system

Abstract

The system according to this embodiment aims to reduce the effort involved in image diagnosis at the time of death. [Solution] The system according to the embodiment comprises an acquisition unit, a storage unit, an analysis unit, and a identification unit. The acquisition unit scans the body with CT or MRI at the time of death. The storage unit stores the image data acquired by the acquisition unit. The analysis unit analyzes the image data stored by the storage unit. The identification unit identifies the cause of death based on the data analyzed by the analysis unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in image diagnosis at the time of death, there is a combination rule of whole-body radiography and CT, and there is a problem that the labor is large.

[0005] The system according to the embodiment aims to reduce the labor in image diagnosis at the time of death.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a storage unit, an analysis unit, and an identification unit. The acquisition unit scans the deceased body with CT or MRI at the time of death. The storage unit stores the image data acquired by the acquisition unit. The analysis unit analyzes the image data stored by the storage unit. The identification unit identifies the cause of death based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the effort involved in image diagnosis at the time of death. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The post-mortem imaging diagnostic system according to an embodiment of the present invention is a system that scans the deceased's body with CT or MRI at the time of death and analyzes the images using an AI-tuned model and an image diagnostic AI. This system can quickly and accurately identify the cause of death by scanning the deceased's body with CT or MRI at the time of death, interpreting the entire body, and detecting abnormalities. For example, the deceased's body is scanned with CT or MRI at the time of death. In this case, CT uses X-rays, and MRI uses radio waves and magnetic fields to acquire images. Next, the acquired image data is stored in a PACS (Picture Archiving and Communication System). PACS is a cloud-based service commonly used in medical institutions, and it is possible to store CT / MRI images in the cloud. Next, the AI-tuned model and the image diagnostic AI analyze the stored image data. The AI ​​interprets the entire body and detects abnormalities. For example, the AI ​​can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion, and can identify the cause of death. The AI ​​can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the AI ​​compares pre-mortem and post-mortem CT images to determine the cause of death. This allows for verification of whether appropriate treatment was provided and confirmation of the cause of death. In addition, because the AI ​​can examine countless possible causes of death, the identification of the cause of death is carried out quickly and accurately. This system solves problems such as the shortage of forensic pathologists, the need to secure operating rooms, and time constraints, thereby improving the efficiency of determining the cause of death. It also reduces the negative image associated with autopsies and allows for the determination of the cause of death without dissecting the body. This allows hospitals to use the data to verify the appropriateness of treatment and review management, and prosecutors to use it as investigative data. Moreover, with the moral significance of government promotion, the importance of determining the cause of death is recognized. In summary, the post-mortem imaging diagnostic system makes post-mortem imaging diagnostics more efficient and enables the rapid and accurate identification of the cause of death.

[0029] The postmortem imaging diagnostic system according to this embodiment comprises an acquisition unit, a storage unit, an analysis unit, and a identification unit. The acquisition unit scans the deceased's body using CT or MRI at the time of death. The acquisition unit can, for example, scan the body using X-rays with CT. The acquisition unit can also scan the body using radio waves and magnetic fields with MRI. For example, the acquisition unit can acquire high-resolution images using CT scans. The acquisition unit can also acquire detailed tissue images using MRI scans. Furthermore, the acquisition unit can perform scans using a combination of both CT and MRI. The storage unit stores the image data acquired by the acquisition unit. The storage unit stores the image data in the cloud, for example, using a PACS (Picture Archiving and Communication System). Cloud storage enables secure storage and rapid access to the image data. For example, the storage unit stores CT / MRI images in the cloud and shares them within the medical institution. The storage unit can also automatically back up the image data. Furthermore, the storage unit can encrypt the image data to ensure security. The analysis unit analyzes the image data stored by the storage unit. The analysis unit uses image diagnostic AI and a tuned AI model to interpret images of the entire body and detect abnormalities. For example, the analysis unit can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. The analysis unit can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the analysis unit can compare pre-mortem and post-mortem CT image data to determine the cause of death. The identification unit identifies the cause of death based on the data analyzed by the analysis unit. The identification unit uses AI to verify all possible causes of death and quickly and accurately identifies the cause of death. For example, the identification unit identifies the cause of death based on the area where an abnormality was detected. The identification unit can also identify the cause of death based on the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the identification unit can compare pre-mortem and post-mortem CT image data to confirm whether appropriate treatment was provided. As a result, the post-mortem image diagnostic system according to this embodiment can streamline post-mortem image diagnostics and quickly and accurately identify the cause of death.

[0030] The acquisition unit scans the deceased's body using CT or MRI at the time of death. For example, the acquisition unit can scan the body using X-rays with CT. Alternatively, the acquisition unit can scan the body using radio waves and magnetic fields with MRI. Specifically, CT scans use X-rays to capture high-resolution images of the internal structure of the body, allowing for a detailed understanding of the condition of bones and organs. CT scans are particularly excellent at detecting fractures and internal bleeding, providing important information for determining the cause of death. On the other hand, MRI scans use strong magnetic fields and radio waves to capture the movement of hydrogen atoms in the body and generate detailed tissue images. MRI scans are particularly suitable for detailed observation of soft tissues and the brain, and can detect tumors, bleeding, and tissue abnormalities with high accuracy. Furthermore, the acquisition unit can also perform scans combining both CT and MRI. This allows for a comprehensive understanding of the condition of bones, organs, and soft tissues, enabling a more accurate diagnosis. For example, by confirming fractures with a CT scan and examining damage to surrounding soft tissues in detail with an MRI scan, comprehensive information necessary for determining the cause of death can be collected. The acquisition unit is equipped with an interface for quickly acquiring this scan data and sending it to the next processing stage. This allows the acquisition unit to efficiently and accurately scan the deceased and provide basic data for post-mortem imaging diagnosis.

[0031] The storage unit stores image data acquired by the acquisition unit. For example, the storage unit uses a PACS (Picture Archiving and Communication System) to store image data in the cloud. PACS is a system for storing, managing, and sharing medical images, and can efficiently manage acquired CT and MRI image data. Cloud storage enables secure storage and rapid access to image data. Cloud storage ensures data redundancy and protects data in the event of disasters or system failures. For example, the storage unit stores CT / MRI images in the cloud and shares them within the medical institution. This allows multiple medical institutions and specialists to access image data simultaneously and collaborate on diagnosis and analysis. The storage unit can also automatically back up image data. Regular backups minimize the risk of data loss and enable long-term data preservation. Furthermore, the storage unit can encrypt image data to ensure security. Encryption prevents unauthorized access and leakage of data, protecting privacy. By integrating these functions and managing acquired image data securely and efficiently, the storage unit can improve the reliability and convenience of the post-mortem imaging diagnostic system.

[0032] The analysis unit analyzes image data stored by the storage unit. Using image diagnostic AI and a tuned AI model, the analysis unit interprets images of the entire body and detects abnormalities. The image diagnostic AI has learned from vast amounts of medical image data and possesses highly accurate abnormality detection capabilities. For example, the analysis unit can detect abnormalities such as duodenal rupture in peritonitis or pericardial hematoma in chest trauma. The AI ​​can analyze CT and MRI images and quickly identify abnormal structures and patterns. The analysis unit can also determine the amount of oxygen in blood vessels and the amount of tissue components. This allows for the detection of abnormal blood flow and tissue degeneration, which can help determine the cause of death. Furthermore, the analysis unit can compare pre-mortem and post-mortem CT image data to investigate the cause of death. For example, comparing pre-mortem and post-mortem image data allows for the evaluation of disease progression and treatment effectiveness, providing information necessary to determine the cause of death. By integrating these functions and analyzing image data quickly and accurately, the analysis unit can improve the accuracy and efficiency of post-mortem imaging diagnosis.

[0033] The identification unit determines the cause of death based on data analyzed by the analysis unit. Using AI, the identification unit verifies all possible causes of death, quickly and accurately identifying the cause. The AI ​​references a vast medical database and compares it with anomaly detection results to identify the most likely cause of death. For example, the identification unit identifies the cause of death based on the location of the detected anomaly. It analyzes the abnormal areas detected in CT and MRI images in detail to identify the cause of death associated with those areas. The identification unit can also identify the cause of death based on the amount of oxygen in blood vessels and the amount of tissue components. If abnormal blood flow or tissue degeneration is related to the cause of death, it uses this data to identify the cause. Furthermore, the identification unit can compare pre-mortem and post-mortem CT image data to confirm whether appropriate treatment was provided. For example, it evaluates whether pre-mortem treatment was appropriate and to what extent the treatment was effective, helping to identify the cause of death. By integrating these functions and quickly and accurately identifying the cause of death, the identification unit can improve the reliability and usefulness of the post-mortem imaging diagnostic system. As a result, the postmortem imaging diagnostic system according to this embodiment can streamline postmortem imaging diagnostics and quickly and accurately identify the cause of death.

[0034] The analysis unit can interpret images of the entire body and detect abnormalities. For example, the analysis unit can analyze whole-body CT images and detect abnormalities. For example, the analysis unit can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. The analysis unit can also analyze whole-body MRI images and detect abnormalities. For example, the analysis unit can use MRI images to detect abnormalities such as intracranial hemorrhage or tumors. The analysis unit can also analyze both CT and MRI images and detect abnormalities. For example, the analysis unit can compare CT and MRI images to identify abnormalities. This allows for rapid detection of abnormalities by interpreting images of the entire body. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input whole-body CT images into a generating AI and have the generating AI perform abnormality detection.

[0035] The identification unit can compare CT image data from before death with CT image data from after death. For example, the identification unit can compare CT image data from before death with CT image data from after death to identify abnormalities. For example, the identification unit can compare CT image data from before death with CT image data from after death to identify the cause of death. The identification unit can also compare CT image data from before death with CT image data from after death to confirm whether appropriate treatment was provided. For example, the identification unit can compare CT image data from before death with CT image data from after death to evaluate the effectiveness of treatment. By comparing CT image data from before death and after death, the cause of death can be identified more accurately. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input CT image data from before death and CT image data from after death into a generating AI and have the generating AI perform the comparison.

[0036] The analysis unit can determine the amount of oxygen in blood vessels and the amount of tissue components. For example, the analysis unit can analyze CT images to determine the amount of oxygen in blood vessels. For example, the analysis unit measures the amount of oxygen in blood vessels using CT images. The analysis unit can also analyze MRI images to determine the amount of tissue components. For example, the analysis unit measures the amount of tissue components using MRI images. The analysis unit can also analyze both CT and MRI images to determine the amount of oxygen in blood vessels and the amount of tissue components. For example, the analysis unit compares CT and MRI images to identify the amount of oxygen in blood vessels and the amount of tissue components. This helps in determining the cause of death by determining the amount of oxygen in blood vessels and the amount of tissue components. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input CT images into a generating AI and have the generating AI perform the measurement of the amount of oxygen in blood vessels.

[0037] The analysis unit can verify all possible causes of death. For example, the analysis unit can analyze CT images to verify all possible causes of death. For example, the analysis unit can use CT images to detect abnormalities and identify the cause of death. The analysis unit can also analyze MRI images to verify all possible causes of death. For example, the analysis unit can use MRI images to detect abnormalities and identify the cause of death. The analysis unit can also analyze both CT and MRI images to verify all possible causes of death. For example, the analysis unit can compare CT and MRI images to identify all possible causes of death. By verifying all possible causes of death, the cause of death can be identified quickly and accurately. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input CT images into a generative AI and have the generative AI perform the verification of all possible causes of death.

[0038] The storage unit can use PACS to store image data in the cloud. For example, the storage unit can store CT images in PACS and manage them on the cloud. For example, the storage unit can store CT images in the cloud and share them within the medical institution. The storage unit can also store MRI images in PACS and manage them on the cloud. For example, the storage unit can store MRI images in the cloud and share them within the medical institution. The storage unit can also store both CT and MRI images in PACS and manage them on the cloud. For example, the storage unit can store both CT and MRI images in the cloud and share them within the medical institution. This makes it possible to store image data in the cloud by using PACS. Some or all of the above processing in the storage unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the storage unit can input CT images into a generation AI and have the generation AI perform the management of cloud storage.

[0039] The specific unit can verify whether appropriate treatment was provided. For example, the specific unit can analyze CT images and evaluate the effectiveness of the treatment. For example, the specific unit can use CT images to confirm the appropriateness of the treatment. The specific unit can also analyze MRI images and evaluate the effectiveness of the treatment. For example, the specific unit can use MRI images to confirm the appropriateness of the treatment. The specific unit can also analyze both CT and MRI images and evaluate the effectiveness of the treatment. For example, the specific unit can compare CT and MRI images to confirm the appropriateness of the treatment. This makes it possible to verify whether appropriate treatment was provided. Some or all of the above processing in the specific unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the specific unit can input CT images into a generating AI and have the generating AI perform an evaluation of the effectiveness of the treatment.

[0040] The identification unit reduces the negative image associated with autopsy and can determine the cause of death without making incisions in the body. The identification unit can, for example, analyze CT images to determine the cause of death. For example, the identification unit can use CT images to detect abnormalities and determine the cause of death. The identification unit can also analyze MRI images to determine the cause of death. For example, the identification unit can use MRI images to detect abnormalities and determine the cause of death. The identification unit can also analyze both CT and MRI images to determine the cause of death. For example, the identification unit compares CT and MRI images to determine the cause of death. This reduces the negative image associated with autopsy and can determine the cause of death without making incisions in the body. Some or all of the above-described processes in the identification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the identification unit can input CT images into a generating AI and have the generating AI perform the determination of the cause of death.

[0041] The acquisition unit can select the optimal scanning method based on the condition of the body during scanning. For example, if the body is damaged, the acquisition unit adjusts the scanning method to avoid the damaged areas. For example, the acquisition unit adjusts the scanning angle to avoid the damaged areas. The acquisition unit can also select scanning settings appropriate for the cooled state if the body is cooled. For example, the acquisition unit sets the scanning temperature appropriate for the cooled state. The acquisition unit can also select a scanning method that takes into account the decomposed areas if the body is decomposed. For example, the acquisition unit adjusts the scanning range to avoid the decomposed areas. By selecting the optimal scanning method based on the condition of the body, the accuracy of the scan is improved. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input body condition data into a generative AI and have the generative AI select the optimal scanning method.

[0042] The acquisition unit can be equipped with a function to automatically adjust the position and angle of the body during scanning. For example, the acquisition unit can automatically adjust the position of the body and set the optimal scanning angle. For example, the acquisition unit can detect the position of the body with a sensor and adjust it automatically. The acquisition unit can also automatically adjust the angle of the body to ensure that the entire body is scanned evenly. For example, the acquisition unit can adjust the angle of the body with a motor to scan the entire body evenly. The acquisition unit can also adjust the position and angle of the body in real time to improve the accuracy of the scan. For example, the acquisition unit can monitor the position and angle of the body in real time to improve the accuracy of the scan. This improves the accuracy of the scan by automatically adjusting the position and angle of the body. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the position and angle data of the body into a generating AI and have the generating AI perform the automatic adjustment.

[0043] The acquisition unit can set optimal scanning conditions during scanning by considering the external environmental information of the corpse. For example, the acquisition unit can set scanning conditions by considering the temperature and humidity of the scanning room. For example, the acquisition unit can measure the temperature of the scanning room with a sensor and set optimal scanning conditions. The acquisition unit can also set scanning conditions by considering the lighting conditions of the scanning room. For example, the acquisition unit can adjust the lighting of the scanning room and set optimal scanning conditions. The acquisition unit can also set scanning conditions by considering the noise level of the scanning room. For example, the acquisition unit can measure the noise level of the scanning room and set optimal scanning conditions. By setting optimal scanning conditions while considering the external environmental information of the corpse, the accuracy of the scan is improved. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input environmental data of the scanning room into a generation AI and have the generation AI perform the setting of optimal scanning conditions.

[0044] The acquisition unit can improve the accuracy of the scan by referring to the deceased's health information from before their death. For example, the acquisition unit can optimize the scan settings based on the deceased's health information from before their death. For example, the acquisition unit can adjust the scan settings by referring to the deceased's health information from before their death. The acquisition unit can also improve the accuracy of the scan by referring to the deceased's medical history from before their death. For example, the acquisition unit can adjust the scan settings by referring to the deceased's medical history from before their death. The acquisition unit can also improve the accuracy of the scan based on the deceased's image data from before their death. For example, the acquisition unit adjusts the scan settings by referring to the image data from before their death. As a result, the accuracy of the scan is improved by referring to the deceased's health information from before their death. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the deceased's health information from before their death into a generating AI and have the generating AI perform the optimization of the scan settings.

[0045] The storage unit can adjust the level of detail in saving data based on its importance. For example, it can save important data at a high level of detail and general data at a low level of detail. For instance, it can save important data at a high level of detail to retain detailed information. Alternatively, it can save general data at a low level of detail to conserve storage space. For example, it can compress general data to conserve storage space. It can also adjust the compression ratio of the data depending on its importance. For example, it can save important data at a low compression ratio and general data at a high compression ratio. This allows for efficient data storage by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the importance of the data into a generative AI and have the generative AI adjust the level of detail of the data.

[0046] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a storage algorithm specifically for images to image data. For instance, it can save image data with high compression to conserve storage space. The storage unit can also apply a storage algorithm specifically for text data. For example, it can save text data with low compression to retain detailed information. The storage unit can also apply a storage algorithm specifically for audio data. For example, it can save audio data with high compression to conserve storage space. This enables efficient data storage by applying different storage algorithms depending on the data category. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the data category into a generative AI and have the generative AI apply the storage algorithm.

[0047] The storage unit can determine the priority of data storage based on the data submission date. For example, the storage unit can prioritize saving data that has been submitted earlier. For example, the storage unit can prioritize saving data that has been submitted earlier to allow for quick access. The storage unit can also postpone saving data that has been submitted later. For example, the storage unit can postpone saving data that has been submitted later to save storage capacity. The storage unit can also adjust the storage schedule based on the submission date. For example, the storage unit can adjust the storage schedule based on the submission date to save data efficiently. This enables efficient data storage by determining the priority of data storage based on the data submission date. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit can input the data submission date into a generative AI and have the generative AI determine the priority of storage.

[0048] The storage unit can adjust the order of saving data based on its relevance. For example, the storage unit can prioritize saving highly relevant data. For example, the storage unit can prioritize saving highly relevant data to enable quick access. The storage unit can also postpone saving less relevant data. For example, the storage unit can postpone saving less relevant data to save storage capacity. The storage unit can also adjust the order of saving data based on its relevance. For example, the storage unit can adjust the order of saving data based on its relevance to save data efficiently. This enables efficient data storage by adjusting the order of saving data based on its relevance. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the saving order.

[0049] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and prioritize the analysis of highly relevant data. The analysis unit can also improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. The analysis unit can also optimize the analysis algorithm based on the interrelationships between data. For example, the analysis unit can optimize the analysis algorithm based on the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationships between data into a generative AI and have the generative AI perform the optimization of the analysis accuracy.

[0050] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis algorithm based on the attribute information of the data submitter. The analysis unit can also improve the accuracy of the analysis by considering the attribute information of the data submitter. The analysis unit can also classify the results of the analysis based on the attribute information of the data submitter. For example, the analysis unit classifies the results of the analysis based on the attribute information of the data submitter. This improves the accuracy of the analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the attribute information of the data submitter into the generative AI and have the generative AI perform the analysis accuracy improvement.

[0051] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can adjust the analysis algorithm based on the geographical distribution of the data. The analysis unit can also improve the accuracy of the analysis by considering the geographical distribution of the data. For example, the analysis unit improves the accuracy of the analysis by considering the geographical distribution of the data. The analysis unit can also classify the results of the analysis based on the geographical distribution of the data. For example, the analysis unit classifies the results of the analysis based on the geographical distribution of the data. This improves the accuracy of the analysis by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0052] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis based on relevant literature. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. The analysis unit can also supplement the results of its analysis by referring to relevant literature. For example, the analysis unit supplements the results of its analysis by referring to relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input relevant literature into a generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0053] The identification unit can optimize a specific algorithm by referring to past specific data at a specific time. For example, the identification unit adjusts the specific algorithm based on past specific data. The identification unit can also improve the accuracy of a specific task by referring to past specific data. For example, the identification unit improves the accuracy of a specific task by referring to past specific data. The identification unit can also supplement specific results based on past specific data. For example, the identification unit supplements specific results based on past specific data. This improves the accuracy of a specific task by referring to past specific data. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input past specific data into a generative AI and have the generative AI perform the optimization of the specific algorithm.

[0054] The identification unit can apply different identification methods to each data category during identification. For example, the identification unit can apply an image-specific identification method to image data. For example, the identification unit applies an image-specific identification method based on image data. The identification unit can also apply a text-specific identification method to text data. For example, the identification unit applies a text-specific identification method based on text data. The identification unit can also apply an audio-specific identification method to audio data. For example, the identification unit applies an audio-specific identification method based on audio data. By applying different identification methods to each data category, the accuracy of identification is improved. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input the data category to the generative AI and have the generative AI perform the application of the identification method.

[0055] The identification unit can determine specific priorities based on the data submission timing at the time of identification. For example, the identification unit can prioritize the identification of data submitted earlier. For example, the identification unit can prioritize the identification of data submitted earlier and provide results quickly. The identification unit can also postpone the identification of data submitted later. For example, the identification unit can postpone the identification of data submitted later and process it efficiently. The identification unit can also adjust specific schedules based on the submission timing. For example, the identification unit can adjust specific schedules based on the submission timing and process the data efficiently. This enables efficient identification by determining specific priorities based on the data submission timing. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input the data submission timing into a generative AI and have the generative AI perform the determination of specific priorities.

[0056] The identification unit can perform identification by referring to relevant market data at the time of identification. The identification unit can, for example, adjust the identification algorithm based on the relevant market data. The identification unit can also improve the accuracy of identification by referring to the relevant market data. The identification unit can also supplement the results of identification by referring to the relevant market data. The identification unit supplements the results of identification by referring to the relevant market data. As a result, the accuracy of identification is improved by referring to the relevant market data. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input relevant market data into a generative AI and have the generative AI perform the accuracy improvement of identification.

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

[0058] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the data. For example, it can analyze the interrelationships of the data and prioritize the analysis of highly relevant data. The analysis unit can also improve the accuracy of the analysis by considering the interrelationships of the data. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the data. Furthermore, it can optimize the analysis algorithm based on the interrelationships of the data. The analysis unit can optimize the analysis algorithm based on the interrelationships of the data. As a result, the accuracy of the analysis is improved by considering the interrelationships of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the interrelationships of the data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0059] The storage unit can adjust the level of detail in storage based on the importance of the data. For example, important data can be stored at a high level of detail, while general data can be stored at a low level of detail. The storage unit can store important data at a high level of detail, retaining detailed information. It can also store general data at a low level of detail, saving storage space. The storage unit can compress general data for storage, saving storage space. Furthermore, it can adjust the compression ratio of storage according to the importance of the data. The storage unit can store important data at a low compression ratio and general data at a high compression ratio. This enables efficient data storage by adjusting the level of detail in storage based on the importance of the data. Some or all of the above processing in the storage unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the storage unit can input the importance of the data into a generative AI and have the generative AI perform the adjustment of the level of detail in storage.

[0060] The identification unit can optimize a specific algorithm by referring to past specific data at a specific time. For example, it can adjust the specific algorithm based on past specific data. The identification unit can adjust the specific algorithm based on past specific data. It can also improve the accuracy of a specific by referring to past specific data. The identification unit can improve the accuracy of a specific by referring to past specific data. Furthermore, it can supplement the results of a specific by referring to past specific data. The identification unit can supplement the results of a specific by referring to past specific data. This improves the accuracy of a specific by referring to past specific data. Some or all of the above processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input past specific data into a generation AI and have the generation AI perform the optimization of the specific algorithm.

[0061] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, it can adjust the analysis algorithm based on the attribute information of the data submitter. The analysis unit can adjust the analysis algorithm based on the attribute information of the data submitter. It can also improve the accuracy of the analysis by considering the attribute information of the data submitter. The analysis unit can improve the accuracy of the analysis by considering the attribute information of the data submitter. Furthermore, it can classify the results of the analysis based on the attribute information of the data submitter. The analysis unit can classify the results of the analysis based on the attribute information of the data submitter. This improves the accuracy of the analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the attribute information of the data submitter into a generation AI and have the generation AI perform the analysis accuracy improvement.

[0062] The storage unit can determine the priority of data storage based on the data submission date. For example, it can prioritize saving data that was submitted earlier. The storage unit can prioritize saving data that was submitted earlier, making it easily accessible. It can also postpone saving data that was submitted later. The storage unit can postpone saving data that was submitted later, saving storage capacity. Furthermore, it can adjust the storage schedule based on the submission date. The storage unit can adjust the storage schedule based on the submission date, enabling efficient data storage. This makes efficient data storage possible by determining the priority of saving based on the data submission date. Some or all of the above processing in the storage unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the storage unit can input the data submission date into a generation AI and have the generation AI determine the priority of saving.

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

[0064] Step 1: The acquisition unit scans the body using CT or MRI at the time of death. For example, the body can be scanned using X-rays with a CT scanner, or using radio waves and magnetic fields with an MRI scanner. The acquisition unit can obtain high-resolution images using a CT scan, or detailed tissue images using an MRI scan. Furthermore, it is also possible to perform scans using a combination of both CT and MRI. Step 2: The storage unit stores the image data acquired by the acquisition unit. For example, by using a PACS (Picture Archiving and Communication System) to store image data in the cloud, secure storage and rapid access to image data are possible. The storage unit can store CT / MRI images in the cloud and share them within the medical institution, automatically back up image data, and ensure security by encrypting image data. Step 3: The analysis unit analyzes the image data stored by the storage unit. The analysis unit uses image diagnostic AI and a tuned AI model to interpret the entire body and detect abnormalities. For example, it can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. It can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, it can compare pre-mortem and post-mortem CT image data to determine the cause of death. Step 4: The identification unit identifies the cause of death based on the data analyzed by the analysis unit. The identification unit uses AI to verify all possible causes of death and quickly and accurately identifies the cause of death. For example, it can identify the cause of death based on the area where an abnormality was detected, or based on the amount of oxygen in the blood vessels or the amount of tissue components. Furthermore, it can compare CT images taken before and after death to confirm whether appropriate treatment was provided.

[0065] (Example of form 2) The post-mortem imaging diagnostic system according to an embodiment of the present invention is a system that scans the deceased's body with CT or MRI at the time of death and analyzes the images using an AI-tuned model and an image diagnostic AI. This system can quickly and accurately identify the cause of death by scanning the deceased's body with CT or MRI at the time of death, interpreting the entire body, and detecting abnormalities. For example, the deceased's body is scanned with CT or MRI at the time of death. In this case, CT uses X-rays, and MRI uses radio waves and magnetic fields to acquire images. Next, the acquired image data is stored in a PACS (Picture Archiving and Communication System). PACS is a cloud-based service commonly used in medical institutions, and it is possible to store CT / MRI images in the cloud. Next, the AI-tuned model and the image diagnostic AI analyze the stored image data. The AI ​​interprets the entire body and detects abnormalities. For example, the AI ​​can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion, and can identify the cause of death. The AI ​​can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the AI ​​compares pre-mortem and post-mortem CT images to determine the cause of death. This allows for verification of whether appropriate treatment was provided and confirmation of the cause of death. In addition, because the AI ​​can examine countless possible causes of death, the identification of the cause of death is carried out quickly and accurately. This system solves problems such as the shortage of forensic pathologists, the need to secure operating rooms, and time constraints, thereby improving the efficiency of determining the cause of death. It also reduces the negative image associated with autopsies and allows for the determination of the cause of death without dissecting the body. This allows hospitals to use the data to verify the appropriateness of treatment and review management, and prosecutors to use it as investigative data. Moreover, with the moral significance of government promotion, the importance of determining the cause of death is recognized. In summary, the post-mortem imaging diagnostic system makes post-mortem imaging diagnostics more efficient and enables the rapid and accurate identification of the cause of death.

[0066] The postmortem imaging diagnostic system according to this embodiment comprises an acquisition unit, a storage unit, an analysis unit, and a identification unit. The acquisition unit scans the deceased's body using CT or MRI at the time of death. The acquisition unit can, for example, scan the body using X-rays with CT. The acquisition unit can also scan the body using radio waves and magnetic fields with MRI. For example, the acquisition unit can acquire high-resolution images using CT scans. The acquisition unit can also acquire detailed tissue images using MRI scans. Furthermore, the acquisition unit can perform scans using a combination of both CT and MRI. The storage unit stores the image data acquired by the acquisition unit. The storage unit stores the image data in the cloud, for example, using a PACS (Picture Archiving and Communication System). Cloud storage enables secure storage and rapid access to the image data. For example, the storage unit stores CT / MRI images in the cloud and shares them within the medical institution. The storage unit can also automatically back up the image data. Furthermore, the storage unit can encrypt the image data to ensure security. The analysis unit analyzes the image data stored by the storage unit. The analysis unit uses image diagnostic AI and a tuned AI model to interpret images of the entire body and detect abnormalities. For example, the analysis unit can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. The analysis unit can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the analysis unit can compare pre-mortem and post-mortem CT image data to determine the cause of death. The identification unit identifies the cause of death based on the data analyzed by the analysis unit. The identification unit uses AI to verify all possible causes of death and quickly and accurately identifies the cause of death. For example, the identification unit identifies the cause of death based on the area where an abnormality was detected. The identification unit can also identify the cause of death based on the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, the identification unit can compare pre-mortem and post-mortem CT image data to confirm whether appropriate treatment was provided. As a result, the post-mortem image diagnostic system according to this embodiment can streamline post-mortem image diagnostics and quickly and accurately identify the cause of death.

[0067] The acquisition unit scans the deceased's body using CT or MRI at the time of death. For example, the acquisition unit can scan the body using X-rays with CT. Alternatively, the acquisition unit can scan the body using radio waves and magnetic fields with MRI. Specifically, CT scans use X-rays to capture high-resolution images of the internal structure of the body, allowing for a detailed understanding of the condition of bones and organs. CT scans are particularly excellent at detecting fractures and internal bleeding, providing important information for determining the cause of death. On the other hand, MRI scans use strong magnetic fields and radio waves to capture the movement of hydrogen atoms in the body and generate detailed tissue images. MRI scans are particularly suitable for detailed observation of soft tissues and the brain, and can detect tumors, bleeding, and tissue abnormalities with high accuracy. Furthermore, the acquisition unit can also perform scans combining both CT and MRI. This allows for a comprehensive understanding of the condition of bones, organs, and soft tissues, enabling a more accurate diagnosis. For example, by confirming fractures with a CT scan and examining damage to surrounding soft tissues in detail with an MRI scan, comprehensive information necessary for determining the cause of death can be collected. The acquisition unit is equipped with an interface for quickly acquiring this scan data and sending it to the next processing stage. This allows the acquisition unit to efficiently and accurately scan the deceased and provide basic data for post-mortem imaging diagnosis.

[0068] The storage unit stores image data acquired by the acquisition unit. For example, the storage unit uses a PACS (Picture Archiving and Communication System) to store image data in the cloud. PACS is a system for storing, managing, and sharing medical images, and can efficiently manage acquired CT and MRI image data. Cloud storage enables secure storage and rapid access to image data. Cloud storage ensures data redundancy and protects data in the event of disasters or system failures. For example, the storage unit stores CT / MRI images in the cloud and shares them within the medical institution. This allows multiple medical institutions and specialists to access image data simultaneously and collaborate on diagnosis and analysis. The storage unit can also automatically back up image data. Regular backups minimize the risk of data loss and enable long-term data preservation. Furthermore, the storage unit can encrypt image data to ensure security. Encryption prevents unauthorized access and leakage of data, protecting privacy. By integrating these functions and managing acquired image data securely and efficiently, the storage unit can improve the reliability and convenience of the post-mortem imaging diagnostic system.

[0069] The analysis unit analyzes image data stored by the storage unit. Using image diagnostic AI and a tuned AI model, the analysis unit interprets images of the entire body and detects abnormalities. The image diagnostic AI has learned from vast amounts of medical image data and possesses highly accurate abnormality detection capabilities. For example, the analysis unit can detect abnormalities such as duodenal rupture in peritonitis or pericardial hematoma in chest trauma. The AI ​​can analyze CT and MRI images and quickly identify abnormal structures and patterns. The analysis unit can also determine the amount of oxygen in blood vessels and the amount of tissue components. This allows for the detection of abnormal blood flow and tissue degeneration, which can help determine the cause of death. Furthermore, the analysis unit can compare pre-mortem and post-mortem CT image data to investigate the cause of death. For example, comparing pre-mortem and post-mortem image data allows for the evaluation of disease progression and treatment effectiveness, providing information necessary to determine the cause of death. By integrating these functions and analyzing image data quickly and accurately, the analysis unit can improve the accuracy and efficiency of post-mortem imaging diagnosis.

[0070] The identification unit determines the cause of death based on data analyzed by the analysis unit. Using AI, the identification unit verifies all possible causes of death, quickly and accurately identifying the cause. The AI ​​references a vast medical database and compares it with anomaly detection results to identify the most likely cause of death. For example, the identification unit identifies the cause of death based on the location of the detected anomaly. It analyzes the abnormal areas detected in CT and MRI images in detail to identify the cause of death associated with those areas. The identification unit can also identify the cause of death based on the amount of oxygen in blood vessels and the amount of tissue components. If abnormal blood flow or tissue degeneration is related to the cause of death, it uses this data to identify the cause. Furthermore, the identification unit can compare pre-mortem and post-mortem CT image data to confirm whether appropriate treatment was provided. For example, it evaluates whether pre-mortem treatment was appropriate and to what extent the treatment was effective, helping to identify the cause of death. By integrating these functions and quickly and accurately identifying the cause of death, the identification unit can improve the reliability and usefulness of the post-mortem imaging diagnostic system. As a result, the postmortem imaging diagnostic system according to this embodiment can streamline postmortem imaging diagnostics and quickly and accurately identify the cause of death.

[0071] The analysis unit can interpret images of the entire body and detect abnormalities. For example, the analysis unit can analyze whole-body CT images and detect abnormalities. For example, the analysis unit can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. The analysis unit can also analyze whole-body MRI images and detect abnormalities. For example, the analysis unit can use MRI images to detect abnormalities such as intracranial hemorrhage or tumors. The analysis unit can also analyze both CT and MRI images and detect abnormalities. For example, the analysis unit can compare CT and MRI images to identify abnormalities. This allows for rapid detection of abnormalities by interpreting images of the entire body. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input whole-body CT images into a generating AI and have the generating AI perform abnormality detection.

[0072] The identification unit can compare CT image data from before death with CT image data from after death. For example, the identification unit can compare CT image data from before death with CT image data from after death to identify abnormalities. For example, the identification unit can compare CT image data from before death with CT image data from after death to identify the cause of death. The identification unit can also compare CT image data from before death with CT image data from after death to confirm whether appropriate treatment was provided. For example, the identification unit can compare CT image data from before death with CT image data from after death to evaluate the effectiveness of treatment. By comparing CT image data from before death and after death, the cause of death can be identified more accurately. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input CT image data from before death and CT image data from after death into a generating AI and have the generating AI perform the comparison.

[0073] The analysis unit can determine the amount of oxygen in blood vessels and the amount of tissue components. For example, the analysis unit can analyze CT images to determine the amount of oxygen in blood vessels. For example, the analysis unit measures the amount of oxygen in blood vessels using CT images. The analysis unit can also analyze MRI images to determine the amount of tissue components. For example, the analysis unit measures the amount of tissue components using MRI images. The analysis unit can also analyze both CT and MRI images to determine the amount of oxygen in blood vessels and the amount of tissue components. For example, the analysis unit compares CT and MRI images to identify the amount of oxygen in blood vessels and the amount of tissue components. This helps in determining the cause of death by determining the amount of oxygen in blood vessels and the amount of tissue components. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input CT images into a generating AI and have the generating AI perform the measurement of the amount of oxygen in blood vessels.

[0074] The analysis unit can verify all possible causes of death. For example, the analysis unit can analyze CT images to verify all possible causes of death. For example, the analysis unit can use CT images to detect abnormalities and identify the cause of death. The analysis unit can also analyze MRI images to verify all possible causes of death. For example, the analysis unit can use MRI images to detect abnormalities and identify the cause of death. The analysis unit can also analyze both CT and MRI images to verify all possible causes of death. For example, the analysis unit can compare CT and MRI images to identify all possible causes of death. By verifying all possible causes of death, the cause of death can be identified quickly and accurately. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input CT images into a generative AI and have the generative AI perform the verification of all possible causes of death.

[0075] The storage unit can use PACS to store image data in the cloud. For example, the storage unit can store CT images in PACS and manage them on the cloud. For example, the storage unit can store CT images in the cloud and share them within the medical institution. The storage unit can also store MRI images in PACS and manage them on the cloud. For example, the storage unit can store MRI images in the cloud and share them within the medical institution. The storage unit can also store both CT and MRI images in PACS and manage them on the cloud. For example, the storage unit can store both CT and MRI images in the cloud and share them within the medical institution. This makes it possible to store image data in the cloud by using PACS. Some or all of the above processing in the storage unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the storage unit can input CT images into a generation AI and have the generation AI perform the management of cloud storage.

[0076] The specific unit can verify whether appropriate treatment was provided. For example, the specific unit can analyze CT images and evaluate the effectiveness of the treatment. For example, the specific unit can use CT images to confirm the appropriateness of the treatment. The specific unit can also analyze MRI images and evaluate the effectiveness of the treatment. For example, the specific unit can use MRI images to confirm the appropriateness of the treatment. The specific unit can also analyze both CT and MRI images and evaluate the effectiveness of the treatment. For example, the specific unit can compare CT and MRI images to confirm the appropriateness of the treatment. This makes it possible to verify whether appropriate treatment was provided. Some or all of the above processing in the specific unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the specific unit can input CT images into a generating AI and have the generating AI perform an evaluation of the effectiveness of the treatment.

[0077] The identification unit reduces the negative image associated with autopsy and can determine the cause of death without making incisions in the body. The identification unit can, for example, analyze CT images to determine the cause of death. For example, the identification unit can use CT images to detect abnormalities and determine the cause of death. The identification unit can also analyze MRI images to determine the cause of death. For example, the identification unit can use MRI images to detect abnormalities and determine the cause of death. The identification unit can also analyze both CT and MRI images to determine the cause of death. For example, the identification unit compares CT and MRI images to determine the cause of death. This reduces the negative image associated with autopsy and can determine the cause of death without making incisions in the body. Some or all of the above-described processes in the identification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the identification unit can input CT images into a generating AI and have the generating AI perform the determination of the cause of death.

[0078] The acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. For example, if the user is tense, the acquisition unit can create a relaxing environment before starting the scan. For example, the acquisition unit can create a relaxing environment by playing music. The acquisition unit can also set a priority schedule for a quick scan if the user is in a hurry. For example, the acquisition unit can adjust the scan priority to perform the scan quickly. The acquisition unit can also allow time for the user to calm down if the user is sad before starting the scan. For example, the acquisition unit can provide time for the user to calm down and then start the scan. This allows for a more appropriate scan by adjusting the timing of the scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0079] The acquisition unit can select the optimal scanning method based on the condition of the body during scanning. For example, if the body is damaged, the acquisition unit adjusts the scanning method to avoid the damaged areas. For example, the acquisition unit adjusts the scanning angle to avoid the damaged areas. The acquisition unit can also select scanning settings appropriate for the cooled state if the body is cooled. For example, the acquisition unit sets the scanning temperature appropriate for the cooled state. The acquisition unit can also select a scanning method that takes into account the decomposed areas if the body is decomposed. For example, the acquisition unit adjusts the scanning range to avoid the decomposed areas. By selecting the optimal scanning method based on the condition of the body, the accuracy of the scan is improved. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input body condition data into a generative AI and have the generative AI select the optimal scanning method.

[0080] The acquisition unit can be equipped with a function to automatically adjust the position and angle of the body during scanning. For example, the acquisition unit can automatically adjust the position of the body and set the optimal scanning angle. For example, the acquisition unit can detect the position of the body with a sensor and adjust it automatically. The acquisition unit can also automatically adjust the angle of the body to ensure that the entire body is scanned evenly. For example, the acquisition unit can adjust the angle of the body with a motor to scan the entire body evenly. The acquisition unit can also adjust the position and angle of the body in real time to improve the accuracy of the scan. For example, the acquisition unit can monitor the position and angle of the body in real time to improve the accuracy of the scan. This improves the accuracy of the scan by automatically adjusting the position and angle of the body. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the position and angle data of the body into a generating AI and have the generating AI perform the automatic adjustment.

[0081] The acquisition unit can estimate the user's emotions and determine the priority of scans based on the estimated emotions. For example, if the user is in a hurry, the acquisition unit can set a higher priority for the scan. For example, the acquisition unit can adjust the scan schedule to prioritize scans for users in a hurry. The acquisition unit can also set a lower priority for scans if the user is relaxed. For example, the acquisition unit can adjust the scan schedule to postpone scans for relaxed users. The acquisition unit can also adjust the scan priority and take the user's emotions into consideration if the user is sad. For example, the acquisition unit can adjust the scan schedule to take the emotions of sad users into consideration. This allows for more appropriate scans by determining the priority of scans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, generative AI, or not using generative AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The acquisition unit can set optimal scanning conditions during scanning by considering the external environmental information of the corpse. For example, the acquisition unit can set scanning conditions by considering the temperature and humidity of the scanning room. For example, the acquisition unit can measure the temperature of the scanning room with a sensor and set optimal scanning conditions. The acquisition unit can also set scanning conditions by considering the lighting conditions of the scanning room. For example, the acquisition unit can adjust the lighting of the scanning room and set optimal scanning conditions. The acquisition unit can also set scanning conditions by considering the noise level of the scanning room. For example, the acquisition unit can measure the noise level of the scanning room and set optimal scanning conditions. By setting optimal scanning conditions while considering the external environmental information of the corpse, the accuracy of the scan is improved. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input environmental data of the scanning room into a generation AI and have the generation AI perform the setting of optimal scanning conditions.

[0083] The acquisition unit can improve the accuracy of the scan by referring to the deceased's health information from before their death. For example, the acquisition unit can optimize the scan settings based on the deceased's health information from before their death. For example, the acquisition unit can adjust the scan settings by referring to the deceased's health information from before their death. The acquisition unit can also improve the accuracy of the scan by referring to the deceased's medical history from before their death. For example, the acquisition unit can adjust the scan settings by referring to the deceased's medical history from before their death. The acquisition unit can also improve the accuracy of the scan based on the deceased's image data from before their death. For example, the acquisition unit adjusts the scan settings by referring to the image data from before their death. As a result, the accuracy of the scan is improved by referring to the deceased's health information from before their death. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the deceased's health information from before their death into a generating AI and have the generating AI perform the optimization of the scan settings.

[0084] The storage unit can estimate the user's emotions and select data to save based on the estimated emotions. For example, if the user is tense, the storage unit can save only important data. For example, the storage unit can estimate the emotions of a tense user and prioritize saving important data. The storage unit can also save detailed data if the user is relaxed. For example, the storage unit can estimate the emotions of a relaxed user and save detailed data. The storage unit can also quickly select data to save if the user is in a hurry. For example, the storage unit can estimate the emotions of a hurried user and quickly select data to save. This allows for more appropriate data storage by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using, for example, generative AI, or without generative AI. For example, the storage unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The storage unit can adjust the level of detail in saving data based on its importance. For example, it can save important data at a high level of detail and general data at a low level of detail. For instance, it can save important data at a high level of detail to retain detailed information. Alternatively, it can save general data at a low level of detail to conserve storage space. For example, it can compress general data to conserve storage space. It can also adjust the compression ratio of the data depending on its importance. For example, it can save important data at a low compression ratio and general data at a high compression ratio. This allows for efficient data storage by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the importance of the data into a generative AI and have the generative AI adjust the level of detail of the data.

[0086] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a storage algorithm specifically for images to image data. For instance, it can save image data with high compression to conserve storage space. The storage unit can also apply a storage algorithm specifically for text data. For example, it can save text data with low compression to retain detailed information. The storage unit can also apply a storage algorithm specifically for audio data. For example, it can save audio data with high compression to conserve storage space. This enables efficient data storage by applying different storage algorithms depending on the data category. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the data category into a generative AI and have the generative AI apply the storage algorithm.

[0087] The storage unit can estimate the user's emotions and determine the priority of saved data based on the estimated emotions. For example, if the user is in a hurry, the storage unit will prioritize saving important data. For example, the storage unit will estimate the emotions of a user in a hurry and prioritize saving important data. The storage unit can also prioritize saving detailed data if the user is relaxed. For example, the storage unit will estimate the emotions of a relaxed user and prioritize saving detailed data. The storage unit can also adjust the priority of saved data to reflect the user's emotions if the user is sad. For example, the storage unit will estimate the emotions of a sad user and adjust the priority of saved data to reflect those emotions. This allows for more appropriate data storage by determining the priority of saved data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The storage unit can determine the priority of data storage based on the data submission date. For example, the storage unit can prioritize saving data that has been submitted earlier. For example, the storage unit can prioritize saving data that has been submitted earlier to allow for quick access. The storage unit can also postpone saving data that has been submitted later. For example, the storage unit can postpone saving data that has been submitted later to save storage capacity. The storage unit can also adjust the storage schedule based on the submission date. For example, the storage unit can adjust the storage schedule based on the submission date to save data efficiently. This enables efficient data storage by determining the priority of data storage based on the data submission date. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit can input the data submission date into a generative AI and have the generative AI determine the priority of storage.

[0089] The storage unit can adjust the order of saving data based on its relevance. For example, the storage unit can prioritize saving highly relevant data. For example, the storage unit can prioritize saving highly relevant data to enable quick access. The storage unit can also postpone saving less relevant data. For example, the storage unit can postpone saving less relevant data to save storage capacity. The storage unit can also adjust the order of saving data based on its relevance. For example, the storage unit can adjust the order of saving data based on its relevance to save data efficiently. This enables efficient data storage by adjusting the order of saving data based on its relevance. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the saving order.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is tense, the analysis unit can apply simple analysis criteria. For example, the analysis unit can estimate the emotions of a tense user and apply simple analysis criteria. The analysis unit can also apply detailed analysis criteria if the user is relaxed. For example, the analysis unit can estimate the emotions of a relaxed user and apply detailed analysis criteria. The analysis unit can also apply criteria for rapid analysis if the user is in a hurry. For example, the analysis unit can estimate the emotions of a hurried user and apply criteria for rapid analysis. By adjusting the analysis criteria according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and prioritize the analysis of highly relevant data. The analysis unit can also improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. The analysis unit can also optimize the analysis algorithm based on the interrelationships between data. For example, the analysis unit can optimize the analysis algorithm based on the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationships between data into a generative AI and have the generative AI perform the optimization of the analysis accuracy.

[0092] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis algorithm based on the attribute information of the data submitter. The analysis unit can also improve the accuracy of the analysis by considering the attribute information of the data submitter. The analysis unit can also classify the results of the analysis based on the attribute information of the data submitter. For example, the analysis unit classifies the results of the analysis based on the attribute information of the data submitter. This improves the accuracy of the analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the attribute information of the data submitter into the generative AI and have the generative AI perform the analysis accuracy improvement.

[0093] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the analysis unit can display important results first. For example, the analysis unit can estimate the emotions of a nervous user and display important results first. The analysis unit can also display detailed results first if the user is relaxed. For example, the analysis unit can estimate the emotions of a relaxed user and display detailed results first. The analysis unit can also display results that can be quickly reviewed first if the user is in a hurry. For example, the analysis unit can estimate the emotions of a hurried user and display results that can be quickly reviewed first. By adjusting the order in which the analysis results are displayed according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can adjust the analysis algorithm based on the geographical distribution of the data. The analysis unit can also improve the accuracy of the analysis by considering the geographical distribution of the data. For example, the analysis unit improves the accuracy of the analysis by considering the geographical distribution of the data. The analysis unit can also classify the results of the analysis based on the geographical distribution of the data. For example, the analysis unit classifies the results of the analysis based on the geographical distribution of the data. This improves the accuracy of the analysis by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0095] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis based on relevant literature. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. The analysis unit can also supplement the results of its analysis by referring to relevant literature. For example, the analysis unit supplements the results of its analysis by referring to relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input relevant literature into a generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0096] The identification unit can estimate the user's emotions and adjust the identification method based on the estimated emotions. For example, if the user is nervous, the identification unit can apply a simple identification method. For example, the identification unit can estimate the emotions of a nervous user and apply a simple identification method. The identification unit can also apply a detailed identification method if the user is relaxed. For example, the identification unit can estimate the emotions of a relaxed user and apply a detailed identification method. The identification unit can also apply a method for rapid identification if the user is in a hurry. For example, the identification unit can estimate the emotions of a hurried user and apply a method for rapid identification. By adjusting the identification method according to the user's emotions, more appropriate identification becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the specific unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The identification unit can optimize a specific algorithm by referring to past specific data at a specific time. For example, the identification unit adjusts the specific algorithm based on past specific data. The identification unit can also improve the accuracy of a specific task by referring to past specific data. For example, the identification unit improves the accuracy of a specific task by referring to past specific data. The identification unit can also supplement specific results based on past specific data. For example, the identification unit supplements specific results based on past specific data. This improves the accuracy of a specific task by referring to past specific data. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input past specific data into a generative AI and have the generative AI perform the optimization of the specific algorithm.

[0098] The identification unit can apply different identification methods to each data category during identification. For example, the identification unit can apply an image-specific identification method to image data. For example, the identification unit applies an image-specific identification method based on image data. The identification unit can also apply a text-specific identification method to text data. For example, the identification unit applies a text-specific identification method based on text data. The identification unit can also apply an audio-specific identification method to audio data. For example, the identification unit applies an audio-specific identification method based on audio data. By applying different identification methods to each data category, the accuracy of identification is improved. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input the data category to the generative AI and have the generative AI perform the application of the identification method.

[0099] The identification unit can estimate the user's emotions and determine specific priorities based on the estimated emotions. For example, if the user is in a hurry, the identification unit will prioritize important identifications. For example, the identification unit will estimate the emotions of a user in a hurry and prioritize important identifications. The identification unit can also prioritize detailed identifications if the user is relaxed. For example, the identification unit will estimate the emotions of a relaxed user and prioritize detailed identifications. The identification unit can also adjust the priority of identifications to take the user's emotions into consideration if the user is sad. For example, the identification unit will estimate the emotions of a sad user and adjust the priority of identifications to take the user's emotions into consideration. This allows for more appropriate identifications by determining the priority of identifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the specific unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0100] The identification unit can determine specific priorities based on the data submission timing at the time of identification. For example, the identification unit can prioritize the identification of data submitted earlier. For example, the identification unit can prioritize the identification of data submitted earlier and provide results quickly. The identification unit can also postpone the identification of data submitted later. For example, the identification unit can postpone the identification of data submitted later and process it efficiently. The identification unit can also adjust specific schedules based on the submission timing. For example, the identification unit can adjust specific schedules based on the submission timing and process the data efficiently. This enables efficient identification by determining specific priorities based on the data submission timing. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input the data submission timing into a generative AI and have the generative AI perform the determination of specific priorities.

[0101] The identification unit can perform identification by referring to relevant market data at the time of identification. The identification unit can, for example, adjust the identification algorithm based on the relevant market data. The identification unit can also improve the accuracy of identification by referring to the relevant market data. The identification unit can also supplement the results of identification by referring to the relevant market data. The identification unit supplements the results of identification by referring to the relevant market data. As a result, the accuracy of identification is improved by referring to the relevant market data. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input relevant market data into a generative AI and have the generative AI perform the accuracy improvement of identification.

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

[0103] The acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. For example, if the user is nervous, the scan can begin after creating a relaxing environment. The acquisition unit can create such an environment by playing relaxing music, for example. If the user is in a hurry, the acquisition unit can also set a priority schedule for a quick scan. The acquisition unit can adjust the scan priority and perform the scan quickly. Furthermore, if the user is sad, the scan can be performed after giving them time to calm down. The acquisition unit can provide the user with time to calm down and then begin the scan. This allows for a more appropriate scan by adjusting the timing of the scan according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using generative AI or not. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the data. For example, it can analyze the interrelationships of the data and prioritize the analysis of highly relevant data. The analysis unit can also improve the accuracy of the analysis by considering the interrelationships of the data. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the data. Furthermore, it can optimize the analysis algorithm based on the interrelationships of the data. The analysis unit can optimize the analysis algorithm based on the interrelationships of the data. As a result, the accuracy of the analysis is improved by considering the interrelationships of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the interrelationships of the data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0105] The identification unit can estimate the user's emotions and adjust the identification method based on the estimated emotions. For example, if the user is nervous, a simple identification method can be applied. The identification unit can estimate the emotions of a nervous user and apply a simple identification method. Also, if the user is relaxed, a more detailed identification method can be applied. The identification unit can estimate the emotions of a relaxed user and apply a more detailed identification method. Furthermore, if the user is in a hurry, a method for rapid identification can be applied. The identification unit can estimate the emotions of a hurried user and apply a method for rapid identification. This allows for more appropriate identification by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using generative AI or not using generative AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The storage unit can adjust the level of detail in storage based on the importance of the data. For example, important data can be stored at a high level of detail, while general data can be stored at a low level of detail. The storage unit can store important data at a high level of detail, retaining detailed information. It can also store general data at a low level of detail, saving storage space. The storage unit can compress general data for storage, saving storage space. Furthermore, it can adjust the compression ratio of storage according to the importance of the data. The storage unit can store important data at a low compression ratio and general data at a high compression ratio. This enables efficient data storage by adjusting the level of detail in storage based on the importance of the data. Some or all of the above processing in the storage unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the storage unit can input the importance of the data into a generative AI and have the generative AI perform the adjustment of the level of detail in storage.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is nervous, a simple analysis criterion can be applied. The analysis unit can estimate the emotions of a nervous user and apply a simple analysis criterion. If the user is relaxed, a more detailed analysis criterion can be applied. The analysis unit can estimate the emotions of a relaxed user and apply a detailed analysis criterion. Furthermore, if the user is in a hurry, criteria for rapid analysis can be applied. The analysis unit can estimate the emotions of a hurried user and apply criteria for rapid analysis. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The identification unit can optimize a specific algorithm by referring to past specific data at a specific time. For example, it can adjust the specific algorithm based on past specific data. The identification unit can adjust the specific algorithm based on past specific data. It can also improve the accuracy of a specific by referring to past specific data. The identification unit can improve the accuracy of a specific by referring to past specific data. Furthermore, it can supplement the results of a specific by referring to past specific data. The identification unit can supplement the results of a specific by referring to past specific data. This improves the accuracy of a specific by referring to past specific data. Some or all of the above processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input past specific data into a generation AI and have the generation AI perform the optimization of the specific algorithm.

[0109] The storage unit can estimate the user's emotions and select data to save based on the estimated emotions. For example, if the user is nervous, only important data can be saved. The storage unit can estimate the emotions of a nervous user and prioritize saving important data. Also, if the user is relaxed, detailed data can be saved. The storage unit can estimate the emotions of a relaxed user and save detailed data. Furthermore, if the user is in a hurry, data can be quickly selected for saving. The storage unit can estimate the emotions of a hurried user and quickly select data to save. This allows for more appropriate data storage by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using generative AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, it can adjust the analysis algorithm based on the attribute information of the data submitter. The analysis unit can adjust the analysis algorithm based on the attribute information of the data submitter. It can also improve the accuracy of the analysis by considering the attribute information of the data submitter. The analysis unit can improve the accuracy of the analysis by considering the attribute information of the data submitter. Furthermore, it can classify the results of the analysis based on the attribute information of the data submitter. The analysis unit can classify the results of the analysis based on the attribute information of the data submitter. This improves the accuracy of the analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the attribute information of the data submitter into a generation AI and have the generation AI perform the analysis accuracy improvement.

[0111] The identification unit can estimate the user's emotions and determine specific priorities based on the estimated emotions. For example, if the user is in a hurry, important identifications can be prioritized. The identification unit can estimate the emotions of a user in a hurry and prioritize important identifications. Also, if the user is relaxed, detailed identifications can be prioritized. The identification unit can estimate the emotions of a relaxed user and prioritize detailed identifications. Furthermore, if the user is sad, the identification unit can adjust the specific priorities to take their emotions into consideration. The identification unit can estimate the emotions of a sad user and adjust the specific priorities to take their emotions into consideration. This allows for more appropriate identification by determining specific priorities according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using generative AI or not. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The storage unit can determine the priority of data storage based on the data submission date. For example, it can prioritize saving data that was submitted earlier. The storage unit can prioritize saving data that was submitted earlier, making it easily accessible. It can also postpone saving data that was submitted later. The storage unit can postpone saving data that was submitted later, saving storage capacity. Furthermore, it can adjust the storage schedule based on the submission date. The storage unit can adjust the storage schedule based on the submission date, enabling efficient data storage. This makes efficient data storage possible by determining the priority of saving based on the data submission date. Some or all of the above processing in the storage unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the storage unit can input the data submission date into a generation AI and have the generation AI determine the priority of saving.

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

[0114] Step 1: The acquisition unit scans the body using CT or MRI at the time of death. For example, the body can be scanned using X-rays with a CT scanner, or using radio waves and magnetic fields with an MRI scanner. The acquisition unit can obtain high-resolution images using a CT scan, or detailed tissue images using an MRI scan. Furthermore, it is also possible to perform scans using a combination of both CT and MRI. Step 2: The storage unit stores the image data acquired by the acquisition unit. For example, by using a PACS (Picture Archiving and Communication System) to store image data in the cloud, secure storage and rapid access to image data are possible. The storage unit can store CT / MRI images in the cloud and share them within the medical institution, automatically back up image data, and ensure security by encrypting image data. Step 3: The analysis unit analyzes the image data stored by the storage unit. The analysis unit uses image diagnostic AI and a tuned AI model to interpret the entire body and detect abnormalities. For example, it can detect abnormalities such as a ruptured duodenum due to peritonitis or a pericardial hematoma due to a chest contusion. It can also determine the amount of oxygen in the blood vessels and the amount of tissue components. Furthermore, it can compare pre-mortem and post-mortem CT image data to determine the cause of death. Step 4: The identification unit identifies the cause of death based on the data analyzed by the analysis unit. The identification unit uses AI to verify all possible causes of death and quickly and accurately identifies the cause of death. For example, it can identify the cause of death based on the area where an abnormality was detected, or based on the amount of oxygen in the blood vessels or the amount of tissue components. Furthermore, it can compare CT images taken before and after death to confirm whether appropriate treatment was provided.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0118] Each of the multiple elements described above, including the acquisition unit, storage unit, analysis unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit scans the body using the camera 42 and microphone 38B of the smart device 14 and acquires image data using the control unit 46A. The storage unit stores the image data in the storage 50 of the smart device 14 or in the database 24 of the data processing unit 12. The analysis unit analyzes the image data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The identification unit identifies the cause of death using the identification processing unit 290 of the data processing unit 12. Furthermore, the acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the acquisition unit, storage unit, analysis unit, and identification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit scans the deceased using the camera 42 and microphone 238 of the smart glasses 214 and acquires image data using the control unit 46A. The storage unit stores the image data in the storage 50 of the smart glasses 214 or in the database 24 of the data processing unit 12. The analysis unit analyzes the image data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The identification unit identifies the cause of death using the identification processing unit 290 of the data processing unit 12. Furthermore, the acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the acquisition unit, storage unit, analysis unit, and identification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the acquisition unit scans the deceased using the camera 42 and microphone 238 of the headset terminal 314 and acquires image data using the control unit 46A. The storage unit stores the image data in the storage 50 of the headset terminal 314 or in the database 24 of the data processing unit 12. The analysis unit analyzes the image data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The identification unit identifies the cause of death using the identification processing unit 290 of the data processing unit 12. Furthermore, the acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the acquisition unit, storage unit, analysis unit, and identification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit scans the body using the camera 42 and microphone 238 of the robot 414 and acquires image data by the control unit 46A. The storage unit stores the image data in the storage 50 of the robot 414 or in the database 24 of the data processing unit 12. The analysis unit analyzes the image data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The identification unit identifies the cause of death using the identification processing unit 290 of the data processing unit 12. Furthermore, the acquisition unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0186] (Note 1) The acquisition unit scans the body with CT or MRI at the time of death, A storage unit for storing image data acquired by the acquisition unit, An analysis unit that analyzes the image data stored by the storage unit, The system includes an identification unit that identifies the cause of death based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Perform a full-body image analysis and detect any abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, Comparing CT image data from before death with CT image data from after death. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, To understand the amount of oxygen in the blood vessels and the amount of tissue components. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Investigating all countless causes of death The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned storage unit is Use PACS to store image data in the cloud. The system described in Appendix 1, characterized by the features described herein. (Note 7) The specified part is, To verify whether appropriate treatment was provided. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, To alleviate the negative image associated with autopsies and to determine the cause of death without using a scalpel on the body. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, During scanning, the optimal scanning method is selected based on the condition of the body. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Add a feature that automatically adjusts the position and angle of the body during scanning. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and determines the priority of scans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, During scanning, the optimal scanning conditions are set considering the external environmental information of the body. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, During scanning, the body's health information from before death is referenced to improve the accuracy of the scan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned storage unit is The system estimates the user's emotions and selects data to store based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned storage unit is When saving, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned storage unit is When saving, different saving algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned storage unit is It estimates the user's emotions and determines the priority of stored data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned storage unit is When saving data, prioritize saving based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned storage unit is When saving, adjust the saving order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, It estimates the user's emotions and adjusts specific methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The specified part is, At specific times, optimize a specific algorithm by referring to specific past data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The specified part is, At specific times, different identification methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The specified part is, It estimates the user's emotions and determines specific priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The specified part is, At specific times, a particular priority is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 32) The specified part is, At the time of identification, the identification is performed by referring to relevant market data for the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The acquisition unit scans the body with CT or MRI at the time of death, A storage unit for storing image data acquired by the acquisition unit, An analysis unit that analyzes the image data stored by the storage unit, The system includes an identification unit that identifies the cause of death based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Perform a full-body image analysis and detect any abnormalities. The system according to feature 1.

3. The specified part is, Comparing CT image data from before death with CT image data from after death. The system according to feature 1.

4. The aforementioned analysis unit, To understand the amount of oxygen in the blood vessels and the amount of tissue components. The system according to feature 1.

5. The aforementioned analysis unit, Investigating all countless causes of death The system according to feature 1.

6. The aforementioned storage unit is Use PACS to store image data in the cloud. The system according to feature 1.

7. The specified part is, To verify whether appropriate treatment was provided. The system according to feature 1.

8. The specified part is, To alleviate the negative image associated with autopsies and to determine the cause of death without using a scalpel on the body. The system according to feature 1.

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