited to, communication means and methods for assessing the level of understanding. For example, the providing unit can display the generated 3D model on the medical staff's computer. The providing unit can also display the generated 3D model on the medical staff's tablet. The providing unit can also display the generated 3D model on the medical staff's smartphone. For example, the providing unit can display the generated 3D model on the medical staff's computer screen, on a tablet screen, or on a smartphone screen. Providing the generated 3D model to the medical staff promotes mutual understanding with the patient and enables the creation of an accurate treatment plan. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can optimize the display content when displaying the generated 3D model on the medical staff's computer.

[0073] The providing unit can perform a treatment simulation based on the generated 3D model and select an appropriate treatment method. The treatment simulation can include, but is not limited to, the software used and the accuracy of the simulation. The appropriate treatment method can include, but is not limited to, the effect of the treatment and the patient's condition. The providing unit can also perform a treatment simulation based on the generated 3D model. The providing unit can also select an optimal treatment method based on the simulation results. For example, the providing unit can create a surgery plan based on the generated 3D model. It can also set targets for radiation therapy. Thus, by performing a treatment simulation based on the generated 3D model, an optimal treatment method can be selected. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, when performing a treatment simulation based on the generated 3D model, the providing unit can optimize the simulation results using AI.

[0074] The collection unit can estimate the patient's emotions and adjust the timing of medical image data collection based on the estimated patient emotions. Examples of emotions include, but are not limited to, facial expression recognition and voice analysis. Examples of collection timing include, but are not limited to, the time of day and the patient's condition. For example, if the patient is nervous, the collection unit collects medical image data after allowing the patient time to relax. Furthermore, if the patient is relaxed, the collection unit can collect medical image data immediately. Furthermore, if the patient is anxious, the collection unit can collect medical image data after counseling. For example, the collection unit can capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It is also possible to record the patient's voice and estimate the emotion using voice analysis technology. This can reduce the burden on the patient by adjusting the timing of medical image data collection according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0075] The collection unit can analyze the patient's past medical history and select the optimal collection method. Past medical history includes, but is not limited to, medical records and test results. The optimal collection method includes, but is not limited to, collection means and collection conditions. For example, the collection unit can analyze the patient's past CT scan history and select the optimal imaging angle. The collection unit can also analyze the patient's past MRI history and select the optimal imaging time. The collection unit can also analyze the patient's past medical history and select the optimal collection device. For example, the collection unit can select the optimal imaging angle based on the patient's past CT scan history. The collection unit can also select the optimal imaging time based on the MRI history. In this way, the optimal collection method can be selected by analyzing the patient's past medical history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's past medical history data into the generation AI and cause the generation AI to select the optimal collection method.

[0076] The collection unit can filter medical image data based on the patient's current health condition and symptoms when collecting the data. Examples of filtering include, but are not limited to, filtering conditions and algorithms. For example, the collection unit can evaluate the patient's current health condition and collect only necessary image data. The collection unit can also prioritize collection of image data of specific areas based on the patient's symptoms. The collection unit can also adjust the resolution of the image data to be collected according to the patient's health condition. For example, the collection unit can evaluate the patient's current health condition and collect only necessary image data. The collection unit can also prioritize collection of image data of specific areas based on the symptoms. This enables efficient data collection by collecting only necessary image data based on the patient's current health condition and symptoms. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the patient's health condition data into the generation AI and have the generation AI perform filtering.

[0077] When collecting medical image data, the collection unit can select an appropriate collection means depending on the patient's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of collection means include, but are not limited to, the device used and the collection method. For example, if the patient desires voice input, the collection unit can select the collection means using voice recognition technology. Furthermore, if the patient desires text input, the collection unit can select the collection means using text analysis technology. Furthermore, if the patient desires image input, the collection unit can select the collection means using image recognition technology. For example, if the patient desires voice input, the collection unit can select the collection means using voice recognition technology. If the patient desires text input, the collection unit can select the collection means using text analysis technology. This improves patient convenience by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's voice data into a generation AI and have the generation AI select the collection means.

[0078] The collection unit can estimate the patient's emotions and determine the priority of medical image data to be collected based on the estimated patient's emotions. Priorities include, but are not limited to, importance and urgency. For example, if the patient is nervous, the collection unit can prioritize collecting important image data. Furthermore, if the patient is relaxed, the collection unit can also collect detailed image data. Furthermore, if the patient is anxious, the collection unit can prioritize collecting image data necessary to provide a sense of security. For example, if the patient is nervous, the collection unit can prioritize collecting important image data. Furthermore, if the patient is relaxed, the collection unit can also collect detailed image data. Thus, by determining the priority of medical image data to be collected according to the patient's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input patient emotion data into the generation AI and have the generation AI determine the priorities.

[0079] When collecting medical image data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical data related to that area. Furthermore, if the patient is traveling, the collection unit can prioritize collecting medical data from the travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting medical data related to the new residence. For example, if the patient lives in a specific area, the collection unit can prioritize collecting medical data related to that area. If the patient is traveling, the collection unit can prioritize collecting medical data from the travel destination. In this way, highly relevant data can be prioritized by taking the patient's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit may analyze the patient's social media activity and collect relevant data when collecting medical image data. Social media activity includes, but is not limited to, the content of posts and the frequency of activity. For example, the collection unit may collect relevant medical data based on health information shared by the patient on social media. The collection unit may also identify health concerns from the patient's social media activity and collect relevant data. The collection unit may also collect relevant medical data based on the activity of the patient's friends on social media. For example, the collection unit may collect relevant medical data based on health information shared by the patient on social media. Health concerns may also be identified from the social media activity and collected relevant data. Thus, relevant medical data can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the patient's social media data into a generation AI and cause the generation AI to collect relevant data.

[0081] When collecting medical image data, the collection unit can customize the collection method by reflecting past patient feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, the collection unit can adjust the collection method based on feedback previously provided by the patient. The collection unit can also optimize the collection method based on the patient's past feedback. The collection unit can also select the type of image data to collect by reflecting the patient's feedback. For example, the collection unit can adjust the collection method based on feedback previously provided by the patient. The collection unit can also optimize the collection method based on the past feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input patient feedback data into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. Presentation methods include, but are not limited to, graph display, text display, and the like. For example, if the patient is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is anxious, the analysis unit can prioritize providing information necessary to reassure the patient. For example, if the patient is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. By adjusting the presentation method of the analysis according to the patient's emotions, it is possible to provide analysis results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's emotional data into the generation AI and have the generation AI adjust the expression method.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the abnormality. Examples of importance include, but are not limited to, a doctor's judgment or a diagnosis result. Examples of detail include, but are not limited to, the depth of analysis and the accuracy of display. For example, the analysis unit provides detailed analysis results for important abnormalities. The analysis unit can also provide simplified analysis results for less important abnormalities. The analysis unit can also determine the priority of the analysis based on the importance of the abnormality. For example, the analysis unit can provide detailed analysis results for important abnormalities. The analysis unit can also provide simplified analysis results for less important abnormalities. By adjusting the level of detail of the analysis based on the importance of the abnormality, important information can be provided preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the abnormality to the generation AI and have the generation AI adjust the level of detail.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of medical image data. Examples of categories include, but are not limited to, types of disease and types of images. Examples of analysis algorithms include, but are not limited to, deep learning and machine learning. For example, the analysis unit applies a specific algorithm to CT scan data for analysis. The analysis unit can also apply a different algorithm to MRI data for analysis. The analysis unit can also apply a dedicated algorithm to ultrasound data for analysis. For example, the analysis unit can apply a specific algorithm to CT scan data for analysis. The analysis unit can also apply a different algorithm to MRI data for analysis. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of medical image data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input medical image data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. Past analysis results include, but are not limited to, diagnostic history, test results, etc. The analysis unit, for example, corrects the current analysis results based on the patient's past analysis results. The analysis unit can also extract specific patterns from the patient's past analysis results and reflect them in the current analysis. The analysis unit can also optimize the analysis algorithm by referring to the patient's past analysis results. For example, the analysis unit corrects the current analysis results based on the patient's past analysis results. It can also extract specific patterns from the past analysis results and reflect them in the current analysis. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can input the patient's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0086] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. Examples of the length of the analysis include, but are not limited to, the analysis time and the amount of data. For example, if the patient is nervous, the analysis unit can provide a short, concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is anxious, the analysis unit can prioritize information necessary to reassure the patient. For example, if the patient is nervous, the analysis unit can provide a short, concise analysis result. If the patient is relaxed, the analysis unit can provide a detailed analysis result. By adjusting the length of the analysis according to the patient's emotions, analysis results that are less burdensome for the patient can be provided. The emotion estimation is achieved using, for example, an emotion engine or a generative AI, using an emotion estimation function. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input patient emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the acquisition date of the medical image data. Examples of acquisition dates include, but are not limited to, the latest data and past data. For example, the analysis unit prioritizes analysis of the latest medical image data. Furthermore, the analysis unit can also analyze older medical image data as needed. Furthermore, the analysis unit can determine the order of analysis based on the acquisition date of the medical image data. For example, the analysis unit prioritizes analysis of the latest medical image data. Furthermore, the analysis unit can also analyze older medical image data as needed. Thus, by determining the analysis priority based on the acquisition date of the medical image data, the latest information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the acquisition date of the medical image data into the generation AI and have the generation AI determine the priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the medical image data. Relevance includes, but is not limited to, correlation and causal relationships. For example, the analysis unit prioritizes analysis of highly relevant medical image data. The analysis unit can also postpone analysis of less relevant medical image data. The analysis unit can also adjust the order of analysis based on the relevance of the medical image data. For example, the analysis unit prioritizes analysis of highly relevant medical image data. The analysis unit can also postpone analysis of less relevant medical image data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the medical image data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the medical image data into the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, medical knowledge, technical knowledge, etc. Examples of technical terminology include, but are not limited to, medical terms, technical terms, etc. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terminology. Furthermore, if the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the patient's level of expertise. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terminology. If the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. By adjusting the use of technical terminology in the analysis according to the patient's level of expertise, analysis results that are easy for the patient to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0090] The generation unit can estimate the patient's emotions and adjust the method for generating the 3D model based on the estimated patient's emotions. Examples of the generation method include, but are not limited to, the software used and the accuracy of generation. For example, if the patient is nervous, the generation unit can generate a simple 3D model. Also, if the patient is relaxed, the generation unit can generate a detailed 3D model. Also, if the patient is anxious, the generation unit can generate a 3D model containing information necessary to provide a sense of security. For example, if the patient is nervous, the generation unit can generate a simple 3D model. Also, if the patient is relaxed, the generation unit can generate a detailed 3D model. This allows for adjusting the method for generating the 3D model according to the patient's emotions, thereby providing a 3D model that is easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input patient emotion data into the generation AI and cause the generation AI to adjust the generation method.

[0091] When generating a 3D model, the generation unit can adjust the level of detail of the generated 3D model based on the shape and position of the anomaly. The level of detail includes, but is not limited to, the depth of generation and the accuracy of display. For example, the generation unit generates a detailed 3D model for an important anomaly. The generation unit can also generate a simplified 3D model for an anomaly of low importance. The generation unit can also adjust the level of detail of the 3D model based on the shape and position of the anomaly. For example, the generation unit generates a detailed 3D model for an important anomaly. The generation unit can also generate a simplified 3D model for an anomaly of low importance. By adjusting the level of detail of the generated 3D model based on the shape and position of the anomaly, important information can be provided in detail. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data of the anomaly to the generation AI and cause the generation AI to adjust the level of detail.

[0092] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the abnormality. Examples of categories include, but are not limited to, types of disease and types of images. Examples of generation algorithms include, but are not limited to, deep learning and machine learning. For example, the generation unit applies a specific generation algorithm to a tumor to generate a 3D model. The generation unit can also apply a different generation algorithm to a vascular abnormality to generate a 3D model. The generation unit can also apply a dedicated generation algorithm to a bone abnormality to generate a 3D model. For example, the generation unit can apply a specific generation algorithm to a tumor to generate a 3D model. The generation unit can also apply a different generation algorithm to a vascular abnormality to generate a 3D model. This improves the accuracy of generation by applying different generation algorithms depending on the category of the abnormality. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the abnormality into the generation AI and cause the generation AI to apply the generation algorithm.

[0093] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to past 3D model generation results for the patient. Past 3D model generation results include, but are not limited to, generation history, evaluation results, etc. For example, the generation unit corrects the current 3D model based on the past 3D model generation results for the patient. The generation unit can also extract specific patterns from the past 3D model generation results for the patient and reflect them in the current 3D model. The generation unit can also optimize the generation algorithm by referring to the past 3D model generation results for the patient. For example, the generation unit corrects the current 3D model based on the past 3D model generation results for the patient. The generation unit can also extract specific patterns from the past 3D model generation results and reflect them in the current 3D model. This improves the accuracy of the generation by referring to the past 3D model generation results for the patient. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can input the patient's past 3D model generation result data into the generation AI and have the generation AI improve the accuracy of the generation.

[0094] The generation unit can estimate the patient's emotions and adjust the display method of the 3D model based on the estimated patient's emotions. Display methods include, but are not limited to, graph display and text display. For example, if the patient is nervous, the generation unit provides a simple, highly visible display method. Furthermore, if the patient is relaxed, the generation unit can provide a display method including detailed information. Furthermore, if the patient is anxious, the generation unit can prioritize displaying information necessary to provide a sense of security. For example, if the patient is nervous, the generation unit provides a simple, highly visible display method. If the patient is relaxed, the generation unit can provide a display method including detailed information. This allows the display method of the 3D model to be adjusted according to the patient's emotions, thereby enabling a display that is easy for the patient to understand. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input patient emotion data into the generation AI and have the generation AI adjust the display method.

[0095] When generating a 3D model, the generation unit can determine the generation priority based on the acquisition date of the medical image data. Examples of acquisition dates include, but are not limited to, the latest data and past data. For example, the generation unit prioritizes generating a 3D model based on the latest medical image data. The generation unit can also generate 3D models for older medical image data as needed. The generation unit can also determine the order of 3D model generation based on the acquisition date of the medical image data. For example, the generation unit prioritizes generating a 3D model based on the latest medical image data. For older medical image data, the generation unit can also generate 3D models as needed. By determining the generation priority based on the acquisition date of the medical image data, the latest information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the acquisition date of the medical image data into the generation AI and have the generation AI determine the priority.

[0096] When generating a 3D model, the generation unit can adjust the generation order based on the relevance of the medical image data. Relevance includes, but is not limited to, correlation and causal relationships. For example, the generation unit prioritizes generating 3D models based on highly relevant medical image data. The generation unit can also postpone generating less relevant medical image data. The generation unit can also adjust the generation order of 3D models based on the relevance of the medical image data. For example, the generation unit prioritizes generating 3D models based on highly relevant medical image data. The generation unit can also postpone generating less relevant medical image data. This enables efficient generation by adjusting the generation order based on the relevance of the medical image data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input relevance data of the medical image data into the generation AI and have the generation AI adjust the generation order.

[0097] When generating a 3D model, the generation unit can adjust the use of technical terminology in the generation according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, medical knowledge, technical knowledge, etc. Examples of technical terminology include, but are not limited to, medical terms, technical terms, etc. For example, if the patient has technical knowledge, the generation unit generates a 3D model using detailed technical terminology. Alternatively, if the patient does not have technical knowledge, the generation unit can generate a 3D model using simple language. The generation unit can also adjust the way the 3D model is expressed according to the patient's level of expertise. For example, if the patient has technical knowledge, the generation unit can generate a 3D model using detailed technical terminology. Alternatively, if the patient does not have technical knowledge, the generation unit can generate a 3D model using simple language. By adjusting the use of technical terminology in the generation according to the patient's level of expertise, a 3D model that is easy for the patient to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit can input the patient's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0098] The providing unit can estimate the patient's emotions and adjust the method of providing the 3D model based on the estimated patient's emotions. Examples of providing methods include, but are not limited to, online and face-to-face provision. For example, if the patient is nervous, the providing unit can provide the 3D model in a simple, highly visible manner. Furthermore, if the patient is relaxed, the providing unit can provide the 3D model in a manner that includes detailed information. Furthermore, if the patient is anxious, the providing unit can prioritize providing information necessary to provide reassurance. For example, if the patient is nervous, the providing unit can provide the 3D model in a simple, highly visible manner. If the patient is relaxed, the providing unit can provide the 3D model in a manner that includes detailed information. This allows the 3D model to be provided in a way that is easy for the patient to understand by adjusting the method of providing the 3D model according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the patient's emotional data into the generating AI and have the generating AI adjust the providing method.

[0099] When providing a 3D model, the providing unit can select the optimal delivery method by referring to the patient's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. The providing unit can, for example, adjust the delivery method based on feedback previously provided by the patient. The providing unit can also select the optimal delivery means from the patient's past feedback. The providing unit can also select the type of information to be provided by reflecting the patient's feedback. For example, the providing unit can adjust the delivery method based on feedback previously provided by the patient. The optimal delivery means can also be selected from the past feedback. This allows the optimal delivery method to be selected by referring to the patient's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to select the delivery method.

[0100] When providing a 3D model, the providing unit can customize the provided content based on the patient's current health condition. Examples of health conditions include, but are not limited to, diagnostic results and symptoms. For example, the providing unit can evaluate the patient's current health condition and provide only the necessary information. The providing unit can also prioritize providing information on specific body parts based on the patient's symptoms. The providing unit can also adjust the level of detail of the provided information depending on the patient's health condition. For example, the providing unit can evaluate the patient's current health condition and provide only the necessary information. It can also prioritize providing information on specific body parts based on the symptoms. This allows the provided content to be customized based on the patient's current health condition, thereby providing the necessary information appropriately. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the patient's health condition data into the generating AI and cause the generating AI to customize the provided content.

[0101] When providing a 3D model, the providing unit can improve the providing method by reflecting patient feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, when a patient provides feedback on the provided information, the providing unit improves the providing method based on that feedback. The providing unit can also optimize the providing means based on the patient feedback. The providing unit can also select the type of information to be provided by reflecting patient feedback. For example, when a patient provides feedback on the provided information, the providing unit improves the providing method based on that feedback. The providing means can also be optimized based on the feedback. Thus, the providing method can be optimized by reflecting patient feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into a generating AI and cause the generating AI to improve the providing method.

[0102] The providing unit can estimate the patient's emotions and determine the order in which the 3D models are provided based on the estimated patient's emotions. Examples of the order of provision include, but are not limited to, importance and urgency. For example, if the patient is nervous, the providing unit can provide important information preferentially. Furthermore, if the patient is relaxed, the providing unit can provide detailed information. Furthermore, if the patient is feeling anxious, the providing unit can provide information necessary to reassure the patient preferentially. For example, if the patient is nervous, the providing unit can provide important information preferentially. Furthermore, if the patient is relaxed, the providing unit can provide detailed information. Thus, by determining the order in which the 3D models are provided based on the patient's emotions, important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input patient emotion data into the generating AI and have the generating AI determine the order of provision.

[0103] When providing a 3D model, the providing unit can select the optimal providing method by taking into account the patient's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, if the patient lives in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the patient is traveling, the providing unit can prioritize providing information about the patient's travel destination. Furthermore, if the patient is planning to move, the providing unit can prioritize providing information related to the patient's new residence. For example, if the patient lives in a specific area, the providing unit can prioritize providing information related to that area. If the patient is traveling, the providing unit can prioritize providing information about the patient's travel destination. This allows for providing highly relevant information by taking the patient's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the patient's geographical location information into the generating AI and cause the generating AI to select the optimal providing method.

[0104] When providing the 3D model, the providing unit can analyze the patient's social media activity to customize the provided content. Social media activity includes, but is not limited to, for example, the content of posts and the frequency of activity. For example, the providing unit can provide relevant information based on health information shared by the patient on social media. The providing unit can also identify health concerns from the patient's social media activity and provide relevant information. The providing unit can also provide relevant information based on the activity of the patient's friends on social media. For example, the providing unit can provide relevant information based on health information shared by the patient on social media. Health concerns can also be identified from social media activity and provided relevant information. In this way, relevant information can be provided by analyzing the patient's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the patient's social media data into the generating AI and have the generating AI customize the provided content.

[0105] When providing a 3D model, the providing unit can customize the providing method by reflecting the patient's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. For example, the providing unit can adjust the providing method based on feedback previously provided by the patient. The providing unit can also optimize the providing means based on the patient's past feedback. The providing unit can also select the type of information to provide by reflecting the patient's feedback. For example, the providing unit can adjust the providing method based on feedback previously provided by the patient. The providing unit can also optimize the providing means based on the past feedback. In this way, the providing method can be optimized by reflecting the patient's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation 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 image data using the camera 42 of the smart device 14. For example, the analysis unit can be realized by the identification processing unit 290 of the data processing device 12 and analyze the medical image data using a generation AI to identify abnormalities. For example, the generation unit can be realized by the identification processing unit 290 of the data processing device 12 and generate a 3D model based on the shape and position of the identified abnormality. For example, the provision unit can be realized by the control unit 46A of the smart device 14 and display the generated 3D model on a medical staff's computer or tablet. For example, the collection unit can collect medical image data using the camera 42 of the smart device 14. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the medical image data using a generation AI to identify abnormalities. For example, the generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a 3D model based on the shape and position of the identified abnormality. For example, the provision unit is realized by the control unit 46A of the smart device 14 and can display the generated 3D model on a computer or tablet of medical staff. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation 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 image data using the camera 42 of the smart glasses 214. For example, the analysis unit can be realized by the identification processing unit 290 of the data processing device 12 and analyze the medical image data using a generation AI to identify abnormalities. For example, the generation unit can be realized by the identification processing unit 290 of the data processing device 12 and generate a 3D model based on the shape and position of the identified abnormality. For example, the provision unit can be realized by the control unit 46A of the smart glasses 214 and display the generated 3D model on a medical staff member's computer or tablet. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation 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 image data using the camera 42 of the headset-type terminal 314. For example, the analysis unit can be realized by the identification processing unit 290 of the data processing device 12 and analyze the medical image data using a generation AI to identify abnormalities. For example, the generation unit can be realized by the identification processing unit 290 of the data processing device 12 and generate a 3D model based on the shape and position of the identified abnormality. For example, the provision unit can be realized by the control unit 46A of the headset-type terminal 314 and display the generated 3D model on a medical staff member's computer or tablet. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation 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 image data using the camera 42 of the robot 414. For example, the analysis unit can be realized by the identification processing unit 290 of the data processing device 12 and analyze the medical image data using a generation AI to identify abnormalities. For example, the generation unit can be realized by the identification processing unit 290 of the data processing device 12 and generate a 3D model based on the shape and position of the identified abnormality. For example, the provision unit can be realized by the control unit 46A of the robot 414 and display the generated 3D model on a medical staff member's computer or tablet.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can collect vital sign data of a patient in real time and provide it to the analysis unit. For example, the collection unit collects vital sign data such as heart rate, blood pressure, and body temperature. The collection unit can also collect the patient's respiratory rate and oxygen saturation. Furthermore, the collection unit can collect the patient's exercise amount and sleep patterns. In this way, the collection unit can collect vital sign data of a patient in real time and provide it to the analysis unit, thereby enabling a more detailed understanding of the patient's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vital sign data to a generation AI and have the generation AI improve the accuracy of the analysis.

[0108] The analysis unit can estimate the patient's emotions and adjust the display format of the analysis results based on the estimated patient emotions. For example, if the patient is nervous, the analysis unit can provide a simple, highly visible graph display. Furthermore, if the patient is relaxed, the analysis unit can also provide a detailed text display. Furthermore, if the patient is feeling anxious, the analysis unit can prioritize the display of information necessary to provide a sense of security. This allows for the provision of information that is easy for patients to understand by adjusting the display format of the analysis results according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the patient's emotion data into the generation AI and have the generation AI adjust the display format.

[0109] When generating a 3D model of an abnormality, the generation unit can improve the accuracy of the model by taking into account the patient's past treatment history. For example, the generation unit can more accurately reflect the current shape of the abnormality based on the patient's past surgical history. The generation unit can also precisely identify the location of the abnormality based on data from past radiation therapy. Furthermore, the generation unit can also reflect changes in the abnormality in the model by taking into account the effects of past drug treatments. In this way, the generation unit can generate a more accurate 3D model by taking into account the patient's past treatment history. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the patient's treatment history data into the generation AI and have the generation AI improve the accuracy of the model.

[0110] The providing unit can estimate the patient's emotions based on the generated 3D model and adjust the treatment plan proposal method based on the estimated emotions. For example, if the patient is nervous, the providing unit can propose a simple, highly visible treatment plan. Furthermore, if the patient is relaxed, the providing unit can also propose a detailed treatment plan. Furthermore, if the patient is feeling anxious, the providing unit can prioritize providing information necessary to reassure the patient. This allows for the provision of a treatment plan that is easy for the patient to understand by adjusting the treatment plan proposal method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the patient's emotion data into the generation AI and have the generation AI adjust the proposal method.

[0111] The collection unit can collect lifestyle habit data of the patient and provide it to the analysis unit. For example, the collection unit can collect the patient's dietary habits and exercise habits. The collection unit can also collect the patient's sleep patterns and stress levels. Furthermore, the collection unit can collect the patient's smoking and drinking habits. In this way, the collection unit can collect the patient's lifestyle habit data and provide it to the analysis unit, thereby enabling a more comprehensive understanding of the patient's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the lifestyle habit data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0112] The analysis unit can estimate the patient's emotions and determine the analysis priority based on the estimated patient's emotions. For example, if the patient is nervous, the analysis unit can prioritize analyzing important abnormalities. Furthermore, if the patient is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the patient is feeling anxious, the analysis unit can prioritize analyzing information necessary to reassure the patient. This allows important information to be provided quickly by determining the analysis priority according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data into the generative AI and have the generative AI determine the priority.

[0113] When generating a 3D model of an abnormality, the generation unit can improve the accuracy of the model by taking into account the patient's genetic information. For example, the generation unit evaluates the risk of abnormality occurrence based on the patient's genetic data and reflects the evaluation in the model. The generation unit can also precisely identify the shape and location of abnormalities caused by genetic factors. Furthermore, the generation unit can also reflect a predicted progression of the abnormality in the model by taking into account the genetic information. This allows the generation unit to generate a more accurate 3D model by taking into account the patient's genetic information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the patient's genetic information data into the generation AI and cause the generation AI to improve the accuracy of the model.

[0114] The providing unit can estimate the patient's emotions based on the generated 3D model and monitor the progress of treatment based on the estimated emotions. For example, if the patient is nervous, the providing unit can simply display the progress of treatment. Furthermore, if the patient is relaxed, the providing unit can also display detailed progress. Furthermore, if the patient is feeling anxious, the providing unit can prioritize displaying information necessary to reassure the patient. This allows for monitoring the progress of treatment according to the patient's emotions, thereby providing information that is easy for the patient to understand. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the patient's emotion data into the generation AI and have the generation AI adjust the monitoring.

[0115] The collection unit can collect environmental data about the patient and provide it to the analysis unit. For example, the collection unit collects data about the patient's living environment and work environment. The collection unit can also collect data about the patient's surroundings, such as temperature, humidity, and air quality. Furthermore, the collection unit can collect noise levels and light intensity in the patient's living environment. By collecting environmental data about the patient and providing it to the analysis unit, the collection unit can obtain a more comprehensive understanding of the patient's health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0116] The analysis unit can estimate the patient's emotions and adjust the notification method of the analysis results based on the estimated patient's emotions. For example, if the patient is nervous, the analysis unit can provide a simple, highly visible notification method. Furthermore, if the patient is relaxed, the analysis unit can also provide a detailed notification method. Furthermore, if the patient is feeling anxious, the analysis unit can prioritize the notification of information necessary to reassure the patient. This allows for the provision of information that is easy for the patient to understand by adjusting the notification method of the analysis results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's emotion data into the generation AI and have the generation AI adjust the notification method.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit acquires medical image data. Medical image data includes, for example, CT scans, MRIs, and X-ray images. The acquisition unit uses a CT scan device, an MRI device, or an X-ray device to obtain cross-sectional and detailed images of the patient's body, as well as images of bones and organs. Step 2: The analysis unit uses the generation AI to analyze the medical image data collected by the collection unit and identify abnormalities. Abnormalities include, for example, tumors, inflammation, and fractures. The generation AI analyzes the medical image data using deep learning technology, machine learning technology, and neural networks to identify abnormalities. Step 3: The generator generates a 3D model based on the shape and location of the abnormality identified by the analyzer. The 3D model includes, for example, the size and location of the tumor and the detailed structure of the vascular abnormality. The generator generates a 3D model based on the shape and location of the identified tumor, vascular abnormality, and fracture. Step 4: The providing unit provides the 3D model generated by the generating unit to medical staff. The providing unit displays the generated 3D model on the medical staff's computer, tablet, or smartphone, thereby supporting medical staff in dealing with difficult diagnoses.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects medical image data; an analysis unit that analyzes the data collected by the collection unit and identifies an abnormality; a generation unit that generates a 3D model based on the abnormality location identified by the analysis unit; a providing unit that provides the 3D model generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Collect medical imaging data from CT scans or MRIs 2. The system of claim 1.

3. The analysis unit Analyzing medical image data using AI to identify abnormalities 2. The system of claim 1.

4. The generation unit Generate a 3D model based on the shape and location of the identified anomaly 2. The system of claim 1.

5. The providing unit The generated 3D model is provided to medical staff to promote mutual understanding with patients.

2. The system of claim 1.

6. The providing unit Treatment simulation is performed based on the generated 3D model, and the appropriate treatment method is selected.

2. The system of claim 1.

7. The collecting unit Estimating a patient's emotions and adjusting the timing of medical image data collection based on the estimated patient emotions 2. The system of claim 1.

8. The collecting unit Analyze the patient's past medical history and select the most appropriate collection method 2. The system of claim 1.

Images