system

Abstract

The system according to the embodiment aims to analyze the results of a medical checkup and propose an optimal action plan. [Solution] A system according to an embodiment includes an imaging unit, an analysis unit, an explanation unit, and a proposal unit. The imaging unit takes photographs of the results of a health check. The analysis unit analyzes the photographs taken by the imaging unit to extract numerical values. The explanation unit analyzes the numerical values ​​extracted by the analysis unit and provides explanations for each test item. The proposal unit comprehensively analyzes the numerical values ​​explained by the explanation unit to propose an action plan.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to understand the results of a health check and determine the next action to be taken.

[0005] The system according to the embodiment aims to analyze the results of a medical checkup and propose an optimal action plan. [Means for solving the problem]

[0006] The system according to the embodiment includes an imaging unit, an analysis unit, an explanation unit, and a proposal unit. The imaging unit photographs the results of the health check. The analysis unit analyzes the photographs taken by the imaging unit to extract numerical values. The explanation unit analyzes the numerical values ​​extracted by the analysis unit and provides explanations for each test item. The proposal unit comprehensively analyzes the numerical values ​​explained by the explanation unit to propose an action plan. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the results of a health check and propose an optimal action plan. [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 health checkup result analysis system according to an embodiment of the present invention photographs health checkup results, automatically analyzes the results, and presents an optimal action plan based on the results. In this health checkup result analysis system, a user photographs the results using a smartphone or other device, and a generation AI analyzes the photograph to extract the values ​​for each test item. For example, the system automatically recognizes values ​​for test items containing technical terms such as AST, γ-GTP, and LAP. The generation AI then analyzes the extracted values ​​and explains what each test item represents. For example, it explains that AST is an indicator of liver health, and γ-GTP is an indicator of alcohol intake and liver function. Furthermore, the generation AI comprehensively analyzes the values ​​and suggests next steps. For example, if a specific value is high, it specifically suggests which hospital and what examinations should be performed. This makes it easier for users to understand the health checkup results and take appropriate action. This system makes it easy to understand health checkup results and clarify the next steps to take. For example, if liver values ​​are high, it provides a specific action plan, such as suggesting a detailed examination at a hospital specializing in liver disease. This allows the health checkup result analysis system to enable the user to easily understand the results of the health checkup and clarify the next action to be taken.

[0029] A health checkup result analysis system according to an embodiment includes a photographing unit, an analysis unit, an explanation unit, and a proposal unit. The photographing unit photographs the health checkup results. For example, a user can photograph the health checkup results using a smartphone camera. The photographing unit can obtain clear images using, for example, a high-resolution camera. The photographing unit can also have a function for automatically adjusting the lighting conditions and shooting angle during photography. The analysis unit analyzes the photographs taken by the photographing unit to extract numerical values. For example, the analysis unit analyzes the photographs using a generation AI to extract numerical values ​​for each test item. The generation AI can perform image analysis using, for example, a deep learning model to accurately extract numerical values. The analysis unit can also extract text information from the photograph using, for example, OCR technology. The explanation unit analyzes the numerical values ​​extracted by the analysis unit and provides an explanation for each test item. For example, the explanation unit analyzes the numerical values ​​extracted using the generation AI and explains what each test item represents. The generation AI can analyze the meaning of the numerical values ​​using, for example, natural language processing technology, and provide an easy-to-understand explanation to the user. The suggestion unit comprehensively analyzes the values ​​explained by the explanation unit to propose an optimal action plan. For example, the suggestion unit uses a generation AI to comprehensively analyze the magnitude of the values ​​and propose what to do next. The generation AI can, for example, detect abnormal values ​​in the values ​​and specifically suggest which hospital should examine what should be examined. This allows the health checkup result analysis system according to the embodiment to easily allow the user to understand the results of the health checkup and clarify the next action to be taken.

[0030] The health checkup result analysis system includes a hospital suggestion unit that presents hospital names and test contents. The hospital suggestion unit specifically presents the user with the hospital they should visit next and the test contents they should undergo. For example, if a specific value is high, the hospital suggestion unit may suggest undergoing a detailed examination at a hospital specializing in liver disease. The hospital suggestion unit may also suggest the nearest hospital by taking into account the user's geographical location information. For example, the hospital suggestion unit may suggest an easily accessible hospital based on the user's current location. Furthermore, the hospital suggestion unit may refer to the user's insurance information to suggest a cost-effective hospital. For example, the hospital suggestion unit may suggest the most suitable hospital by taking into account the user's insurance coverage. This allows the hospital suggestion unit to specifically know the user's next hospital and the test contents they should undergo.

[0031] The health checkup result analysis system includes a detailed explanation unit that provides an explanation of each test item. The detailed explanation unit provides detailed information about each test item. For example, the detailed explanation unit explains that AST is an indicator of liver health, and that γ-GTP is an indicator of alcohol intake and liver function. The detailed explanation unit can also explain the normal value range and the meaning of abnormal values ​​for each test item. For example, the detailed explanation unit explains that the normal value range for AST is 10 to 40 U / L, and that abnormal values ​​are 50 U / L or higher. Furthermore, the detailed explanation unit can provide background information and related health information for each test item. For example, the detailed explanation unit explains that AST is an indicator of liver health, and suggests ways to improve lifestyle habits to maintain liver health. In this way, the detailed explanation unit makes it easier for the user to understand the detailed information about each test item.

[0032] The analysis unit can use generative AI to analyze photos and extract numerical values. Generative AI uses a deep learning model to perform image analysis and accurately extract numerical values. For example, generative AI inputs a photo of a health checkup result and outputs the numerical values ​​for each test item. Generative AI can also extract text information from photos using OCR technology. For example, generative AI analyzes the text information contained in a photo and extracts numerical values. Furthermore, generative AI can apply flexible analysis algorithms to accommodate test results in different formats. For example, generative AI can automatically recognize test results with different layouts and extract numerical values. This makes it possible to accurately extract numerical values ​​from photos using generative AI.

[0033] The explanation unit can analyze the values ​​extracted using the generation AI and provide an explanation for each test item. The generation AI uses natural language processing technology to analyze the meaning of the values ​​and provide an easy-to-understand explanation to the user. For example, the generation AI can explain that AST is an indicator of liver health. The generation AI can also explain the normal value range and the meaning of abnormal values ​​for each test item. For example, the generation AI can explain that the normal value range for γ-GTP is 10 to 50 U / L, and that abnormal values ​​are 60 U / L or higher. The generation AI can also provide background information and related health information for each test item. For example, the generation AI can explain that LAP is an indicator of the health of the liver and biliary tract, and suggest lifestyle improvements to maintain liver and biliary tract health. This allows the generation AI to accurately explain each test item.

[0034] The suggestion unit can use the generation AI to comprehensively analyze the magnitude of the values ​​and propose an action plan. The generation AI detects abnormal values ​​and specifically recommends which hospital to visit and what needs to be examined. For example, if the AST value is high, the generation AI might suggest undergoing detailed testing at a hospital specializing in liver disease. The generation AI can also comprehensively analyze the magnitude of the values ​​and propose the next action to take. For example, if the γ-GTP and LAP values ​​are high, the generation AI might suggest reducing alcohol intake and improving lifestyle habits to maintain liver health. Furthermore, the generation AI can consider the user's geographic location information to suggest the nearest hospital. For example, the generation AI might suggest a hospital that is easily accessible based on the user's current location. This allows the generation AI to comprehensively analyze the magnitude of the values ​​and propose the optimal action plan.

[0035] The image capturing unit can have a function to detect camera shake caused by the user when capturing an image and automatically correct it. The image capturing unit detects camera shake using an acceleration sensor or a gyro sensor. For example, the image capturing unit detects camera shake when capturing an image and automatically corrects it using image processing technology. The image capturing unit can also correct camera shake by combining multiple images. For example, the image capturing unit can correct camera shake by combining multiple images captured in succession. Furthermore, the image capturing unit can automatically set an optimal shutter speed. For example, the image capturing unit can detect camera shake and adjust the shutter speed to capture a clear photo. In this way, the image capturing unit can automatically correct camera shake to capture a clear photo.

[0036] The imaging unit can automatically recognize the type and format of the paper on which the test results are printed when capturing an image and select the imaging mode. The imaging unit automatically recognizes the type and format of the paper on which the test results are printed using image analysis technology. For example, the imaging unit can automatically recognize the type of paper on which the test results are printed and select the optimal imaging mode. The imaging unit can also automatically recognize the format of the test results and select the optimal imaging mode. For example, the imaging unit can analyze the format of the test results and set an appropriate resolution and imaging angle. The imaging unit can also automatically recognize the size of the paper on which the test results are printed and select the optimal imaging mode. For example, the imaging unit can adjust the imaging range according to the paper size so that the entire image is captured clearly. This allows the imaging unit to select the optimal imaging mode according to the type and format of the paper on which the test results are printed.

[0037] The photographing unit may have a function of detecting the user's ambient light when photographing and automatically adjusting the exposure. The photographing unit detects ambient light using a light sensor. For example, the photographing unit detects ambient light when photographing and automatically sets the optimal exposure. The photographing unit can also adjust the exposure in real time according to changes in ambient light. For example, the photographing unit automatically uses a flash when there is insufficient ambient light. Furthermore, the photographing unit can adjust the exposure to an appropriate brightness when there is excessive ambient light. For example, the photographing unit lowers the exposure when there is strong light to prevent the photo from being blown out. This allows the photographing unit to automatically set the optimal exposure according to the ambient light.

[0038] The photographing unit can automatically adjust photographing settings according to the camera performance of the user's smartphone when photographing. The photographing unit detects the camera performance of the smartphone and automatically adjusts the optimal photographing settings. For example, the photographing unit detects the camera performance of the smartphone and automatically sets the optimal resolution. The photographing unit can also automatically set the optimal white balance according to the camera performance of the smartphone. For example, the photographing unit adjusts the white balance of the camera so that the color of the photo is natural. Furthermore, the photographing unit can automatically set the optimal ISO sensitivity according to the camera performance of the smartphone. For example, the photographing unit increases the ISO sensitivity when photographing in a dark environment to take a bright photo. This allows the photographing unit to automatically adjust the optimal photographing settings according to the camera performance of the smartphone.

[0039] The analysis unit can automatically detect stains and creases on the paper of the test results during analysis, thereby improving the accuracy of the analysis. The analysis unit automatically detects stains and creases on the paper of the test results using image analysis technology. For example, the analysis unit automatically detects stains on the paper of the test results, thereby improving the accuracy of the analysis. The analysis unit can also automatically detect creases on the paper of the test results, thereby improving the accuracy of the analysis. For example, the analysis unit detects creases on the paper and performs analysis by removing the effects of the creases. The analysis unit can also automatically detect tears on the paper of the test results, thereby improving the accuracy of the analysis. For example, the analysis unit detects tears on the paper and performs analysis by completing the torn parts. In this way, the analysis unit can automatically detect stains and creases on the paper of the test results, thereby improving the accuracy of the analysis.

[0040] The analysis unit can apply flexible analysis algorithms to accommodate test results in different formats during analysis. The analysis unit automatically recognizes test results in different formats and applies an analysis algorithm. For example, the analysis unit automatically recognizes test results in different formats and selects an appropriate analysis algorithm. The analysis unit can also adjust the analysis algorithm to accommodate test results in different languages. For example, the analysis unit analyzes test results in different languages ​​and extracts numerical values. The analysis unit can also apply analysis algorithms to accommodate test results in different layouts. For example, the analysis unit automatically recognizes test results in different layouts and extracts numerical values. This enables the analysis unit to flexibly analyze test results in different formats.

[0041] The analysis unit may have a function for detecting abnormal values ​​by comparing the user's past test results during analysis. The analysis unit compares the user's past test results with the current results and automatically detects abnormal values. For example, the analysis unit compares the user's past test results with the current results and detects abnormal values. The analysis unit can also analyze trends in the user's past test results and detect abnormal values. For example, the analysis unit analyzes trends in past test results and detects abnormal values. Furthermore, the analysis unit can compare the user's past test results with the current results and display abnormal values ​​in a graph. For example, the analysis unit visually displays abnormal values ​​in a graph and presents them to the user in an easy-to-understand manner. In this way, the analysis unit can detect abnormal values ​​by comparing them with past test results, thereby enabling early detection of abnormalities.

[0042] The analysis unit can customize the analysis results based on the user's age and gender during analysis. The analysis unit sets appropriate reference values ​​and customizes the analysis results based on the user's age. For example, the analysis unit sets a normal value range based on the user's age and provides the analysis results. The analysis unit can also set appropriate reference values ​​and customize the analysis results based on the user's gender. For example, the analysis unit sets a normal value range based on the user's gender and provides the analysis results. Furthermore, the analysis unit can customize the analysis results and provide individual advice based on the user's age and gender. For example, the analysis unit suggests appropriate ways to improve lifestyle habits based on the user's age and gender. This allows the analysis unit to provide analysis results that suit the user's age and gender.

[0043] The explanation unit may have a function to provide background information and related health information for each test item during explanation. The explanation unit provides the background information and related health information for each test item. For example, the explanation unit may explain that AST is an indicator of liver health and suggest lifestyle improvements to maintain liver health. The explanation unit may also provide the latest medical papers and research data related to each test item. For example, the explanation unit may provide the latest medical papers related to AST, allowing the user to gain a deeper understanding of the test results. The explanation unit may also integrate the background information and related health information for each test item and provide them. For example, the explanation unit may integrate the background information for AST and related health information to provide an easy-to-understand explanation for the user. In this way, the explanation unit provides the background information and related health information for each test item, allowing the user to gain a deeper understanding of the test results.

[0044] The explanation unit may have a function of adjusting the level of detail of the explanation according to the user's level of understanding during explanation. The explanation unit adjusts the level of detail of the explanation according to the user's level of understanding. For example, if the user's level of understanding is low, the explanation unit provides a concise and easy-to-understand explanation. Furthermore, if the user's level of understanding is high, the explanation unit can also provide a detailed explanation. For example, the explanation unit provides detailed information and background information about the test items according to the user's level of understanding. Furthermore, the explanation unit can adjust the level of detail of the explanation in real time according to the user's level of understanding. For example, the explanation unit evaluates the user's level of understanding and adjusts the level of detail of the explanation based on the evaluation result. In this way, the explanation unit can provide an explanation that is easy for the user to understand by adjusting the level of detail of the explanation according to the user's level of understanding.

[0045] The explanation unit can provide a customized explanation based on the user's past health condition and lifestyle habits when providing an explanation. The explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. For example, the explanation unit provides an appropriate explanation based on the user's past health condition. The explanation unit can also provide an appropriate explanation based on the user's lifestyle habits. For example, the explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. Furthermore, the explanation unit can provide individual advice by referring to the user's past test results and lifestyle habit data. For example, the explanation unit suggests ways to improve lifestyle habits based on the user's past test results. In this way, the explanation unit can provide more appropriate information to the user by providing an explanation customized based on the user's past health condition and lifestyle habits.

[0046] The explanation unit can provide multilingual explanations according to the user's language setting when providing explanations. The explanation unit provides multilingual explanations according to the user's language setting. For example, the explanation unit provides explanations in an appropriate language based on the user's language setting. The explanation unit can also provide a language switching function when the user uses multiple languages. For example, the explanation unit provides multilingual explanations based on the user's language setting. Furthermore, the explanation unit has a function for learning technical terms of a specific language to improve translation accuracy. For example, the explanation unit references a database of technical terms to improve translation accuracy of medical terms. As a result, the explanation unit provides multilingual explanations according to the user's language setting, allowing the user to receive explanations in a language that is easy for the user to understand.

[0047] The suggestion unit can suggest an action plan by referring to the user's past medical history when making a suggestion. The suggestion unit suggests an optimal action plan by referring to the user's past medical history. For example, the suggestion unit suggests an appropriate action plan based on the user's past medical history. The suggestion unit can also analyze the user's past medical history and suggest an optimal action plan. For example, the suggestion unit provides individual advice based on the user's past medical history. Furthermore, the suggestion unit can suggest specific hospitals and test contents by referring to the user's past medical history. For example, the suggestion unit suggests a hospital with a specialist based on the user's past medical history. In this way, the suggestion unit can take more appropriate action for the user by suggesting an optimal action plan based on the user's past medical history.

[0048] When making a proposal, the suggestion unit can suggest specific improvement measures based on the user's lifestyle habits and dietary content. The suggestion unit suggests specific improvement measures based on the user's lifestyle habits and dietary content. For example, the suggestion unit suggests an appropriate exercise plan based on the user's lifestyle habits. The suggestion unit can also suggest a nutritionally balanced meal plan based on the user's dietary content. For example, the suggestion unit analyzes the user's dietary content and suggests specific meal improvement measures. Furthermore, the suggestion unit can comprehensively analyze the user's lifestyle habits and dietary content and suggest specific improvement measures. For example, the suggestion unit suggests a specific action plan for maintaining and improving health based on the user's lifestyle habits and dietary content. As a result, the suggestion unit suggests specific improvement measures based on the user's lifestyle habits and dietary content, allowing the user to take specific actions for maintaining and improving their health.

[0049] The suggestion unit can suggest hospitals and clinics taking into account the user's geographical location information when making a suggestion. The suggestion unit suggests the most suitable hospital or clinic taking into account the user's geographical location information. For example, the suggestion unit suggests the nearest hospital based on the user's current location. The suggestion unit can also suggest an easily accessible hospital taking into account the user's geographical location information. For example, the suggestion unit suggests the most suitable hospital taking into account the user's geographical location information and means of transportation. Furthermore, the suggestion unit can reflect the hospital's congestion status and reservation status in real time based on the user's geographical location information. For example, the suggestion unit acquires the congestion status of hospitals within the user's living area in real time and reflects it in the suggestion. In this way, the suggestion unit can suggest the most suitable hospital or clinic taking into account the user's geographical location information, allowing the user to select a medical institution that is easily accessible.

[0050] The suggestion unit can suggest an action plan by referring to the user's insurance information when making the suggestion. The suggestion unit suggests a cost-effective action plan by referring to the user's insurance information. For example, the suggestion unit suggests a cost-effective treatment based on the user's insurance information. The suggestion unit can also suggest an optimal hospital by taking into account the user's insurance coverage. For example, the suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. Furthermore, the suggestion unit can also provide individual advice by referring to the user's insurance information. For example, the suggestion unit suggests an action plan for receiving economically optimal medical services based on the user's insurance information. As a result, the suggestion unit suggests a cost-effective action plan by referring to the user's insurance information, allowing the user to receive economically optimal medical services.

[0051] When suggesting a hospital, the hospital suggestion unit can suggest the most suitable hospital by referring to the user's past medical history. The hospital suggestion unit suggests the most suitable hospital by referring to the user's past medical history. For example, the hospital suggestion unit suggests a hospital with an appropriate specialist based on the user's past medical history. The hospital suggestion unit can also analyze the user's past medical history and suggest the most suitable hospital. For example, the hospital suggestion unit provides individual advice based on the user's past medical history. Furthermore, the hospital suggestion unit can suggest specific hospitals and examination contents by referring to the user's past medical history. For example, the hospital suggestion unit suggests a hospital with a specialist based on the user's past medical history. In this way, the hospital suggestion unit can select the most suitable medical institution for the user by suggesting the most suitable hospital based on the user's past medical history.

[0052] The hospital suggestion unit may have a function of reflecting the congestion status of hospitals within the user's living area in real time when suggesting a hospital. The hospital suggestion unit acquires the congestion status of hospitals within the user's living area in real time and reflects it in the suggestion. For example, the hospital suggestion unit acquires the congestion status of hospitals within the user's living area in real time and suggests hospitals with short waiting times. The hospital suggestion unit can also acquire the reservation status of hospitals within the user's living area in real time and reflect it in the suggestion. For example, the hospital suggestion unit acquires the reservation status of hospitals within the user's living area in real time and suggests hospitals where it is easy to make an appointment. Furthermore, the hospital suggestion unit can also acquire the waiting times of hospitals within the user's living area in real time and reflect it in the suggestion. For example, the hospital suggestion unit acquires the waiting times of hospitals within the user's living area in real time and suggests hospitals with short waiting times. In this way, the hospital suggestion unit reflects the congestion status of hospitals within the user's living area in real time, allowing the user to select a hospital with a short waiting time.

[0053] The hospital suggestion unit can suggest the most suitable hospital taking into consideration the user's insurance coverage when suggesting a hospital. The hospital suggestion unit suggests the most suitable hospital taking into consideration the user's insurance coverage. For example, the hospital suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. The hospital suggestion unit can also suggest the most suitable hospital taking into consideration the user's insurance coverage. For example, the hospital suggestion unit provides individual advice based on the user's insurance coverage. Furthermore, the hospital suggestion unit can suggest specific hospitals and examination contents by referring to the user's insurance coverage. For example, the hospital suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. In this way, the hospital suggestion unit suggests the most suitable hospital taking into consideration the user's insurance coverage, allowing the user to receive the most economically optimal medical service.

[0054] When suggesting a hospital, the hospital suggestion unit can suggest the most suitable hospital by taking into consideration the user's means of transportation and travel time. The hospital suggestion unit suggests the most suitable hospital by taking into consideration the user's means of transportation and travel time. For example, the hospital suggestion unit suggests an easily accessible hospital based on the user's means of transportation. The hospital suggestion unit can also suggest the most suitable hospital by taking into consideration the user's travel time. For example, the hospital suggestion unit suggests the most suitable hospital by comprehensively taking into consideration the user's means of transportation and travel time. Furthermore, the hospital suggestion unit can reflect the hospital's congestion status and reservation status in real time based on the user's means of transportation and travel time. For example, the hospital suggestion unit suggests a hospital with a short waiting time by taking into consideration the user's means of transportation and travel time. In this way, the hospital suggestion unit can suggest the most suitable hospital by taking into consideration the user's means of transportation and travel time, allowing the user to select a medical institution that is easily accessible.

[0055] The detailed explanation unit may have a function to provide medical papers and research data related to each test item during the detailed explanation. The detailed explanation unit provides medical papers and research data related to each test item. For example, the detailed explanation unit provides the latest medical papers related to each test item. The detailed explanation unit can also provide research data related to each test item. For example, the detailed explanation unit provides an integrated version of medical papers and research data related to each test item. Furthermore, the detailed explanation unit can also provide an integrated version of background information and related health information for each test item. For example, the detailed explanation unit integrates background information and related health information for each test item to provide an easy-to-understand explanation to the user. In this way, the detailed explanation unit provides medical papers and research data related to each test item, allowing the user to gain a deeper understanding of the test results.

[0056] The detailed explanation unit may have a function of adjusting the level of detail of the explanation according to the user's level of understanding when providing the detailed explanation. The detailed explanation unit adjusts the level of detail of the explanation according to the user's level of understanding. For example, the detailed explanation unit provides a concise and easy-to-understand explanation when the user's level of understanding is low. The detailed explanation unit can also provide a detailed explanation when the user's level of understanding is high. For example, the detailed explanation unit provides detailed information and background information about the test items according to the user's level of understanding. Furthermore, the detailed explanation unit can adjust the level of detail of the explanation in real time according to the user's level of understanding. For example, the detailed explanation unit evaluates the user's level of understanding and adjusts the level of detail of the explanation based on the evaluation result. In this way, the detailed explanation unit can provide an explanation that is easy for the user to understand by adjusting the level of detail of the explanation according to the user's level of understanding.

[0057] The detailed explanation unit can provide a customized explanation based on the user's past health condition and lifestyle habits when providing the detailed explanation. The detailed explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. For example, the detailed explanation unit provides an appropriate explanation based on the user's past health condition. The detailed explanation unit can also provide an appropriate explanation based on the user's lifestyle habits. For example, the detailed explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. Furthermore, the detailed explanation unit can provide individual advice by referring to the user's past test results and lifestyle habit data. For example, the detailed explanation unit suggests ways to improve lifestyle habits based on the user's past test results. In this way, the detailed explanation unit can provide more appropriate information to the user by providing an explanation customized based on the user's past health condition and lifestyle habits.

[0058] The detailed explanation unit can provide a detailed explanation in multiple languages ​​according to the user's language setting when providing the detailed explanation. The detailed explanation unit provides a detailed explanation in multiple languages ​​according to the user's language setting. For example, the detailed explanation unit provides a detailed explanation in an appropriate language based on the user's language setting. The detailed explanation unit can also provide a language switching function when the user uses multiple languages. For example, the detailed explanation unit provides a detailed explanation in multiple languages ​​based on the user's language setting. Furthermore, the detailed explanation unit has a function for learning technical terms of a specific language to improve translation accuracy. For example, the detailed explanation unit references a database of technical terms to improve translation accuracy of medical terms. As a result, the detailed explanation unit provides a detailed explanation in multiple languages ​​according to the user's language setting, allowing the user to receive an explanation in a language that is easy for the user to understand.

[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 analysis unit can compare the user's past health checkup results with the current results to detect trends in abnormal values. For example, the analysis unit can store past health checkup results in a database and compare them with the current results to detect fluctuations in abnormal values. The analysis unit can also display trends in abnormal values ​​in a graph, providing the user with a visually easy-to-understand format. Furthermore, the analysis unit can predict future health risks and suggest preventive measures based on trends in abnormal values. This allows the user to understand changes in their health condition and take measures early.

[0061] When photographing a user's health checkup results, the photographing unit can automatically recognize the type and format of the paper and select the optimal photographing mode. For example, the photographing unit can automatically recognize the type of paper on which the test results are printed and set the optimal resolution and photographing angle. The photographing unit can also analyze the format of the test results and automatically adjust the appropriate photographing range. Furthermore, the photographing unit can select the photographing mode according to the size of the paper on which the test results are printed so that the entire image is captured clearly. This allows the user to accurately photograph the test results and the analysis unit to extract accurate numerical values.

[0062] The analysis unit can apply flexible analysis algorithms to accommodate health checkup results in different formats. For example, the analysis unit can automatically recognize test results in different layouts and extract numerical values. The analysis unit can also adjust the analysis algorithm to accommodate test results in different languages. Furthermore, the analysis unit can integrate test results in different formats to provide users with consistent analysis results. This allows users to receive accurate analysis of test results in any format.

[0063] The suggestion unit can suggest specific improvement measures based on the user's lifestyle habits and dietary content. For example, the suggestion unit can analyze the user's dietary content and suggest a nutritionally balanced meal plan. The suggestion unit can also consider the user's exercise habits and suggest an appropriate exercise plan. Furthermore, the suggestion unit can comprehensively analyze the user's entire lifestyle habits and suggest a specific action plan for maintaining and improving health. This allows the user to receive specific improvement measures based on their own lifestyle habits.

[0064] The analysis unit can automatically detect stains and creases on the paper of the test results during analysis, thereby improving the accuracy of the analysis. For example, the analysis unit can automatically detect stains on the paper of the test results, thereby improving the accuracy of the analysis. The analysis unit can also automatically detect creases on the paper of the test results and perform analysis after removing the effects of the creases. Furthermore, the analysis unit can automatically detect tears on the paper of the test results and perform analysis after completing the torn parts. This allows the user to receive an accurate analysis regardless of the condition of the paper of the test results.

[0065] The explanation unit may have a function to provide background information and related health information for each test item during the explanation. For example, the explanation unit may explain that AST is an indicator of liver health and suggest lifestyle improvements to maintain liver health. The explanation unit may also provide the latest medical papers and research data related to each test item. Furthermore, the explanation unit may provide an integrated version of the background information and related health information for each test item, allowing the user to gain a deeper understanding of the test results. This makes it easier for the user to understand the background information and related health information for each test item.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The photographing unit takes a photograph of the health checkup results. For example, a user can take a photograph of the health checkup results using a smartphone camera. The photographing unit can obtain clear images using a high-resolution camera and can have a function to automatically adjust the lighting conditions and shooting angle when taking a photograph. Step 2: The analysis unit analyzes the photos taken by the photography unit and extracts numerical values. For example, it uses generative AI to analyze the photos and extract the numerical values ​​for each test item. It can perform image analysis using deep learning models and OCR technology to accurately extract numerical values. Step 3: The explanation unit analyzes the values ​​extracted by the analysis unit and provides an explanation for each test item. For example, it uses generation AI to analyze the extracted values ​​and explain what each test item represents. It uses natural language processing technology to analyze the meaning of the values ​​and provide an explanation that is easy for the user to understand. Step 4: The proposal section performs a comprehensive analysis of the values ​​explained by the explanation section and proposes the optimal action plan. For example, generative AI can be used to comprehensively analyze the magnitude of the values ​​and propose what to do next. It can detect abnormal values ​​and provide specific information on which hospitals need to examine what.

[0068] (Example 2) A health checkup result analysis system according to an embodiment of the present invention photographs health checkup results, automatically analyzes the results, and presents an optimal action plan based on the results. In this health checkup result analysis system, a user photographs the results using a smartphone or other device, and a generation AI analyzes the photograph to extract the values ​​for each test item. For example, the system automatically recognizes values ​​for test items containing technical terms such as AST, γ-GTP, and LAP. The generation AI then analyzes the extracted values ​​and explains what each test item represents. For example, it explains that AST is an indicator of liver health, and γ-GTP is an indicator of alcohol intake and liver function. Furthermore, the generation AI comprehensively analyzes the values ​​and suggests next steps. For example, if a specific value is high, it specifically suggests which hospital and what examinations should be performed. This makes it easier for users to understand the health checkup results and take appropriate action. This system makes it easy to understand health checkup results and clarify the next steps to take. For example, if liver values ​​are high, it provides a specific action plan, such as suggesting a detailed examination at a hospital specializing in liver disease. This allows the health checkup result analysis system to enable the user to easily understand the results of the health checkup and clarify the next action to be taken.

[0069] A health checkup result analysis system according to an embodiment includes a photographing unit, an analysis unit, an explanation unit, and a proposal unit. The photographing unit photographs the health checkup results. For example, a user can photograph the health checkup results using a smartphone camera. The photographing unit can obtain clear images using, for example, a high-resolution camera. The photographing unit can also have a function for automatically adjusting the lighting conditions and shooting angle during photography. The analysis unit analyzes the photographs taken by the photographing unit to extract numerical values. For example, the analysis unit analyzes the photographs using a generation AI to extract numerical values ​​for each test item. The generation AI can perform image analysis using, for example, a deep learning model to accurately extract numerical values. The analysis unit can also extract text information from the photograph using, for example, OCR technology. The explanation unit analyzes the numerical values ​​extracted by the analysis unit and provides an explanation for each test item. For example, the explanation unit analyzes the numerical values ​​extracted using the generation AI and explains what each test item represents. The generation AI can analyze the meaning of the numerical values ​​using, for example, natural language processing technology, and provide an easy-to-understand explanation to the user. The suggestion unit comprehensively analyzes the values ​​explained by the explanation unit to propose an optimal action plan. For example, the suggestion unit uses a generation AI to comprehensively analyze the magnitude of the values ​​and propose what to do next. The generation AI can, for example, detect abnormal values ​​in the values ​​and specifically suggest which hospital should examine what should be examined. This allows the health checkup result analysis system according to the embodiment to easily allow the user to understand the results of the health checkup and clarify the next action to be taken.

[0070] The health checkup result analysis system includes a hospital suggestion unit that presents hospital names and test contents. The hospital suggestion unit specifically presents the user with the hospital they should visit next and the test contents they should undergo. For example, if a specific value is high, the hospital suggestion unit may suggest undergoing a detailed examination at a hospital specializing in liver disease. The hospital suggestion unit may also suggest the nearest hospital by taking into account the user's geographical location information. For example, the hospital suggestion unit may suggest an easily accessible hospital based on the user's current location. Furthermore, the hospital suggestion unit may refer to the user's insurance information to suggest a cost-effective hospital. For example, the hospital suggestion unit may suggest the most suitable hospital by taking into account the user's insurance coverage. This allows the hospital suggestion unit to specifically know the user's next hospital and the test contents they should undergo.

[0071] The health checkup result analysis system includes a detailed explanation unit that provides an explanation of each test item. The detailed explanation unit provides detailed information about each test item. For example, the detailed explanation unit explains that AST is an indicator of liver health, and that γ-GTP is an indicator of alcohol intake and liver function. The detailed explanation unit can also explain the normal value range and the meaning of abnormal values ​​for each test item. For example, the detailed explanation unit explains that the normal value range for AST is 10 to 40 U / L, and that abnormal values ​​are 50 U / L or higher. Furthermore, the detailed explanation unit can provide background information and related health information for each test item. For example, the detailed explanation unit explains that AST is an indicator of liver health, and suggests ways to improve lifestyle habits to maintain liver health. In this way, the detailed explanation unit makes it easier for the user to understand the detailed information about each test item.

[0072] The analysis unit can use generative AI to analyze photos and extract numerical values. Generative AI uses a deep learning model to perform image analysis and accurately extract numerical values. For example, generative AI inputs a photo of a health checkup result and outputs the numerical values ​​for each test item. Generative AI can also extract text information from photos using OCR technology. For example, generative AI analyzes the text information contained in a photo and extracts numerical values. Furthermore, generative AI can apply flexible analysis algorithms to accommodate test results in different formats. For example, generative AI can automatically recognize test results with different layouts and extract numerical values. This makes it possible to accurately extract numerical values ​​from photos using generative AI.

[0073] The explanation unit can analyze the values ​​extracted using the generation AI and provide an explanation for each test item. The generation AI uses natural language processing technology to analyze the meaning of the values ​​and provide an easy-to-understand explanation to the user. For example, the generation AI can explain that AST is an indicator of liver health. The generation AI can also explain the normal value range and the meaning of abnormal values ​​for each test item. For example, the generation AI can explain that the normal value range for γ-GTP is 10 to 50 U / L, and that abnormal values ​​are 60 U / L or higher. The generation AI can also provide background information and related health information for each test item. For example, the generation AI can explain that LAP is an indicator of the health of the liver and biliary tract, and suggest lifestyle improvements to maintain liver and biliary tract health. This allows the generation AI to accurately explain each test item.

[0074] The suggestion unit can use the generation AI to comprehensively analyze the magnitude of the values ​​and propose an action plan. The generation AI detects abnormal values ​​and specifically recommends which hospital to visit and what needs to be examined. For example, if the AST value is high, the generation AI might suggest undergoing detailed testing at a hospital specializing in liver disease. The generation AI can also comprehensively analyze the magnitude of the values ​​and propose the next action to take. For example, if the γ-GTP and LAP values ​​are high, the generation AI might suggest reducing alcohol intake and improving lifestyle habits to maintain liver health. Furthermore, the generation AI can consider the user's geographic location information to suggest the nearest hospital. For example, the generation AI might suggest a hospital that is easily accessible based on the user's current location. This allows the generation AI to comprehensively analyze the magnitude of the values ​​and propose the optimal action plan.

[0075] The image capturing unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user's emotions. The image capturing unit estimates the user's emotions using facial expression recognition technology. For example, the image capturing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The image capturing unit can also estimate the user's emotions using voice analysis technology. For example, the image capturing unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the image capturing unit can adjust the timing of capturing images based on the user's emotions. For example, if the user is nervous, the image capturing unit waits until the user relaxes before starting capturing images. Also, if the user is in a hurry, the image capturing unit can start capturing images immediately. This allows the image capturing unit to capture images at the optimal timing according to the user's emotions.

[0076] The image capturing unit can have a function to detect camera shake caused by the user when capturing an image and automatically correct it. The image capturing unit detects camera shake using an acceleration sensor or a gyro sensor. For example, the image capturing unit detects camera shake when capturing an image and automatically corrects it using image processing technology. The image capturing unit can also correct camera shake by combining multiple images. For example, the image capturing unit can correct camera shake by combining multiple images captured in succession. Furthermore, the image capturing unit can automatically set an optimal shutter speed. For example, the image capturing unit can detect camera shake and adjust the shutter speed to capture a clear photo. In this way, the image capturing unit can automatically correct camera shake to capture a clear photo.

[0077] The imaging unit can automatically recognize the type and format of the paper on which the test results are printed when capturing an image and select the imaging mode. The imaging unit automatically recognizes the type and format of the paper on which the test results are printed using image analysis technology. For example, the imaging unit can automatically recognize the type of paper on which the test results are printed and select the optimal imaging mode. The imaging unit can also automatically recognize the format of the test results and select the optimal imaging mode. For example, the imaging unit can analyze the format of the test results and set an appropriate resolution and imaging angle. The imaging unit can also automatically recognize the size of the paper on which the test results are printed and select the optimal imaging mode. For example, the imaging unit can adjust the imaging range according to the paper size so that the entire image is captured clearly. This allows the imaging unit to select the optimal imaging mode according to the type and format of the paper on which the test results are printed.

[0078] The image capturing unit can estimate the user's emotions and determine the priority of image capturing based on the estimated user's emotions. The image capturing unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the image capturing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The image capturing unit can also analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the image capturing unit can determine the priority of image capturing based on the user's emotions. For example, if the user is nervous, the image capturing unit can prioritize other tasks until the user relaxes. Furthermore, if the user is in a hurry, the image capturing unit can also give top priority to image capturing. This allows the image capturing unit to determine the priority of image capturing according to the user's emotions.

[0079] The photographing unit may have a function of detecting the user's ambient light when photographing and automatically adjusting the exposure. The photographing unit detects ambient light using a light sensor. For example, the photographing unit detects ambient light when photographing and automatically sets the optimal exposure. The photographing unit can also adjust the exposure in real time according to changes in ambient light. For example, the photographing unit automatically uses a flash when there is insufficient ambient light. Furthermore, the photographing unit can adjust the exposure to an appropriate brightness when there is excessive ambient light. For example, the photographing unit lowers the exposure when there is strong light to prevent the photo from being blown out. This allows the photographing unit to automatically set the optimal exposure according to the ambient light.

[0080] The photographing unit can automatically adjust photographing settings according to the camera performance of the user's smartphone when photographing. The photographing unit detects the camera performance of the smartphone and automatically adjusts the optimal photographing settings. For example, the photographing unit detects the camera performance of the smartphone and automatically sets the optimal resolution. The photographing unit can also automatically set the optimal white balance according to the camera performance of the smartphone. For example, the photographing unit adjusts the white balance of the camera so that the color of the photo is natural. Furthermore, the photographing unit can automatically set the optimal ISO sensitivity according to the camera performance of the smartphone. For example, the photographing unit increases the ISO sensitivity when photographing in a dark environment to take a bright photo. This allows the photographing unit to automatically adjust the optimal photographing settings according to the camera performance of the smartphone.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the accuracy of the analysis based on the user's emotions. For example, if the user is nervous, the analysis unit increases the analysis accuracy to provide highly reliable results. Furthermore, if the user is relaxed, the analysis unit can set the analysis accuracy to normal. This allows the analysis unit to adjust the accuracy of the analysis according to the user's emotions.

[0082] The analysis unit can automatically detect stains and creases on the paper of the test results during analysis, thereby improving the accuracy of the analysis. The analysis unit automatically detects stains and creases on the paper of the test results using image analysis technology. For example, the analysis unit automatically detects stains on the paper of the test results, thereby improving the accuracy of the analysis. The analysis unit can also automatically detect creases on the paper of the test results, thereby improving the accuracy of the analysis. For example, the analysis unit detects creases on the paper and performs analysis by removing the effects of the creases. The analysis unit can also automatically detect tears on the paper of the test results, thereby improving the accuracy of the analysis. For example, the analysis unit detects tears on the paper and performs analysis by completing the torn parts. In this way, the analysis unit can automatically detect stains and creases on the paper of the test results, thereby improving the accuracy of the analysis.

[0083] The analysis unit can apply flexible analysis algorithms to accommodate test results in different formats during analysis. The analysis unit automatically recognizes test results in different formats and applies an analysis algorithm. For example, the analysis unit automatically recognizes test results in different formats and selects an appropriate analysis algorithm. The analysis unit can also adjust the analysis algorithm to accommodate test results in different languages. For example, the analysis unit analyzes test results in different languages ​​and extracts numerical values. The analysis unit can also apply analysis algorithms to accommodate test results in different layouts. For example, the analysis unit automatically recognizes test results in different layouts and extracts numerical values. This enables the analysis unit to flexibly analyze test results in different formats.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the analysis unit can adjust the display method of the analysis results based on the user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions.

[0085] The analysis unit may have a function for detecting abnormal values ​​by comparing the user's past test results during analysis. The analysis unit compares the user's past test results with the current results and automatically detects abnormal values. For example, the analysis unit compares the user's past test results with the current results and detects abnormal values. The analysis unit can also analyze trends in the user's past test results and detect abnormal values. For example, the analysis unit analyzes trends in past test results and detects abnormal values. Furthermore, the analysis unit can compare the user's past test results with the current results and display abnormal values ​​in a graph. For example, the analysis unit visually displays abnormal values ​​in a graph and presents them to the user in an easy-to-understand manner. In this way, the analysis unit can detect abnormal values ​​by comparing them with past test results, thereby enabling early detection of abnormalities.

[0086] The analysis unit can customize the analysis results based on the user's age and gender during analysis. The analysis unit sets appropriate reference values ​​and customizes the analysis results based on the user's age. For example, the analysis unit sets a normal value range based on the user's age and provides the analysis results. The analysis unit can also set appropriate reference values ​​and customize the analysis results based on the user's gender. For example, the analysis unit sets a normal value range based on the user's gender and provides the analysis results. Furthermore, the analysis unit can customize the analysis results and provide individual advice based on the user's age and gender. For example, the analysis unit suggests appropriate ways to improve lifestyle habits based on the user's age and gender. This allows the analysis unit to provide analysis results that suit the user's age and gender.

[0087] The explanation unit can estimate the user's emotions and adjust the way the explanation is expressed based on the estimated user's emotions. The explanation unit estimates the user's emotions using facial expression recognition technology or voice analysis technology. For example, the explanation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The explanation unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the explanation unit can adjust the way the explanation is expressed based on the user's emotions. For example, if the user is nervous, the explanation unit uses concise and easy-to-understand expressions. Also, if the user is relaxed, the explanation unit can provide detailed explanations. This allows the explanation unit to adjust the way the explanation is expressed according to the user's emotions.

[0088] The explanation unit may have a function to provide background information and related health information for each test item during explanation. The explanation unit provides the background information and related health information for each test item. For example, the explanation unit may explain that AST is an indicator of liver health and suggest lifestyle improvements to maintain liver health. The explanation unit may also provide the latest medical papers and research data related to each test item. For example, the explanation unit may provide the latest medical papers related to AST, allowing the user to gain a deeper understanding of the test results. The explanation unit may also integrate the background information and related health information for each test item and provide them. For example, the explanation unit may integrate the background information for AST and related health information to provide an easy-to-understand explanation for the user. In this way, the explanation unit provides the background information and related health information for each test item, allowing the user to gain a deeper understanding of the test results.

[0089] The explanation unit may have a function of adjusting the level of detail of the explanation according to the user's level of understanding during explanation. The explanation unit adjusts the level of detail of the explanation according to the user's level of understanding. For example, if the user's level of understanding is low, the explanation unit provides a concise and easy-to-understand explanation. Furthermore, if the user's level of understanding is high, the explanation unit can also provide a detailed explanation. For example, the explanation unit provides detailed information and background information about the test items according to the user's level of understanding. Furthermore, the explanation unit can adjust the level of detail of the explanation in real time according to the user's level of understanding. For example, the explanation unit evaluates the user's level of understanding and adjusts the level of detail of the explanation based on the evaluation result. In this way, the explanation unit can provide an explanation that is easy for the user to understand by adjusting the level of detail of the explanation according to the user's level of understanding.

[0090] The explanation unit can estimate the user's emotions and adjust the order of explanations based on the estimated user's emotions. The explanation unit estimates the user's emotions using facial expression recognition technology or voice analysis technology. For example, the explanation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The explanation unit can also analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the explanation unit can adjust the order of explanations based on the user's emotions. For example, if the user is nervous, the explanation unit can provide important information first. Also, if the user is relaxed, the explanation unit can provide explanations in a sequential order. In this way, the explanation unit can adjust the order of explanations according to the user's emotions, thereby providing explanations in an order that is easy for the user to understand.

[0091] The explanation unit can provide a customized explanation based on the user's past health condition and lifestyle habits when providing an explanation. The explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. For example, the explanation unit provides an appropriate explanation based on the user's past health condition. The explanation unit can also provide an appropriate explanation based on the user's lifestyle habits. For example, the explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. Furthermore, the explanation unit can provide individual advice by referring to the user's past test results and lifestyle habit data. For example, the explanation unit suggests ways to improve lifestyle habits based on the user's past test results. In this way, the explanation unit can provide more appropriate information to the user by providing an explanation customized based on the user's past health condition and lifestyle habits.

[0092] The explanation unit can provide multilingual explanations according to the user's language setting when providing explanations. The explanation unit provides multilingual explanations according to the user's language setting. For example, the explanation unit provides explanations in an appropriate language based on the user's language setting. The explanation unit can also provide a language switching function when the user uses multiple languages. For example, the explanation unit provides multilingual explanations based on the user's language setting. Furthermore, the explanation unit has a function for learning technical terms of a specific language to improve translation accuracy. For example, the explanation unit references a database of technical terms to improve translation accuracy of medical terms. As a result, the explanation unit provides multilingual explanations according to the user's language setting, allowing the user to receive explanations in a language that is easy for the user to understand.

[0093] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using facial expression recognition technology or voice analysis technology. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the emotion by analyzing the tone and speed of the user's voice. Furthermore, the suggestion unit can adjust the way in which suggestions are expressed based on the user's emotion. For example, if the user is nervous, the suggestion unit uses concise and easy-to-understand expressions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way in which suggestions are expressed based on the user's emotion.

[0094] The suggestion unit can suggest an action plan by referring to the user's past medical history when making a suggestion. The suggestion unit suggests an optimal action plan by referring to the user's past medical history. For example, the suggestion unit suggests an appropriate action plan based on the user's past medical history. The suggestion unit can also analyze the user's past medical history and suggest an optimal action plan. For example, the suggestion unit provides individual advice based on the user's past medical history. Furthermore, the suggestion unit can suggest specific hospitals and test contents by referring to the user's past medical history. For example, the suggestion unit suggests a hospital with a specialist based on the user's past medical history. In this way, the suggestion unit can take more appropriate action for the user by suggesting an optimal action plan based on the user's past medical history.

[0095] When making a proposal, the suggestion unit can suggest specific improvement measures based on the user's lifestyle habits and dietary content. The suggestion unit suggests specific improvement measures based on the user's lifestyle habits and dietary content. For example, the suggestion unit suggests an appropriate exercise plan based on the user's lifestyle habits. The suggestion unit can also suggest a nutritionally balanced meal plan based on the user's dietary content. For example, the suggestion unit analyzes the user's dietary content and suggests specific meal improvement measures. Furthermore, the suggestion unit can comprehensively analyze the user's lifestyle habits and dietary content and suggest specific improvement measures. For example, the suggestion unit suggests a specific action plan for maintaining and improving health based on the user's lifestyle habits and dietary content. As a result, the suggestion unit suggests specific improvement measures based on the user's lifestyle habits and dietary content, allowing the user to take specific actions for maintaining and improving their health.

[0096] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. The suggestion unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The suggestion unit can also analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the suggestion unit can determine the priority of suggestions based on the user's emotions. For example, if the user is nervous, the suggestion unit can provide important suggestions with the highest priority. Also, if the user is relaxed, the suggestion unit can make suggestions in an orderly manner. In this way, the suggestion unit can determine the priority of suggestions according to the user's emotions and provide the most important suggestions to the user with priority.

[0097] The suggestion unit can suggest hospitals and clinics taking into account the user's geographical location information when making a suggestion. The suggestion unit suggests the most suitable hospital or clinic taking into account the user's geographical location information. For example, the suggestion unit suggests the nearest hospital based on the user's current location. The suggestion unit can also suggest an easily accessible hospital taking into account the user's geographical location information. For example, the suggestion unit suggests the most suitable hospital taking into account the user's geographical location information and means of transportation. Furthermore, the suggestion unit can reflect the hospital's congestion status and reservation status in real time based on the user's geographical location information. For example, the suggestion unit acquires the congestion status of hospitals within the user's living area in real time and reflects it in the suggestion. In this way, the suggestion unit can suggest the most suitable hospital or clinic taking into account the user's geographical location information, allowing the user to select a medical institution that is easily accessible.

[0098] The suggestion unit can suggest an action plan by referring to the user's insurance information when making the suggestion. The suggestion unit suggests a cost-effective action plan by referring to the user's insurance information. For example, the suggestion unit suggests a cost-effective treatment based on the user's insurance information. The suggestion unit can also suggest an optimal hospital by taking into account the user's insurance coverage. For example, the suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. Furthermore, the suggestion unit can also provide individual advice by referring to the user's insurance information. For example, the suggestion unit suggests an action plan for receiving economically optimal medical services based on the user's insurance information. As a result, the suggestion unit suggests a cost-effective action plan by referring to the user's insurance information, allowing the user to receive economically optimal medical services.

[0099] The hospital suggestion unit can estimate the user's emotions and adjust hospital selection criteria based on the estimated user emotions. The hospital suggestion unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the hospital suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The hospital suggestion unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the hospital suggestion unit can adjust the hospital selection criteria based on the user's emotions. For example, if the user is nervous, the hospital suggestion unit can prioritize and suggest reliable hospitals. Also, if the user is relaxed, the hospital suggestion unit can suggest hospitals with easy access. In this way, the hospital suggestion unit can select the optimal hospital for the user by adjusting the hospital selection criteria according to the user's emotions.

[0100] When suggesting a hospital, the hospital suggestion unit can suggest the most suitable hospital by referring to the user's past medical history. The hospital suggestion unit suggests the most suitable hospital by referring to the user's past medical history. For example, the hospital suggestion unit suggests a hospital with an appropriate specialist based on the user's past medical history. The hospital suggestion unit can also analyze the user's past medical history and suggest the most suitable hospital. For example, the hospital suggestion unit provides individual advice based on the user's past medical history. Furthermore, the hospital suggestion unit can suggest specific hospitals and examination contents by referring to the user's past medical history. For example, the hospital suggestion unit suggests a hospital with a specialist based on the user's past medical history. In this way, the hospital suggestion unit can select the most suitable medical institution for the user by suggesting the most suitable hospital based on the user's past medical history.

[0101] The hospital suggestion unit may have a function of reflecting the congestion status of hospitals within the user's living area in real time when suggesting a hospital. The hospital suggestion unit acquires the congestion status of hospitals within the user's living area in real time and reflects it in the suggestion. For example, the hospital suggestion unit acquires the congestion status of hospitals within the user's living area in real time and suggests hospitals with short waiting times. The hospital suggestion unit can also acquire the reservation status of hospitals within the user's living area in real time and reflect it in the suggestion. For example, the hospital suggestion unit acquires the reservation status of hospitals within the user's living area in real time and suggests hospitals where it is easy to make an appointment. Furthermore, the hospital suggestion unit can also acquire the waiting times of hospitals within the user's living area in real time and reflect it in the suggestion. For example, the hospital suggestion unit acquires the waiting times of hospitals within the user's living area in real time and suggests hospitals with short waiting times. In this way, the hospital suggestion unit reflects the congestion status of hospitals within the user's living area in real time, allowing the user to select a hospital with a short waiting time.

[0102] The hospital suggestion unit can estimate the user's emotions and determine the priority of hospital suggestions based on the estimated user's emotions. The hospital suggestion unit estimates the user's emotions using facial expression recognition technology and voice analysis technology. For example, the hospital suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The hospital suggestion unit can also analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the hospital suggestion unit can determine the priority of hospital suggestions based on the user's emotions. For example, if the user is nervous, the hospital suggestion unit can give top priority to suggesting reliable hospitals. Also, if the user is relaxed, the hospital suggestion unit can also suggest hospitals with easy access. In this way, the hospital suggestion unit can prioritize hospital suggestions based on the user's emotions, thereby preferentially suggesting hospitals that are most important to the user.

[0103] The hospital suggestion unit can suggest the most suitable hospital taking into consideration the user's insurance coverage when suggesting a hospital. The hospital suggestion unit suggests the most suitable hospital taking into consideration the user's insurance coverage. For example, the hospital suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. The hospital suggestion unit can also suggest the most suitable hospital taking into consideration the user's insurance coverage. For example, the hospital suggestion unit provides individual advice based on the user's insurance coverage. Furthermore, the hospital suggestion unit can suggest specific hospitals and examination contents by referring to the user's insurance coverage. For example, the hospital suggestion unit suggests a cost-effective hospital based on the user's insurance coverage. In this way, the hospital suggestion unit suggests the most suitable hospital taking into consideration the user's insurance coverage, allowing the user to receive the most economically optimal medical service.

[0104] When suggesting a hospital, the hospital suggestion unit can suggest the most suitable hospital by taking into consideration the user's means of transportation and travel time. The hospital suggestion unit suggests the most suitable hospital by taking into consideration the user's means of transportation and travel time. For example, the hospital suggestion unit suggests an easily accessible hospital based on the user's means of transportation. The hospital suggestion unit can also suggest the most suitable hospital by taking into consideration the user's travel time. For example, the hospital suggestion unit suggests the most suitable hospital by comprehensively taking into consideration the user's means of transportation and travel time. Furthermore, the hospital suggestion unit can reflect the hospital's congestion status and reservation status in real time based on the user's means of transportation and travel time. For example, the hospital suggestion unit suggests a hospital with a short waiting time by taking into consideration the user's means of transportation and travel time. In this way, the hospital suggestion unit can suggest the most suitable hospital by taking into consideration the user's means of transportation and travel time, allowing the user to select a medical institution that is easily accessible.

[0105] The detailed explanation unit can estimate the user's emotions and adjust the manner in which the detailed explanation is expressed based on the estimated user's emotions. The detailed explanation unit estimates the user's emotions using facial expression recognition technology or voice analysis technology. For example, the detailed explanation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The detailed explanation unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the detailed explanation unit can adjust the manner in which the detailed explanation is expressed based on the user's emotions. For example, if the user is nervous, the detailed explanation unit uses concise and easy-to-understand expressions. Furthermore, the detailed explanation unit can provide a detailed explanation if the user is relaxed. In this way, the detailed explanation unit can provide an explanation that is easy for the user to understand by adjusting the manner in which the detailed explanation is expressed based on the user's emotions.

[0106] The detailed explanation unit may have a function to provide medical papers and research data related to each test item during the detailed explanation. The detailed explanation unit provides medical papers and research data related to each test item. For example, the detailed explanation unit provides the latest medical papers related to each test item. The detailed explanation unit can also provide research data related to each test item. For example, the detailed explanation unit provides an integrated version of medical papers and research data related to each test item. Furthermore, the detailed explanation unit can also provide an integrated version of background information and related health information for each test item. For example, the detailed explanation unit integrates background information and related health information for each test item to provide an easy-to-understand explanation to the user. In this way, the detailed explanation unit provides medical papers and research data related to each test item, allowing the user to gain a deeper understanding of the test results.

[0107] The detailed explanation unit may have a function of adjusting the level of detail of the explanation according to the user's level of understanding when providing the detailed explanation. The detailed explanation unit adjusts the level of detail of the explanation according to the user's level of understanding. For example, the detailed explanation unit provides a concise and easy-to-understand explanation when the user's level of understanding is low. The detailed explanation unit can also provide a detailed explanation when the user's level of understanding is high. For example, the detailed explanation unit provides detailed information and background information about the test items according to the user's level of understanding. Furthermore, the detailed explanation unit can adjust the level of detail of the explanation in real time according to the user's level of understanding. For example, the detailed explanation unit evaluates the user's level of understanding and adjusts the level of detail of the explanation based on the evaluation result. In this way, the detailed explanation unit can provide an explanation that is easy for the user to understand by adjusting the level of detail of the explanation according to the user's level of understanding.

[0108] The detailed explanation unit can estimate the user's emotions and adjust the order of the detailed explanation based on the estimated user's emotions. The detailed explanation unit estimates the user's emotions using facial expression recognition technology or voice analysis technology. For example, the detailed explanation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The detailed explanation unit can also estimate the emotions by analyzing the tone and speed of the user's voice. Furthermore, the detailed explanation unit can adjust the order of the detailed explanation based on the user's emotions. For example, if the user is nervous, the detailed explanation unit can provide important information first. Also, if the user is relaxed, the detailed explanation unit can provide an explanation in an orderly manner. In this way, the detailed explanation unit can adjust the order of the detailed explanation according to the user's emotions, thereby providing an explanation in an order that is easy for the user to understand.

[0109] The detailed explanation unit can provide a customized explanation based on the user's past health condition and lifestyle habits when providing the detailed explanation. The detailed explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. For example, the detailed explanation unit provides an appropriate explanation based on the user's past health condition. The detailed explanation unit can also provide an appropriate explanation based on the user's lifestyle habits. For example, the detailed explanation unit provides a customized explanation based on the user's past health condition and lifestyle habits. Furthermore, the detailed explanation unit can provide individual advice by referring to the user's past test results and lifestyle habit data. For example, the detailed explanation unit suggests ways to improve lifestyle habits based on the user's past test results. In this way, the detailed explanation unit can provide more appropriate information to the user by providing an explanation customized based on the user's past health condition and lifestyle habits.

[0110] The detailed explanation unit can provide a detailed explanation in multiple languages ​​according to the user's language setting when providing the detailed explanation. The detailed explanation unit provides a detailed explanation in multiple languages ​​according to the user's language setting. For example, the detailed explanation unit provides a detailed explanation in an appropriate language based on the user's language setting. The detailed explanation unit can also provide a language switching function when the user uses multiple languages. For example, the detailed explanation unit provides a detailed explanation in multiple languages ​​based on the user's language setting. Furthermore, the detailed explanation unit has a function for learning technical terms of a specific language to improve translation accuracy. For example, the detailed explanation unit references a database of technical terms to improve translation accuracy of medical terms. As a result, the detailed explanation unit provides a detailed explanation in multiple languages ​​according to the user's language setting, allowing the user to receive an explanation in a language that is easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, explanation unit, suggestion unit, and hospital suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14 and photographs the results of the health checkup. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts numerical values ​​from the photograph. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains the meaning of the extracted numerical values. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the next action to be taken. The hospital suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable hospital and examination contents for the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, explanation unit, suggestion unit, and hospital suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214 and photographs the results of the health checkup. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts numerical values ​​from the photographed image. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains the meaning of the extracted numerical values. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the next action to be taken. The hospital suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable hospital and examination contents for the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, explanation unit, suggestion unit, and hospital suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset-type terminal 314 and photographs the results of the health checkup. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts numerical values ​​from the photographed image. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains the meaning of the extracted numerical values. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the next action to be taken. The hospital suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable hospital and examination contents for the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, explanation unit, suggestion unit, and hospital suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414 and photographs the results of the health check. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts numerical values ​​from the photographed image. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains the meaning of the extracted numerical values. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the next action to be taken. The hospital suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable hospital and examination contents for the user.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can compare the user's past health checkup results with the current results to detect trends in abnormal values. For example, the analysis unit can store past health checkup results in a database and compare them with the current results to detect fluctuations in abnormal values. The analysis unit can also display trends in abnormal values ​​in a graph, providing the user with a visually easy-to-understand format. Furthermore, the analysis unit can predict future health risks and suggest preventive measures based on trends in abnormal values. This allows the user to understand changes in their health condition and take measures early.

[0113] The suggestion unit can estimate the user's emotions and customize the suggestion content based on the estimated emotions. For example, if the user feels anxious, the suggestion unit can make suggestions using expressions that give a sense of security. Alternatively, if the user feels relaxed, the suggestion unit can provide detailed information to enable the user to understand more deeply. Furthermore, the suggestion unit can adjust the priority of suggestions according to the user's emotions and make the most important suggestions first. This allows the user to receive appropriate suggestions according to their emotions.

[0114] When photographing a user's health checkup results, the photographing unit can automatically recognize the type and format of the paper and select the optimal photographing mode. For example, the photographing unit can automatically recognize the type of paper on which the test results are printed and set the optimal resolution and photographing angle. The photographing unit can also analyze the format of the test results and automatically adjust the appropriate photographing range. Furthermore, the photographing unit can select the photographing mode according to the size of the paper on which the test results are printed so that the entire image is captured clearly. This allows the user to accurately photograph the test results and the analysis unit to extract accurate numerical values.

[0115] The explanation unit can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the user is nervous, the explanation unit can use simple and easy-to-understand expressions to make it easier for the user to understand. Alternatively, if the user is relaxed, the explanation unit can provide detailed information to enable the user to understand more deeply. Furthermore, the explanation unit can adjust the order of the explanation according to the user's emotions and provide important information first. This allows the user to receive an appropriate explanation according to their emotions.

[0116] The analysis unit can apply flexible analysis algorithms to accommodate health checkup results in different formats. For example, the analysis unit can automatically recognize test results in different layouts and extract numerical values. The analysis unit can also adjust the analysis algorithm to accommodate test results in different languages. Furthermore, the analysis unit can integrate test results in different formats to provide users with consistent analysis results. This allows users to receive accurate analysis of test results in any format.

[0117] The suggestion unit can suggest specific improvement measures based on the user's lifestyle habits and dietary content. For example, the suggestion unit can analyze the user's dietary content and suggest a nutritionally balanced meal plan. The suggestion unit can also consider the user's exercise habits and suggest an appropriate exercise plan. Furthermore, the suggestion unit can comprehensively analyze the user's entire lifestyle habits and suggest a specific action plan for maintaining and improving health. This allows the user to receive specific improvement measures based on their own lifestyle habits.

[0118] The photographing unit can estimate the user's emotions and adjust the timing of photographing based on the estimated emotions. For example, if the user is nervous, the photographing unit waits until the user relaxes before starting photographing. Also, if the user is in a hurry, the photographing unit can start photographing immediately. Furthermore, the photographing unit can adjust the photographing mode according to the user's emotions and take the most suitable photo. This allows the user to take photographs at the most suitable timing according to their emotions.

[0119] The analysis unit can automatically detect stains and creases on the paper of the test results during analysis, thereby improving the accuracy of the analysis. For example, the analysis unit can automatically detect stains on the paper of the test results, thereby improving the accuracy of the analysis. The analysis unit can also automatically detect creases on the paper of the test results and perform analysis after removing the effects of the creases. Furthermore, the analysis unit can automatically detect tears on the paper of the test results and perform analysis after completing the torn parts. This allows the user to receive an accurate analysis regardless of the condition of the paper of the test results.

[0120] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide important suggestions as a top priority. Also, if the user is relaxed, the suggestion unit can provide suggestions in an orderly manner. Furthermore, the suggestion unit can adjust the way suggestions are expressed according to the user's emotions and provide suggestions in a form that is easy for the user to understand. This allows the user to receive the most appropriate suggestions according to their emotions.

[0121] The explanation unit may have a function to provide background information and related health information for each test item during the explanation. For example, the explanation unit may explain that AST is an indicator of liver health and suggest lifestyle improvements to maintain liver health. The explanation unit may also provide the latest medical papers and research data related to each test item. Furthermore, the explanation unit may provide an integrated version of the background information and related health information for each test item, allowing the user to gain a deeper understanding of the test results. This makes it easier for the user to understand the background information and related health information for each test item.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The photographing unit takes a photograph of the health checkup results. For example, a user can take a photograph of the health checkup results using a smartphone camera. The photographing unit can obtain clear images using a high-resolution camera and can have a function to automatically adjust the lighting conditions and shooting angle when taking a photograph. Step 2: The analysis unit analyzes the photos taken by the photography unit and extracts numerical values. For example, it uses generative AI to analyze the photos and extract the numerical values ​​for each test item. It can perform image analysis using deep learning models and OCR technology to accurately extract numerical values. Step 3: The explanation unit analyzes the values ​​extracted by the analysis unit and provides an explanation for each test item. For example, it uses generation AI to analyze the extracted values ​​and explain what each test item represents. It uses natural language processing technology to analyze the meaning of the values ​​and provide an explanation that is easy for the user to understand. Step 4: The proposal section performs a comprehensive analysis of the values ​​explained by the explanation section and proposes the optimal action plan. For example, generative AI can be used to comprehensively analyze the magnitude of the values ​​and propose what to do next. It can detect abnormal values ​​and provide specific information on which hospitals need to examine what.

[0124] 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.

[0125] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photography department takes photos of the health checkup results, an analysis unit that analyzes the photograph taken by the photographing unit and extracts numerical values; an explanation unit that analyzes the values ​​extracted by the analysis unit and provides an explanation for each test item; a proposal unit that proposes an action plan by comprehensively analyzing the numerical values ​​explained by the explanation unit; Equipped with A system characterized by:

2. Equipped with a hospital suggestion section that displays hospital names and test details 2. The system of claim 1.

3. Equipped with a detailed explanation section that provides explanations of each test item 2. The system of claim 1.

4. The analysis unit Analyzing photos using generative AI and extracting values 2. The system of claim 1.

5. The explanation section Analyze the extracted values ​​using generative AI and provide an explanation for each test item 2. The system of claim 1.

6. The proposal unit Using generative AI to comprehensively analyze numerical values ​​and propose action plans 2. The system of claim 1.

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

2. The system of claim 1.

8. The imaging unit is It has a function that detects and automatically corrects camera shake caused by the user's hand when taking a photo.

2. The system of claim 1.

9. The imaging unit is When taking a photo, the type and format of the paper containing the test results is automatically recognized and the photo mode is selected.

2. The system of claim 1.

10. The imaging unit is Estimate the user's emotions and determine the priority of taking photos based on the estimated user emotions.

2. The system of claim 1.

Images