System

Abstract

An object of a system according to an embodiment is to reduce a burden on a medical worker in diagnosis of an ultrasonic image.SOLUTION: A system includes an imaging unit, an analysis unit, and a provision unit. The imaging unit captures an ultrasonic image. The analysis unit analyzes the ultrasonic image captured by the imaging unit. The providing unit provides the medical worker with the diagnosis result generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of placing a heavy burden on medical professionals when diagnosing ultrasound images.

[0005] The system according to the embodiment aims to reduce the burden on medical personnel in diagnosing ultrasound images. [Means for solving the problem]

[0006] The system according to the embodiment includes an imaging unit, an analysis unit, and a providing unit. The imaging unit captures ultrasound images. The analysis unit analyzes the ultrasound images captured by the imaging unit. The providing unit provides a medical professional with a diagnosis result generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on medical personnel in diagnosing ultrasound images. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a compact and lightweight ultrasound imaging device for daily use, reducing the burden of hospital visits. This system allows patients to easily take ultrasound images at home or at work, analyze them using a generation AI, and provide diagnostic results to medical professionals. For example, a patient may take ultrasound images of a fetus at home, and a generation AI may analyze the images to generate a diagnostic result. The generated diagnostic result is then provided to a medical professional, who can provide appropriate advice and treatment to the patient based on the results. This reduces the burden of hospital visits for patients and the burden on medical professionals. This system allows patients to easily take ultrasound images at home or at work and receive analysis results from the generation AI, thereby reducing the frequency of hospital visits. Furthermore, medical professionals can improve the accuracy of their diagnoses and reduce their burden by making diagnoses based on the analysis results from the generation AI. For example, pregnant women may take ultrasound images regularly at home and receive analysis results from the generation AI, thereby reducing the frequency of hospital visits and the burden on medical professionals.

[0029] An ultrasound imaging system according to an embodiment includes an imaging unit, an analysis unit, and a providing unit. The imaging unit captures ultrasound images. For example, the imaging unit uses a small and lightweight ultrasound imaging device that can be easily operated by a patient at home or at work. The imaging unit allows a patient to capture ultrasound images of a fetus at home, for example. The analysis unit uses a generation AI to analyze the ultrasound images captured by the imaging unit. For example, the analysis unit analyzes the ultrasound images to determine whether or not there are any abnormalities and to generate a specific diagnosis result. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the ultrasound images to generate a diagnosis result. The providing unit provides the diagnosis result generated by the analysis unit to a medical professional. For example, the providing unit provides the diagnosis result analyzed by the generation AI to a medical professional, who can then provide appropriate advice and treatment to the patient based on the result. As a result, the ultrasound imaging system according to an embodiment performs ultrasound image capture, analysis, and provision of diagnosis results in a single process, thereby reducing the burden on medical professionals.

[0030] Furthermore, the ultrasound imaging system includes an imaging unit that uses a small and lightweight ultrasound imaging device. The imaging unit uses a small and lightweight ultrasound imaging device. For example, the imaging unit uses an ultrasound imaging device that weighs 500 grams or less and is 20 centimeters or less in size. This allows a patient to easily take ultrasound images at home or at work. For example, a patient can take ultrasound images of a fetus at home. This allows a patient to easily take ultrasound images at home or at work using a small and lightweight device.

[0031] The analysis unit can analyze ultrasound images to determine whether or not there is an abnormality and generate specific diagnostic results. The analysis unit uses generative AI to analyze ultrasound images to determine whether or not there is an abnormality and generate specific diagnostic results. For example, the analysis unit analyzes ultrasound images to detect whether or not there is an abnormality. The analysis unit can detect an abnormality, for example, when a specific pattern or numerical value exceeds a threshold. The analysis unit can also generate specific diagnostic results. For example, the analysis unit can analyze ultrasound images to generate a specific disease name and recommended treatment. This makes it possible to generate quick and accurate diagnostic results by analyzing ultrasound images.

[0032] The providing unit can provide the diagnostic results analyzed by the generative AI to medical professionals. The providing unit provides the diagnostic results analyzed by the generative AI to medical professionals. For example, the providing unit provides the diagnostic results analyzed by the generative AI to medical professionals, who can then provide appropriate advice and treatment to patients based on the results. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes ultrasound images and generates diagnostic results. In this way, the accuracy and efficiency of diagnoses can be improved by providing the analysis results by the generative AI to medical professionals.

[0033] The imaging unit can be easily operated by a patient at home or at work. The imaging unit can be easily operated by a patient at home or at work. For example, the imaging unit has an intuitive user interface, allowing the patient to operate it easily. Furthermore, the imaging unit has simple operating procedures, allowing the patient to take ultrasound images in a short time. Furthermore, the imaging unit is designed to be easily operated by a patient at home or at work. For example, the imaging unit is easy to carry, allowing the patient to easily take ultrasound images at home or at work. This allows the patient to easily operate the unit at home or at work, reducing the burden of visiting a hospital.

[0034] The analysis unit can diagnose the fetus's growth status and whether or not there are any abnormalities. The analysis unit uses generative AI to diagnose the fetus's growth status and whether or not there are any abnormalities. For example, the analysis unit analyzes ultrasound images of the fetus to evaluate the fetus's growth status. The analysis unit can evaluate, for example, the fetus's weight, height, and organ development status. The analysis unit can also analyze ultrasound images of the fetus to diagnose whether or not there are any abnormalities. For example, the analysis unit can detect abnormalities when specific patterns or values ​​exceed a threshold. This allows the pregnant woman to receive appropriate advice and treatment by diagnosing the fetus's growth status and whether or not there are any abnormalities.

[0035] The imaging unit can analyze the patient's past imaging history and select the optimal imaging method. The imaging unit analyzes the patient's past imaging history and selects the optimal imaging method. For example, the imaging unit preferentially selects the imaging method that the patient has preferred in the past. The imaging unit can also select the imaging method with the highest success rate based on the patient's past imaging history. The imaging unit can also select the optimal imaging angle and settings based on the patient's past imaging history. In this way, the success rate of imaging is improved by selecting the optimal imaging method based on the past imaging history.

[0036] The imaging unit can perform filtering based on the patient's current health condition and living situation when taking an image. The imaging unit can perform filtering based on the patient's current health condition and living situation when taking an image. For example, if the patient is tired, the imaging unit selects an imaging method that requires less time. Also, if the patient is healthy, the imaging unit can perform detailed imaging. Also, the imaging unit can select the optimal imaging time period depending on the patient's living situation. This allows for more appropriate imaging by selecting an imaging method that suits the patient's health condition and living situation.

[0037] The imaging unit can select an appropriate imaging means according to the patient's input method when imaging. The imaging unit selects an appropriate imaging means according to the patient's input method when imaging. For example, when the patient uses voice input, the imaging unit performs imaging in response to a voice command. Furthermore, when the patient uses text input, the imaging unit can also perform imaging based on text instructions. Furthermore, when the patient uses image input, the imaging unit can also perform imaging based on image analysis. This improves the convenience of imaging by selecting an imaging means according to the patient's input method.

[0038] The imaging unit can be added with a function to automatically adjust the patient's position and posture when imaging. The imaging unit can be added with a function to automatically adjust the patient's position and posture when imaging. For example, if the patient is lying down, the imaging unit can adjust the patient to the optimal position before imaging. Also, if the patient is sitting, the imaging unit can adjust the patient's posture before imaging. Also, if the patient is standing, the imaging unit can adjust the patient's position before imaging. This makes it possible to automatically adjust the patient's position and posture to take optimal images.

[0039] The imaging unit can prioritize imaging of highly relevant imaging regions in consideration of the patient's geographical location information when imaging. The imaging unit prioritizes imaging of highly relevant imaging regions in consideration of the patient's geographical location information when imaging. For example, if the patient lives in a specific area, the imaging unit can prioritize imaging of regions related to diseases that are likely to occur in that area. Furthermore, if the patient is traveling, the imaging unit can also prioritize imaging of regions related to health risks at the travel destination. Furthermore, if the patient is in a specific environment, the imaging unit can select imaging regions in consideration of health risks related to that environment. In this way, by selecting imaging regions based on the patient's geographical location information, highly relevant regions can be preferentially imaged.

[0040] The imaging unit can analyze the patient's social media activity and obtain related health information when imaging. The imaging unit can analyze the patient's social media activity and obtain related health information when imaging. For example, the imaging unit can select the body part to be imaged based on health information shared by the patient on social media. The imaging unit can also estimate health risks from the patient's social media activity and image related body parts. The imaging unit can also select the body part to be imaged by referring to the health information of the patient's friends on social media. This allows for more appropriate imaging by obtaining health information based on the patient's social media activity.

[0041] The imaging unit can customize the imaging method by reflecting the patient's past feedback when imaging. The imaging unit customizes the imaging method by reflecting the patient's past feedback when imaging. For example, the imaging unit adjusts the imaging method based on feedback provided by the patient in the past. The imaging unit can also select optimal imaging settings from the patient's past feedback. The imaging unit can also improve the imaging procedure by referring to the patient's past feedback. In this way, the imaging method can be customized by reflecting the patient's past feedback.

[0042] The imaging unit can propose an appropriate imaging schedule based on the patient's lifestyle at the time of imaging. The imaging unit proposes an appropriate imaging schedule based on the patient's lifestyle at the time of imaging. For example, the imaging unit analyzes the patient's lifestyle and proposes the optimal imaging time period. The imaging unit can also adjust the imaging frequency according to the patient's lifestyle. The imaging unit can also customize the imaging schedule taking the patient's lifestyle into consideration. In this way, by proposing an imaging schedule based on the patient's lifestyle, the convenience of imaging is improved.

[0043] The analysis unit can set the level of detail of the analysis based on the importance of the ultrasound image during analysis. The analysis unit sets the level of detail of the analysis based on the importance of the ultrasound image during analysis. For example, the analysis unit analyzes images of important areas in detail. The analysis unit can also analyze images of less important areas simply. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the ultrasound image.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the ultrasound image during analysis. The analysis unit applies different analysis algorithms depending on the category of the ultrasound image during analysis. For example, the analysis unit applies an analysis algorithm dedicated to fetuses to ultrasound images of fetuses. The analysis unit can also apply an analysis algorithm dedicated to the heart to ultrasound images of the heart. The analysis unit can also apply an analysis algorithm dedicated to the abdomen to ultrasound images of the abdomen. In this way, by applying an analysis algorithm depending on the category of the ultrasound image, the accuracy of the analysis is improved.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. For example, the analysis unit corrects the current analysis results based on the patient's past analysis results. The analysis unit can also grasp abnormal trends from the patient's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results.

[0046] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. For example, the analysis unit can improve the accuracy of the analysis by combining image analysis and text analysis. The analysis unit can also improve the accuracy of the analysis by combining machine learning and rule-based analysis methods. The analysis unit can also improve the accuracy of the analysis by combining different analysis algorithms. In this way, the accuracy of the analysis is improved by combining different analysis methods.

[0047] The analysis unit can set the analysis priority based on the time when the ultrasound image was taken during analysis. The analysis unit can set the analysis priority based on the time when the ultrasound image was taken during analysis. For example, the analysis unit prioritizes the analysis of the most recent ultrasound image. The analysis unit can also postpone the analysis of images taken earlier. The analysis unit can also adjust the analysis priority according to the time when the ultrasound image was taken. In this way, by determining the analysis priority based on the time when the ultrasound image was taken, the most recent information can be analyzed preferentially.

[0048] The analysis unit can set the order of analysis based on the relevance of the ultrasound images during analysis. The analysis unit sets the order of analysis based on the relevance of the ultrasound images during analysis. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the order of analysis according to the relevance of the images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the ultrasound images.

[0049] The analysis unit can set the use of technical terms for the analysis according to the patient's level of expertise during analysis. The analysis unit can set the use of technical terms for the analysis according to the patient's level of expertise during analysis. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terms. Also, if the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the way in which the analysis results are expressed according to the patient's level of expertise. In this way, by adjusting the technical terms for the analysis according to the patient's level of expertise, it is possible to provide analysis results that are easier to understand.

[0050] The analysis unit can integrate information from different data sources during analysis to improve the accuracy of the analysis. The analysis unit integrates information from different data sources during analysis to improve the accuracy of the analysis. For example, the analysis unit integrates and analyzes ultrasound images and blood test results. The analysis unit can also integrate and analyze ultrasound images and patient medical history. The analysis unit can also integrate and analyze ultrasound images and genetic information. In this way, the accuracy of the analysis is improved by integrating information from different data sources.

[0051] The providing unit can set the level of detail of the provided information based on the importance of the diagnostic result at the time of providing the information. The providing unit sets the level of detail of the provided information based on the importance of the diagnostic result at the time of providing the information. For example, the providing unit provides important diagnostic results in detail. The providing unit can also provide less important diagnostic results in a simplified manner. The providing unit can also determine the priority of the provided information according to the importance of the diagnostic result. This allows for efficient information provision by adjusting the level of detail of the provided information based on the importance of the diagnostic result.

[0052] The providing unit can apply different providing algorithms depending on the category of the diagnostic result at the time of providing. The providing unit applies different providing algorithms depending on the category of the diagnostic result at the time of providing. For example, the providing unit applies a providing algorithm dedicated to the fetus to the diagnostic result of the fetus. The providing unit can also apply a providing algorithm dedicated to the heart to the diagnostic result of the heart. The providing unit can also apply a providing algorithm dedicated to the abdomen to the diagnostic result of the abdomen. In this way, by applying a providing algorithm according to the category of the diagnostic result, more appropriate information can be provided.

[0053] The providing unit can improve the accuracy of provision at the time of provision by referring to the patient's past provision results. The providing unit improves the accuracy of provision at the time of provision by referring to the patient's past provision results. For example, the providing unit corrects the current provision results based on the patient's past provision results. The providing unit can also grasp abnormal trends from the patient's past provision results and reflect them in the current provision. The providing unit can also adjust the provision algorithm by referring to the patient's past provision results. In this way, the accuracy of provision is improved by referring to the patient's past provision results.

[0054] The providing unit can add a function to set the display method of the diagnostic results when providing the results. The providing unit adds a function to set the display method of the diagnostic results when providing the results. For example, the providing unit allows the patient to select the display style (graph, text, image, etc.) that they prefer. The providing unit can also display the diagnostic results in a format that is easy for the patient to understand. The providing unit can also suggest the optimal display method based on the patient's past selection history. In this way, by customizing the display method of the diagnostic results, it is possible to provide information in a format that is easy for the patient to understand.

[0055] The providing unit can set the priority of provision based on the time of submission of the diagnostic results at the time of provision. The providing unit sets the priority of provision based on the time of submission of the diagnostic results at the time of provision. For example, the providing unit provides the most recent diagnostic results preferentially. The providing unit can also postpone diagnostic results that have been submitted older. The providing unit can also adjust the priority of provision depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the diagnostic results, the latest information can be provided preferentially.

[0056] The providing unit can set the order of provision based on the relevance of the diagnostic results at the time of provision. The providing unit sets the order of provision based on the relevance of the diagnostic results at the time of provision. For example, the providing unit provides highly relevant diagnostic results preferentially. The providing unit can also postpone less relevant diagnostic results. The providing unit can also adjust the order of provision according to the relevance of the diagnostic results. As a result, adjusting the order of provision based on the relevance of the diagnostic results enables efficient information provision.

[0057] The providing unit can set the use of technical terms to be provided in accordance with the patient's level of expertise when providing the information. The providing unit sets the use of technical terms to be provided in accordance with the patient's level of expertise when providing the information. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. Also, if the patient does not have technical expertise, the providing unit can explain the diagnosis results in simple terms. Also, the providing unit can adjust the way in which the diagnosis results are expressed in accordance with the patient's level of expertise. In this way, by adjusting the technical terms to be provided in accordance with the patient's level of expertise, it is possible to provide information that is easier to understand.

[0058] The providing unit can integrate information from different data sources at the time of providing to improve the accuracy of the information provided. The providing unit integrates information from different data sources at the time of providing to improve the accuracy of the information provided. For example, the providing unit integrates ultrasound images and blood test results and provides them. The providing unit can also integrate ultrasound images and patient medical history and provide them. The providing unit can also integrate ultrasound images and genetic information and provide them. In this way, the accuracy of the information provided is improved by integrating information from different data sources.

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

[0060] The imaging unit can analyze the patient's past imaging history and select the optimal imaging method. For example, the imaging unit can prioritize the imaging method that the patient has previously preferred. The imaging unit can also select the imaging method with the highest success rate based on the patient's past imaging history. The imaging unit can also select the optimal imaging angle and settings based on the patient's past imaging history. In this way, the success rate of imaging is improved by selecting the optimal imaging method based on the patient's past imaging history.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the ultrasound image. For example, the analysis unit applies an analysis algorithm specifically for fetuses to ultrasound images of fetuses. The analysis unit can also apply an analysis algorithm specifically for the heart to ultrasound images of the heart. The analysis unit can also apply an analysis algorithm specifically for the abdomen to ultrasound images of the abdomen. In this way, the accuracy of the analysis is improved by applying an analysis algorithm according to the category of the ultrasound image.

[0062] The providing unit can apply different providing algorithms depending on the category of the diagnostic result at the time of providing. For example, the providing unit applies a providing algorithm dedicated to the fetus to the diagnostic result of the fetus. The providing unit can also apply a providing algorithm dedicated to the heart to the diagnostic result of the heart. The providing unit can also apply a providing algorithm dedicated to the abdomen to the diagnostic result of the abdomen. In this way, by applying a providing algorithm according to the category of the diagnostic result, more appropriate information can be provided.

[0063] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. For example, the analysis unit can improve the accuracy of the analysis by combining image analysis and text analysis. The analysis unit can also improve the accuracy of the analysis by combining machine learning and rule-based analysis methods. The analysis unit can also improve the accuracy of the analysis by combining different analysis algorithms. In this way, the accuracy of the analysis is improved by combining different analysis methods.

[0064] The providing unit can add a function to set the display method of the diagnostic results when providing the results. For example, the providing unit can allow the patient to select the display style (graph, text, image, etc.) that they prefer. The providing unit can also display the diagnostic results in a format that is easy for the patient to understand. The providing unit can also suggest the optimal display method based on the patient's past selection history. In this way, by customizing the display method of the diagnostic results, it is possible to provide information in a format that is easy for the patient to understand.

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

[0066] Step 1: The imaging unit captures an ultrasound image. For example, the imaging unit uses a small and lightweight ultrasound imaging device that can be easily operated by a patient at home or at work. For example, the imaging unit allows a patient to capture an ultrasound image of a fetus at home. Step 2: The analysis unit uses the generation AI to analyze the ultrasound image captured by the imaging unit. For example, the analysis unit analyzes the ultrasound image and determines whether there are any abnormalities and generates a specific diagnosis result. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the ultrasound image and generates a diagnosis result. Step 3: The provider provides the diagnostic results generated by the analyzer to medical professionals. For example, the provider provides the diagnostic results analyzed by the generating AI to medical professionals, who can then provide appropriate advice and treatment to the patient based on the results.

[0067] (Example 2) A system according to an embodiment of the present invention uses a compact and lightweight ultrasound imaging device for daily use, reducing the burden of hospital visits. This system allows patients to easily take ultrasound images at home or at work, analyze them using a generation AI, and provide diagnostic results to medical professionals. For example, a patient may take ultrasound images of a fetus at home, and a generation AI may analyze the images to generate a diagnostic result. The generated diagnostic result is then provided to a medical professional, who can provide appropriate advice and treatment to the patient based on the results. This reduces the burden of hospital visits for patients and the burden on medical professionals. This system allows patients to easily take ultrasound images at home or at work and receive analysis results from the generation AI, thereby reducing the frequency of hospital visits. Furthermore, medical professionals can improve the accuracy of their diagnoses and reduce their burden by making diagnoses based on the analysis results from the generation AI. For example, pregnant women may take ultrasound images regularly at home and receive analysis results from the generation AI, thereby reducing the frequency of hospital visits and the burden on medical professionals.

[0068] An ultrasound imaging system according to an embodiment includes an imaging unit, an analysis unit, and a providing unit. The imaging unit captures ultrasound images. For example, the imaging unit uses a small and lightweight ultrasound imaging device that can be easily operated by a patient at home or at work. The imaging unit allows a patient to capture ultrasound images of a fetus at home, for example. The analysis unit uses a generation AI to analyze the ultrasound images captured by the imaging unit. For example, the analysis unit analyzes the ultrasound images to determine whether or not there are any abnormalities and to generate a specific diagnosis result. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the ultrasound images to generate a diagnosis result. The providing unit provides the diagnosis result generated by the analysis unit to a medical professional. For example, the providing unit provides the diagnosis result analyzed by the generation AI to a medical professional, who can then provide appropriate advice and treatment to the patient based on the result. As a result, the ultrasound imaging system according to an embodiment performs ultrasound image capture, analysis, and provision of diagnosis results in a single process, thereby reducing the burden on medical professionals.

[0069] Furthermore, the ultrasound imaging system includes an imaging unit that uses a small and lightweight ultrasound imaging device. The imaging unit uses a small and lightweight ultrasound imaging device. For example, the imaging unit uses an ultrasound imaging device that weighs 500 grams or less and is 20 centimeters or less in size. This allows a patient to easily take ultrasound images at home or at work. For example, a patient can take ultrasound images of a fetus at home. This allows a patient to easily take ultrasound images at home or at work using a small and lightweight device.

[0070] The analysis unit can analyze ultrasound images to determine whether or not there is an abnormality and generate specific diagnostic results. The analysis unit uses generative AI to analyze ultrasound images to determine whether or not there is an abnormality and generate specific diagnostic results. For example, the analysis unit analyzes ultrasound images to detect whether or not there is an abnormality. The analysis unit can detect an abnormality, for example, when a specific pattern or numerical value exceeds a threshold. The analysis unit can also generate specific diagnostic results. For example, the analysis unit can analyze ultrasound images to generate a specific disease name and recommended treatment. This makes it possible to generate quick and accurate diagnostic results by analyzing ultrasound images.

[0071] The providing unit can provide the diagnostic results analyzed by the generative AI to medical professionals. The providing unit provides the diagnostic results analyzed by the generative AI to medical professionals. For example, the providing unit provides the diagnostic results analyzed by the generative AI to medical professionals, who can then provide appropriate advice and treatment to patients based on the results. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes ultrasound images and generates diagnostic results. In this way, the accuracy and efficiency of diagnoses can be improved by providing the analysis results by the generative AI to medical professionals.

[0072] The imaging unit can be easily operated by a patient at home or at work. The imaging unit can be easily operated by a patient at home or at work. For example, the imaging unit has an intuitive user interface, allowing the patient to operate it easily. Furthermore, the imaging unit has simple operating procedures, allowing the patient to take ultrasound images in a short time. Furthermore, the imaging unit is designed to be easily operated by a patient at home or at work. For example, the imaging unit is easy to carry, allowing the patient to easily take ultrasound images at home or at work. This allows the patient to easily operate the unit at home or at work, reducing the burden of visiting a hospital.

[0073] The analysis unit can diagnose the fetus's growth status and whether or not there are any abnormalities. The analysis unit uses generative AI to diagnose the fetus's growth status and whether or not there are any abnormalities. For example, the analysis unit analyzes ultrasound images of the fetus to evaluate the fetus's growth status. The analysis unit can evaluate, for example, the fetus's weight, height, and organ development status. The analysis unit can also analyze ultrasound images of the fetus to diagnose whether or not there are any abnormalities. For example, the analysis unit can detect abnormalities when specific patterns or values ​​exceed a threshold. This allows the pregnant woman to receive appropriate advice and treatment by diagnosing the fetus's growth status and whether or not there are any abnormalities.

[0074] The imaging unit can estimate the patient's emotions and adjust the timing of imaging based on the estimated patient's emotions. The imaging unit estimates the patient's emotions and adjusts the timing of imaging based on the estimated patient's emotions. For example, if the patient is relaxed, the imaging unit starts imaging at a natural timing. Also, if the patient is nervous, the imaging unit can wait until the patient relaxes before starting imaging. Also, if the patient is in a hurry, the imaging unit can take images quickly. In this way, by adjusting the imaging timing according to the patient's emotions, it is possible to take images in a more natural state. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The imaging unit can analyze the patient's past imaging history and select the optimal imaging method. The imaging unit analyzes the patient's past imaging history and selects the optimal imaging method. For example, the imaging unit preferentially selects the imaging method that the patient has preferred in the past. The imaging unit can also select the imaging method with the highest success rate based on the patient's past imaging history. The imaging unit can also select the optimal imaging angle and settings based on the patient's past imaging history. In this way, the success rate of imaging is improved by selecting the optimal imaging method based on the past imaging history.

[0076] The imaging unit can perform filtering based on the patient's current health condition and living situation when taking an image. The imaging unit can perform filtering based on the patient's current health condition and living situation when taking an image. For example, if the patient is tired, the imaging unit selects an imaging method that requires less time. Also, if the patient is healthy, the imaging unit can perform detailed imaging. Also, the imaging unit can select the optimal imaging time period depending on the patient's living situation. This allows for more appropriate imaging by selecting an imaging method that suits the patient's health condition and living situation.

[0077] The imaging unit can select an appropriate imaging means according to the patient's input method when imaging. The imaging unit selects an appropriate imaging means according to the patient's input method when imaging. For example, when the patient uses voice input, the imaging unit performs imaging in response to a voice command. Furthermore, when the patient uses text input, the imaging unit can also perform imaging based on text instructions. Furthermore, when the patient uses image input, the imaging unit can also perform imaging based on image analysis. This improves the convenience of imaging by selecting an imaging means according to the patient's input method.

[0078] The imaging unit can be added with a function to automatically adjust the patient's position and posture when imaging. The imaging unit can be added with a function to automatically adjust the patient's position and posture when imaging. For example, if the patient is lying down, the imaging unit can adjust the patient to the optimal position before imaging. Also, if the patient is sitting, the imaging unit can adjust the patient's posture before imaging. Also, if the patient is standing, the imaging unit can adjust the patient's position before imaging. This makes it possible to automatically adjust the patient's position and posture to take optimal images.

[0079] The imaging unit can estimate the patient's emotions and determine the priority of the areas to be imaged based on the estimated patient's emotions. The imaging unit estimates the patient's emotions and determines the priority of the areas to be imaged based on the estimated patient's emotions. For example, if the patient is feeling anxious, the imaging unit prioritizes imaging of the most important areas. Also, if the patient is relaxed, the imaging unit can sequentially image detailed areas. Also, if the patient is in a hurry, the imaging unit can image only the most important areas. In this way, by determining the priority of the areas to be imaged according to the patient's emotions, it is possible to prioritize imaging of important areas. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The imaging unit can prioritize imaging of highly relevant imaging regions in consideration of the patient's geographical location information when imaging. The imaging unit prioritizes imaging of highly relevant imaging regions in consideration of the patient's geographical location information when imaging. For example, if the patient lives in a specific area, the imaging unit can prioritize imaging of regions related to diseases that are likely to occur in that area. Furthermore, if the patient is traveling, the imaging unit can also prioritize imaging of regions related to health risks at the travel destination. Furthermore, if the patient is in a specific environment, the imaging unit can select imaging regions in consideration of health risks related to that environment. In this way, by selecting imaging regions based on the patient's geographical location information, highly relevant regions can be preferentially imaged.

[0081] The imaging unit can analyze the patient's social media activity and obtain related health information when imaging. The imaging unit can analyze the patient's social media activity and obtain related health information when imaging. For example, the imaging unit can select the body part to be imaged based on health information shared by the patient on social media. The imaging unit can also estimate health risks from the patient's social media activity and image related body parts. The imaging unit can also select the body part to be imaged by referring to the health information of the patient's friends on social media. This allows for more appropriate imaging by obtaining health information based on the patient's social media activity.

[0082] The imaging unit can customize the imaging method by reflecting the patient's past feedback when imaging. The imaging unit customizes the imaging method by reflecting the patient's past feedback when imaging. For example, the imaging unit adjusts the imaging method based on feedback provided by the patient in the past. The imaging unit can also select optimal imaging settings from the patient's past feedback. The imaging unit can also improve the imaging procedure by referring to the patient's past feedback. In this way, the imaging method can be customized by reflecting the patient's past feedback.

[0083] The imaging unit can propose an appropriate imaging schedule based on the patient's lifestyle at the time of imaging. The imaging unit proposes an appropriate imaging schedule based on the patient's lifestyle at the time of imaging. For example, the imaging unit analyzes the patient's lifestyle and proposes the optimal imaging time period. The imaging unit can also adjust the imaging frequency according to the patient's lifestyle. The imaging unit can also customize the imaging schedule taking the patient's lifestyle into consideration. In this way, by proposing an imaging schedule based on the patient's lifestyle, the convenience of imaging is improved.

[0084] The analysis unit can estimate the patient's emotions and adjust the way the analysis is presented based on the estimated patient's emotions. The analysis unit can estimate the patient's emotions and adjust the way the analysis is presented based on the estimated patient's emotions. For example, the analysis unit can provide detailed analysis results when the patient is relaxed. The analysis unit can also provide concise and easy-to-understand analysis results when the patient is nervous. The analysis unit can also provide analysis results that focus on the main points when the patient is in a hurry. This allows for adjusting the way the analysis is presented depending on the patient's emotions, making it possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit can set the level of detail of the analysis based on the importance of the ultrasound image during analysis. The analysis unit sets the level of detail of the analysis based on the importance of the ultrasound image during analysis. For example, the analysis unit analyzes images of important areas in detail. The analysis unit can also analyze images of less important areas simply. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the ultrasound image.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the ultrasound image during analysis. The analysis unit applies different analysis algorithms depending on the category of the ultrasound image during analysis. For example, the analysis unit applies an analysis algorithm dedicated to fetuses to ultrasound images of fetuses. The analysis unit can also apply an analysis algorithm dedicated to the heart to ultrasound images of the heart. The analysis unit can also apply an analysis algorithm dedicated to the abdomen to ultrasound images of the abdomen. In this way, by applying an analysis algorithm depending on the category of the ultrasound image, the accuracy of the analysis is improved.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. For example, the analysis unit corrects the current analysis results based on the patient's past analysis results. The analysis unit can also grasp abnormal trends from the patient's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results.

[0088] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. For example, the analysis unit can improve the accuracy of the analysis by combining image analysis and text analysis. The analysis unit can also improve the accuracy of the analysis by combining machine learning and rule-based analysis methods. The analysis unit can also improve the accuracy of the analysis by combining different analysis algorithms. In this way, the accuracy of the analysis is improved by combining different analysis methods.

[0089] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, the analysis unit can perform a detailed analysis if the patient is relaxed. The analysis unit can also perform a concise analysis if the patient is nervous. The analysis unit can also perform an analysis that focuses on the main points if the patient is in a hurry. This allows for adjusting the length of the analysis according to the patient's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can set the analysis priority based on the time when the ultrasound image was taken during analysis. The analysis unit can set the analysis priority based on the time when the ultrasound image was taken during analysis. For example, the analysis unit prioritizes the analysis of the most recent ultrasound image. The analysis unit can also postpone the analysis of images taken earlier. The analysis unit can also adjust the analysis priority according to the time when the ultrasound image was taken. In this way, by determining the analysis priority based on the time when the ultrasound image was taken, the most recent information can be analyzed preferentially.

[0091] The analysis unit can set the order of analysis based on the relevance of the ultrasound images during analysis. The analysis unit sets the order of analysis based on the relevance of the ultrasound images during analysis. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the order of analysis according to the relevance of the images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the ultrasound images.

[0092] The analysis unit can set the use of technical terms for the analysis according to the patient's level of expertise during analysis. The analysis unit can set the use of technical terms for the analysis according to the patient's level of expertise during analysis. For example, if the patient has technical knowledge, the analysis unit uses detailed technical terms. Also, if the patient does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the way in which the analysis results are expressed according to the patient's level of expertise. In this way, by adjusting the technical terms for the analysis according to the patient's level of expertise, it is possible to provide analysis results that are easier to understand.

[0093] The analysis unit can integrate information from different data sources during analysis to improve the accuracy of the analysis. The analysis unit integrates information from different data sources during analysis to improve the accuracy of the analysis. For example, the analysis unit integrates and analyzes ultrasound images and blood test results. The analysis unit can also integrate and analyze ultrasound images and patient medical history. The analysis unit can also integrate and analyze ultrasound images and genetic information. In this way, the accuracy of the analysis is improved by integrating information from different data sources.

[0094] The providing unit can estimate the patient's emotions and adjust the way in which the diagnosis result is presented based on the estimated patient's emotions. The providing unit can estimate the patient's emotions and adjust the way in which the diagnosis result is presented based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can provide a detailed diagnosis result. If the patient is nervous, the providing unit can also provide a concise and easy-to-understand diagnosis result. If the patient is in a hurry, the providing unit can also provide a diagnosis result that focuses on the main points. In this way, by adjusting the way in which the diagnosis result is presented according to the patient's emotions, a more appropriate diagnosis result can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The providing unit can set the level of detail of the provided information based on the importance of the diagnostic result at the time of providing the information. The providing unit sets the level of detail of the provided information based on the importance of the diagnostic result at the time of providing the information. For example, the providing unit provides important diagnostic results in detail. The providing unit can also provide less important diagnostic results in a simplified manner. The providing unit can also determine the priority of the provided information according to the importance of the diagnostic result. This allows for efficient information provision by adjusting the level of detail of the provided information based on the importance of the diagnostic result.

[0096] The providing unit can apply different providing algorithms depending on the category of the diagnostic result at the time of providing. The providing unit applies different providing algorithms depending on the category of the diagnostic result at the time of providing. For example, the providing unit applies a providing algorithm dedicated to the fetus to the diagnostic result of the fetus. The providing unit can also apply a providing algorithm dedicated to the heart to the diagnostic result of the heart. The providing unit can also apply a providing algorithm dedicated to the abdomen to the diagnostic result of the abdomen. In this way, by applying a providing algorithm according to the category of the diagnostic result, more appropriate information can be provided.

[0097] The providing unit can improve the accuracy of provision at the time of provision by referring to the patient's past provision results. The providing unit improves the accuracy of provision at the time of provision by referring to the patient's past provision results. For example, the providing unit corrects the current provision results based on the patient's past provision results. The providing unit can also grasp abnormal trends from the patient's past provision results and reflect them in the current provision. The providing unit can also adjust the provision algorithm by referring to the patient's past provision results. In this way, the accuracy of provision is improved by referring to the patient's past provision results.

[0098] The providing unit can add a function to set the display method of the diagnostic results when providing the results. The providing unit adds a function to set the display method of the diagnostic results when providing the results. For example, the providing unit allows the patient to select the display style (graph, text, image, etc.) that they prefer. The providing unit can also display the diagnostic results in a format that is easy for the patient to understand. The providing unit can also suggest the optimal display method based on the patient's past selection history. In this way, by customizing the display method of the diagnostic results, it is possible to provide information in a format that is easy for the patient to understand.

[0099] The providing unit can estimate the patient's emotions and determine the priority of diagnostic results to be provided based on the estimated patient's emotions. The providing unit can estimate the patient's emotions and determine the priority of diagnostic results to be provided based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can prioritize providing the most important diagnostic results. Also, if the patient is relaxed, the providing unit can sequentially provide detailed diagnostic results. Also, if the patient is in a hurry, the providing unit can provide only the most important diagnostic results. In this way, by determining the priority of diagnostic results according to the patient's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The providing unit can set the priority of provision based on the time of submission of the diagnostic results at the time of provision. The providing unit sets the priority of provision based on the time of submission of the diagnostic results at the time of provision. For example, the providing unit provides the most recent diagnostic results preferentially. The providing unit can also postpone diagnostic results that have been submitted older. The providing unit can also adjust the priority of provision depending on the time of submission. In this way, by determining the priority of provision based on the time of submission of the diagnostic results, the latest information can be provided preferentially.

[0101] The providing unit can set the order of provision based on the relevance of the diagnostic results at the time of provision. The providing unit sets the order of provision based on the relevance of the diagnostic results at the time of provision. For example, the providing unit provides highly relevant diagnostic results preferentially. The providing unit can also postpone less relevant diagnostic results. The providing unit can also adjust the order of provision according to the relevance of the diagnostic results. As a result, adjusting the order of provision based on the relevance of the diagnostic results enables efficient information provision.

[0102] The providing unit can set the use of technical terms to be provided in accordance with the patient's level of expertise when providing the information. The providing unit sets the use of technical terms to be provided in accordance with the patient's level of expertise when providing the information. For example, if the patient has technical expertise, the providing unit uses detailed technical terms. Also, if the patient does not have technical expertise, the providing unit can explain the diagnosis results in simple terms. Also, the providing unit can adjust the way in which the diagnosis results are expressed in accordance with the patient's level of expertise. In this way, by adjusting the technical terms to be provided in accordance with the patient's level of expertise, it is possible to provide information that is easier to understand.

[0103] The providing unit can integrate information from different data sources at the time of providing to improve the accuracy of the information provided. The providing unit integrates information from different data sources at the time of providing to improve the accuracy of the information provided. For example, the providing unit integrates ultrasound images and blood test results and provides them. The providing unit can also integrate ultrasound images and patient medical history and provide them. The providing unit can also integrate ultrasound images and genetic information and provide them. In this way, the accuracy of the information provided is improved by integrating information from different data sources. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned imaging unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the imaging unit can capture ultrasound images using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the ultrasound images using a generative AI. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the analysis results to a medical professional. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned imaging unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the imaging unit can capture ultrasound images using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the ultrasound images using a generative AI. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the analysis results to a medical professional. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned imaging unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the imaging unit can capture ultrasound images using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the ultrasound images using a generative AI. The provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the analysis results to medical professionals. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned imaging unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the imaging unit can capture ultrasound images using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the ultrasound images using a generative AI. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the analysis results to a medical professional.

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

[0105] The analysis unit can estimate the patient's emotions and adjust the priority of analysis based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit can prioritize the most important analysis. If the patient is relaxed, the analysis unit can also sequentially perform detailed analyses. If the patient is in a hurry, the analysis unit can also prioritize analyses that focus on the main points. This allows important information to be provided quickly by adjusting the priority of analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The providing unit can estimate the patient's emotions and adjust the way in which the diagnosis results are presented based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can provide a detailed diagnosis result. If the patient is nervous, the providing unit can also provide a concise and easy-to-understand diagnosis result. If the patient is in a hurry, the providing unit can also provide a diagnosis result that focuses on the main points. This allows for adjusting the way in which the diagnosis results are presented according to the patient's emotions, thereby providing a more appropriate diagnosis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The imaging unit can estimate the patient's emotions and adjust the timing of imaging based on the estimated patient's emotions. For example, if the patient is relaxed, the imaging unit can start imaging at a natural timing. Also, if the patient is nervous, the imaging unit can wait until the patient relaxes before starting imaging. Also, if the patient is in a hurry, the imaging unit can take images quickly. This allows imaging in a more natural state by adjusting the imaging timing according to the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The analysis unit can estimate the patient's emotions and adjust the way the analysis is presented based on the estimated patient's emotions. For example, if the patient is relaxed, the analysis unit can provide detailed analysis results. If the patient is nervous, the analysis unit can also provide concise and easy-to-understand analysis results. If the patient is in a hurry, the analysis unit can also provide analysis results that focus on the main points. This allows for adjusting the way the analysis is presented depending on the patient's emotions, making it possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The providing unit can estimate the patient's emotions and determine the priority of diagnostic results to provide based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can prioritize providing the most important diagnostic results. Also, if the patient is relaxed, the providing unit can sequentially provide detailed diagnostic results. Also, if the patient is in a hurry, the providing unit can provide only the most important diagnostic results. In this way, by determining the priority of diagnostic results according to the patient's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generative AI. Generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The imaging unit can analyze the patient's past imaging history and select the optimal imaging method. For example, the imaging unit can prioritize the imaging method that the patient has previously preferred. The imaging unit can also select the imaging method with the highest success rate based on the patient's past imaging history. The imaging unit can also select the optimal imaging angle and settings based on the patient's past imaging history. In this way, the success rate of imaging is improved by selecting the optimal imaging method based on the patient's past imaging history.

[0111] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the ultrasound image. For example, the analysis unit applies an analysis algorithm specifically for fetuses to ultrasound images of fetuses. The analysis unit can also apply an analysis algorithm specifically for the heart to ultrasound images of the heart. The analysis unit can also apply an analysis algorithm specifically for the abdomen to ultrasound images of the abdomen. In this way, the accuracy of the analysis is improved by applying an analysis algorithm according to the category of the ultrasound image.

[0112] The providing unit can apply different providing algorithms depending on the category of the diagnostic result at the time of providing. For example, the providing unit applies a providing algorithm dedicated to the fetus to the diagnostic result of the fetus. The providing unit can also apply a providing algorithm dedicated to the heart to the diagnostic result of the heart. The providing unit can also apply a providing algorithm dedicated to the abdomen to the diagnostic result of the abdomen. In this way, by applying a providing algorithm according to the category of the diagnostic result, more appropriate information can be provided.

[0113] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. For example, the analysis unit can improve the accuracy of the analysis by combining image analysis and text analysis. The analysis unit can also improve the accuracy of the analysis by combining machine learning and rule-based analysis methods. The analysis unit can also improve the accuracy of the analysis by combining different analysis algorithms. In this way, the accuracy of the analysis is improved by combining different analysis methods.

[0114] The providing unit can add a function to set the display method of the diagnostic results when providing the results. For example, the providing unit can allow the patient to select the display style (graph, text, image, etc.) that they prefer. The providing unit can also display the diagnostic results in a format that is easy for the patient to understand. The providing unit can also suggest the optimal display method based on the patient's past selection history. In this way, by customizing the display method of the diagnostic results, it is possible to provide information in a format that is easy for the patient to understand.

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

[0116] Step 1: The imaging unit captures an ultrasound image. For example, the imaging unit uses a small and lightweight ultrasound imaging device that can be easily operated by a patient at home or at work. For example, the imaging unit allows a patient to capture an ultrasound image of a fetus at home. Step 2: The analysis unit uses the generation AI to analyze the ultrasound image captured by the imaging unit. For example, the analysis unit analyzes the ultrasound image and determines whether there are any abnormalities and generates a specific diagnosis result. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the ultrasound image and generates a diagnosis result. Step 3: The provider provides the diagnostic results generated by the analyzer to medical professionals. For example, the provider provides the diagnostic results analyzed by the generating AI to medical professionals, who can then provide appropriate advice and treatment to the patient based on the results.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0188] [Explanation of symbols]

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

Claims

1. an imaging unit that captures ultrasound images; an analysis unit that analyzes the ultrasound image captured by the imaging unit; a providing unit that provides a medical professional with the diagnostic result generated by the analysis unit; Equipped with A system characterized by:

2. The imaging unit is Uses a small and lightweight ultrasound imaging device 2. The system of claim 1.

3. The analysis unit Analyzes ultrasound images to determine whether there are any abnormalities and generate specific diagnostic results 2. The system of claim 1.

4. The providing unit Providing diagnostic results analyzed by generative AI to medical professionals 2. The system of claim 1.

5. The imaging unit is Easy for patients to operate at home or at work 2. The system of claim 1.

6. The analysis unit Diagnose the fetal growth status and whether there are any abnormalities 2. The system of claim 1.

7. The imaging unit is Estimates the patient's emotions and adjusts the timing of imaging based on the estimated emotions.

2. The system of claim 1.

8. The imaging unit is Analyze the patient's past imaging history and select the appropriate imaging method 2. The system of claim 1.

Images