System

Abstract

An object of the system according to the embodiment is to find a medical condition that may be overlooked by a doctor at the time of a medical checkup and to provide a user with a health risk and a method for solving the health risk.SOLUTION: A system includes a test value collection part, an image collection part, a correlation analysis part, an abnormality specification part, a risk provision part, and a solution provision part. The inspection value collection part collects each inspection value. The image collection unit collects image data. The correlation analysis part analyzes the data collected by the inspection value collection part and the image collection part. The abnormality specifying unit specifies a location of abnormality based on the data analyzed by the correlation analysis unit. The risk providing unit provides a health risk to the user based on the abnormality specified by the abnormality specifying unit. The solution providing part provides a solution based on the risk provided by the risk providing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have difficulty detecting conditions that doctors may overlook during medical checkups, and there is room for improvement.

[0005] The system according to the embodiment aims to detect medical conditions that may be overlooked by doctors during health checkups and to provide users with health risks and solutions to those risks. [Means for solving the problem]

[0006] The system according to the embodiment includes a test value collection unit, an image collection unit, a correlation analysis unit, an abnormality identification unit, a risk provision unit, and a solution provision unit. The test value collection unit collects test values. The image collection unit collects image data. The correlation analysis unit analyzes data collected by the test value collection unit and the image collection unit. The abnormality identification unit identifies the location of an abnormality based on the data analyzed by the correlation analysis unit. The risk provision unit provides health risks to the user based on the abnormalities identified by the abnormality identification unit. The solution provision unit provides solutions based on the risks provided by the risk provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect medical conditions that doctors may overlook during medical checkups and provide users with health risks and solutions to those risks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses multimodal AI to analyze the correlation between test values ​​and images during health checkups to identify conditions that may be overlooked by ordinary physicians. This system inputs test values ​​and image data into the multimodal AI, which then analyzes the correlation between the test values ​​and the images to identify potential abnormalities. Based on the analysis results, the AI ​​then provides the user with individual health risks and solutions as a second opinion. For example, the system inputs various test values, such as blood and urine tests, and medical images, such as X-rays and MRI images. The multimodal AI then analyzes the correlation between the input test values ​​and the image data to identify potential abnormalities. Based on the analysis results, the AI ​​then provides the user with individual health risks and solutions. This allows the system to identify conditions that may be overlooked by ordinary physicians. Furthermore, by providing users with individual health risks and solutions, the system can serve as a second opinion. This allows the system to analyze the correlation between test values ​​and images during health checkups to identify conditions that may be overlooked by ordinary physicians. It can also act as a second opinion by providing users with individual health risks and solutions. For example, if AI identifies an abnormality, the user can seek the opinion of another doctor. This will lead to more accurate diagnoses and appropriate treatment.

[0029] A health checkup support system according to an embodiment includes a test value collection unit, an image collection unit, a correlation analysis unit, an abnormality identification unit, a risk reporting unit, and a solution reporting unit. The test value collection unit collects test values. For example, the test value collection unit collects various test values, such as blood test values ​​and urine test values. The test value collection unit can also collect test values ​​such as electrocardiogram data and hormone levels. The test value collection unit can also estimate a user's emotions and adjust the timing of collecting test values ​​based on the estimated user's emotions. For example, if a user is feeling stressed, the test values ​​are collected during a time when the user is able to relax. The image collection unit collects image data. For example, the image collection unit collects medical images, such as X-ray images and MRI images. The image collection unit can also collect medical images, such as ultrasound images and CT scan images. The image collection unit can also estimate a user's emotions and adjust the timing of collecting image data based on the estimated user's emotions. For example, if a user is relaxed, the image collection unit collects image data at a normal time. The correlation analysis unit analyzes the data collected by the test value collection unit and the image collection unit. For example, the correlation analysis unit analyzes the association between each test value and image data. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the test values ​​and the image data. The correlation analysis unit can also estimate the user's emotions and adjust the criteria for the correlation analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the correlation analysis can be performed using simple criteria. The anomaly identification unit identifies the location of an abnormality based on the data analyzed by the correlation analysis unit. For example, the anomaly identification unit can improve the accuracy of the identification by taking into account the interrelationships between the data analyzed by the correlation analysis unit. The anomaly identification unit can also perform the identification by taking into account attribute information of the submitter of the test value and the image data. The anomaly identification unit can also estimate the user's emotions and adjust the criteria for the anomaly identification based on the estimated user's emotions. For example, if the user is relaxed, the anomaly identification can be performed using detailed criteria. The risk providing unit provides the user with health risks based on the abnormalities identified by the anomaly identification unit. For example, the risk providing unit improves the accuracy of the risk by taking into account the interrelationships between the anomalies identified by the anomaly identification unit.The risk providing unit can also provide risks taking into account attribute information of the person who submitted the abnormality identified by the abnormality identifying unit. Furthermore, the risk providing unit can estimate the user's emotions and adjust the method of providing risks based on the estimated user emotions. For example, if the user is in a hurry, the risk can be provided quickly. The solution providing unit provides solutions based on the risks provided by the risk providing unit. For example, the solution providing unit can improve the accuracy of the solutions by taking into account the interrelationships between the risks provided by the risk providing unit. Furthermore, the solution providing unit can estimate the user's emotions and adjust the method of providing solutions based on the estimated user emotions. For example, if the user is relaxed, the solution can be provided in a detailed manner. This allows the health checkup support system according to the embodiment to analyze the correlation between each test value and image data, identify possible abnormalities, and provide the user with health risks and solutions.

[0030] The test value collection unit can collect various test values ​​from blood tests or urine tests. The various test values ​​include, but are not limited to, blood test values, urine test values, and hormone levels. The test value collection unit, for example, collects blood test results. The test value collection unit can also collect urine test results. The test value collection unit can also collect hormone level results. By collecting various test values, it is possible to understand the health condition in more detail. Some or all of the above-mentioned processing in the test value collection unit may be performed, for example, using AI or without using AI. For example, the test value collection unit can input blood test results into the generation AI and have the generation AI analyze the test values.

[0031] The image collection unit can collect medical images such as X-ray images or MRI images. Medical images include, but are not limited to, X-ray images, MRI images, and ultrasound images. The image collection unit, for example, collects X-ray images. The image collection unit can also collect MRI images. The image collection unit can also collect ultrasound images. This allows for the detection of visual abnormalities by collecting medical images. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input X-ray images to the generation AI and have the generation AI analyze the image data.

[0032] The correlation analysis unit can analyze the correlation between each test value and the image data. The analysis of the correlation includes, but is not limited to, for example, a method for calculating a correlation coefficient and an analysis algorithm to be used. For example, the correlation analysis unit calculates a correlation coefficient to analyze the correlation between each test value and the image data. The correlation analysis unit can also analyze the correlation based on the analysis algorithm to be used. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the interrelationship between the test value and the image data. This improves the accuracy of identifying possible abnormalities by analyzing the correlation between each test value and the image data. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input the test value and image data into a generation AI and have the generation AI perform a correlation analysis.

[0033] The anomaly identification unit can identify a location where an anomaly may exist based on the data analyzed by the correlation analysis unit. Identification of a location where an anomaly may exist includes, but is not limited to, an anomaly threshold, an image analysis technique, and the like. The anomaly identification unit can identify a location where an anomaly may exist based on, for example, an anomaly threshold. The anomaly identification unit can also identify a location where an anomaly may exist using an image analysis technique. The anomaly identification unit can also improve the accuracy of identification by taking into account the interrelationships between the data analyzed by the correlation analysis unit. This enables early detection by identifying a location where an anomaly may exist. Some or all of the above-described processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input the data analyzed by the correlation analysis unit into a generation AI and have the generation AI identify an anomaly.

[0034] The risk providing unit can provide a health risk to the user based on the abnormality identified by the anomaly identification unit. The provision of health risks includes, for example, the type of risk and criteria for risk assessment, but is not limited to these examples. For example, the risk providing unit evaluates the risk of the abnormality identified by the anomaly identification unit and provides the risk to the user. The risk providing unit can also provide an appropriate risk depending on the type of risk. The risk providing unit can also improve the accuracy of the risk by taking into account the interrelationships between the abnormalities identified by the anomaly identification unit. This allows the user to take appropriate measures by providing the health risk to the user. Some or all of the above-described processing by the risk providing unit may be performed using, or without, AI. For example, the risk providing unit can input data on the abnormality identified by the anomaly identification unit into a generation AI and cause the generation AI to evaluate the risk.

[0035] The solution providing unit can provide a solution based on the risk provided by the risk providing unit. The provision of solutions includes, but is not limited to, treatments, preventive measures, and lifestyle improvement methods. For example, the solution providing unit can provide an appropriate treatment based on the risk provided by the risk providing unit. The solution providing unit can also provide preventive measures. The solution providing unit can also provide lifestyle improvement methods. By providing solutions, the user can take appropriate measures. Some or all of the above-described processing in the solution providing unit can be performed using, or without, AI. For example, the solution providing unit can input risk data provided by the risk providing unit into a generation AI and have the generation AI execute a proposed solution.

[0036] The test value collection unit can analyze the user's past test history and select an appropriate collection method. Analysis of the past test history includes, but is not limited to, the type of history data and the analysis algorithm. For example, the test value collection unit prioritizes the selection of a test method that the user has frequently used in the past. The test value collection unit can also select the most efficient collection method based on the user's past test history. The test value collection unit can also suggest the optimal collection method based on the user's past test results. This allows the optimal collection method to be selected by analyzing the past test history. Some or all of the above-described processing in the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's past test history data into a generation AI and have the generation AI select a collection method.

[0037] When collecting test values, the test value collection unit can filter the test values ​​based on the user's current health condition and lifestyle habits. Filtering of the current health condition and lifestyle habits includes, but is not limited to, health condition evaluation criteria and lifestyle habit data collection methods. For example, the test value collection unit collects only necessary test values ​​based on the user's current health condition. The test value collection unit can also prioritize collection of highly relevant test values ​​based on the user's lifestyle habits. The test value collection unit can also collect optimal test values ​​by comprehensively considering the user's health condition and lifestyle habits. This allows collection of only necessary test values ​​taking into account the user's health condition and lifestyle habits. Some or all of the above-described processing by the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's health condition data and lifestyle habit data into a generation AI and have the generation AI perform filtering.

[0038] When collecting test values, the test value collection unit can select the optimal collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the test value collection unit collects test values ​​via voice. Furthermore, if the user desires text input, the test value collection unit can also collect test values ​​via text. Furthermore, if the user desires image input, the test value collection unit can also collect test values ​​via image. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the test value collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the test value collection unit may input the user's input data into a generation AI and have the generation AI select a collection means.

[0039] When collecting test values, the test value collection unit can prioritize collecting test values ​​that are highly relevant by taking into account the user's geographical location information. Consideration of geographical location information includes, but is not limited to, the method for collecting location information and the criteria for evaluating relevance. For example, if the user is in a specific area, the test value collection unit prioritizes collecting test values ​​related to that area. The test value collection unit can also collect necessary test values ​​based on the user's geographical location information. The test value collection unit can also collect optimal test values ​​by taking into account the user's location information. This allows necessary test values ​​to be collected based on the user's geographical location information. Some or all of the above-described processing by the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's location information data into the generation AI and have the generation AI select the test values ​​to collect.

[0040] The test value collection unit can analyze the user's social media activity when collecting test values ​​and collect related test values. Analysis of social media activity includes, but is not limited to, activity data collection methods and analysis algorithms. For example, the test value collection unit identifies necessary test values ​​from the user's social media activity. The test value collection unit can also analyze the content of the user's posts and collect related test values. The test value collection unit can also collect optimal test values ​​by referring to the user's social media activity. This allows necessary test values ​​to be collected by referring to the user's social media activity. Some or all of the above-described processing by the test value collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the test value collection unit can input the user's social media data into a generation AI and have the generation AI select test values.

[0041] When collecting test values, the test value collection unit can customize the collection method by reflecting the user's past feedback. Reflecting past feedback includes, but is not limited to, the method for collecting feedback data and the criteria for customization. For example, the test value collection unit selects the optimal collection method based on the user's past feedback. The test value collection unit can also improve the collection method by reflecting the user's feedback. The test value collection unit can also customize the collection method by incorporating the user's opinions. This allows the optimal collection method to be selected based on the user's past feedback. Some or all of the above-described processing in the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0042] The image collection unit can analyze the user's past image data and select the optimal collection method. Analysis of past image data includes, but is not limited to, the type of image data and the analysis algorithm. For example, the image collection unit preferentially selects an image collection method that the user has frequently used in the past. The image collection unit can also select the most efficient collection method from the user's past image data. The image collection unit can also suggest the optimal collection method based on the user's past image data. In this way, the optimal collection method can be selected by analyzing the past image data. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's past image data into a generation AI and have the generation AI select a collection method.

[0043] When collecting image data, the image collection unit can perform filtering based on the user's current health condition and lifestyle habits. Filtering of the current health condition and lifestyle habits includes, but is not limited to, health condition evaluation criteria and lifestyle habit data collection methods. The image collection unit, for example, collects only necessary image data based on the user's current health condition. The image collection unit can also prioritize collection of highly relevant image data based on the user's lifestyle habits. The image collection unit can also collect optimal image data by comprehensively considering the user's health condition and lifestyle habits. This allows collection of only necessary image data by considering the user's health condition and lifestyle habits. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's health condition data and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0044] When collecting image data, the image collection unit can select the optimal collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the image collection unit collects image data by voice. Furthermore, if the user desires text input, the image collection unit can also collect image data by text. Furthermore, if the user desires image input, the image collection unit can also collect image data by image. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0045] When collecting image data, the image collection unit can prioritize collecting highly relevant image data by taking into account the user's geographical location information. Consideration of geographical location information includes, but is not limited to, for example, a location information collection method and relevance evaluation criteria. For example, if the user is in a specific area, the image collection unit prioritizes collecting image data related to that area. The image collection unit can also collect necessary image data based on the user's geographical location information. The image collection unit can also collect optimal image data by taking into account the user's location information. This allows necessary image data to be collected based on the user's geographical location information. Some or all of the above-described processing in the image collection unit may be performed using, or without, AI. For example, the image collection unit can input the user's location information data into a generation AI and have the generation AI select the image data to collect.

[0046] When collecting image data, the image collection unit can analyze the user's social media activity and collect related image data. Analysis of social media activity includes, but is not limited to, activity data collection methods and analysis algorithms. For example, the image collection unit can identify necessary image data from the user's social media activity. The image collection unit can also analyze the content of the user's posts and collect related image data. The image collection unit can also collect optimal image data by referring to the user's social media activity. This makes it possible to collect necessary image data by referring to the user's social media activity. Some or all of the above-described processing in the image collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the image collection unit can input the user's social media data into a generation AI and have the generation AI select image data.

[0047] When collecting image data, the image collection unit can customize the collection method by reflecting the user's past feedback. Reflecting the past feedback includes, for example, a feedback data collection method and customization criteria, but is not limited to these examples. The image collection unit selects, for example, an optimal collection method based on the user's past feedback. The image collection unit can also improve the collection method by reflecting the user's feedback. The image collection unit can also customize the collection method by incorporating the user's opinions. This allows the optimal collection method to be selected based on the user's past feedback. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0048] The correlation analysis unit can improve the accuracy of the analysis by taking into account the correlation between the test value and the image data during the correlation analysis. Consideration of the correlation includes, for example, the method of calculating the correlation coefficient and the analysis algorithm to be used, but is not limited to these examples. The correlation analysis unit can, for example, analyze the correlation between the test value and the image data in detail to improve the accuracy. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the association between the test value and the image data. The correlation analysis unit can also improve the accuracy of the analysis based on the correlation between the test value and the image data. In this way, the accuracy of the analysis is improved by taking into account the correlation between the test value and the image data. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the correlation analysis unit can input the test value and the image data into a generation AI and have the generation AI analyze the correlation.

[0049] During correlation analysis, the correlation analysis unit can perform the analysis while taking into account attribute information of the submitter of the test values ​​and image data. Consideration of the submitter's attribute information includes, but is not limited to, age, gender, and medical history. The correlation analysis unit performs the correlation analysis while taking into account, for example, the submitter's age and gender. The correlation analysis unit can also perform the correlation analysis while taking into account the submitter's health condition and lifestyle habits. The correlation analysis unit can also perform the correlation analysis based on the submitter's attribute information. By taking the submitter's attribute information into account, more accurate analysis can be performed. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input the submitter's attribute information into the generation AI and have the generation AI perform the analysis.

[0050] During correlation analysis, the correlation analysis unit can weight the analysis based on the frequency of submission of test values ​​and image data. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the correlation analysis unit performs correlation analysis by prioritizing data submitted frequently. The correlation analysis unit can also perform correlation analysis by complementing data submitted infrequently. The correlation analysis unit can also weight the analysis based on the submission frequency. By weighting the analysis based on the submission frequency, more reliable analysis can be performed. Some or all of the above-described processing in the correlation analysis unit may be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input submission frequency data into a generation AI and have the generation AI perform weighting.

[0051] The correlation analysis unit can perform correlation analysis while taking into account the geographical distribution of the test values ​​and image data. Consideration of the geographical distribution includes, but is not limited to, for example, the method of collecting location information and the criteria for evaluating the relevance. The correlation analysis unit can also analyze region-specific health risks based on the geographical distribution of the test values ​​and image data. The correlation analysis unit can also improve the accuracy of the analysis by taking the geographical distribution into account. The correlation analysis unit can also perform correlation analysis by comparing data for each region. This allows region-specific health risks to be analyzed by taking the geographical distribution into account. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input geographical distribution data into a generation AI and have the generation AI perform the analysis.

[0052] During correlation analysis, the correlation analysis unit can improve the accuracy of the analysis by referring to literature related to the test values ​​and image data. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the correlation analysis unit analyzes the correlation between the test values ​​and image data by referring to related literature. The correlation analysis unit can also improve the accuracy of the analysis based on literature data. The correlation analysis unit can also perform correlation analysis by incorporating the latest research results. By referring to related literature, the accuracy of the analysis is improved. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input related literature data into a generation AI and have the generation AI perform the analysis.

[0053] During correlation analysis, the correlation analysis unit can perform the analysis taking into account the market value of the test values ​​and image data. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the correlation analysis unit performs correlation analysis by placing emphasis on data with high market value. The correlation analysis unit can also weight the analysis taking market value into account. The correlation analysis unit can also perform correlation analysis reflecting fluctuations in market value. In this way, the analysis can be weighted by taking market value into account. Some or all of the above-mentioned processing in the correlation analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the correlation analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0054] When identifying an anomaly, the anomaly identification unit can improve the accuracy of the identification by taking into account the interrelationships of the data analyzed by the correlation analysis unit. Consideration of the interrelationships includes, for example, the method of calculating the correlation coefficient and the analysis algorithm used, but is not limited to these examples. The anomaly identification unit improves the accuracy of the anomaly identification based on, for example, the data from the correlation analysis unit. The anomaly identification unit can also identify an anomaly by taking into account the interrelationships of the data. The anomaly identification unit can also improve the accuracy of the anomaly identification by reflecting the results of the correlation analysis. Thus, by taking the interrelationships of the data into account, the accuracy of the anomaly identification is improved. Some or all of the above-described processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input data from the correlation analysis unit into a generation AI and cause the generation AI to perform an analysis to improve the identification accuracy.

[0055] When identifying an anomaly, the anomaly identification unit can take into consideration the attribute information of the person who submitted the test values ​​and image data. Consideration of the attribute information of the person who submitted the image data includes, but is not limited to, age, gender, and medical history. The anomaly identification unit can, for example, take into consideration the age and gender of the person who submitted the image data into identifying an anomaly. The anomaly identification unit can also, for example, take into consideration the health condition and lifestyle of the person who submitted the image data into identifying an anomaly. The anomaly identification unit can also, for example, take into consideration the attribute information of the person who submitted the image data into identifying an anomaly. By taking into consideration the attribute information of the person who submitted the image data, anomaly identification can be performed with higher accuracy. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, AI, or without AI. For example, the anomaly identification unit can input the attribute information of the person who submitted the image data into the generation AI and have the generation AI perform the identification.

[0056] When identifying an anomaly, the anomaly identification unit can assign specific weights based on the frequency of submission of test values ​​and image data. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the anomaly identification unit can assign specific weights based on the submission frequency to identify an anomaly. The anomaly identification unit can also assign specific weights based on the submission frequency. By assigning specific weights based on the submission frequency, anomaly identification can be performed with higher reliability. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, AI, or without AI. For example, the anomaly identification unit can input the submission frequency data to a generation AI and have the generation AI perform the weighting.

[0057] The anomaly identification unit can identify anomalies by taking into account the geographical distribution of test values ​​and image data. Examples of consideration of the geographical distribution include, but are not limited to, the method of collecting location information and the criteria for evaluating the relevance. The anomaly identification unit can identify region-specific anomalies based on the geographical distribution of test values ​​and image data. The anomaly identification unit can also improve the accuracy of anomaly identification by taking the geographical distribution into account. The anomaly identification unit can also identify anomalies by comparing data for each region. This allows region-specific anomalies to be identified by taking the geographical distribution into account. Some or all of the above-described processing in the anomaly identification unit can be performed using, or without, AI. For example, the anomaly identification unit can input geographical distribution data into a generation AI and have the generation AI perform the identification.

[0058] When identifying an anomaly, the anomaly identification unit can improve the accuracy of identification by referring to literature related to the test values ​​and image data. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the anomaly identification unit identifies an anomaly in the test values ​​and image data by referring to related literature. The anomaly identification unit can also improve the accuracy of identification based on literature data. The anomaly identification unit can also incorporate the latest research results to identify anomalies. In this way, referring to related literature improves the accuracy of identification. Some or all of the above-described processing in the anomaly identification unit may be performed using, or without, AI. For example, the anomaly identification unit can input related literature data into a generation AI and have the generation AI perform the identification.

[0059] When identifying an anomaly, the anomaly identification unit can take into account the market value of the test value and the image data. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the anomaly identification unit can identify an anomaly by prioritizing data with high market value. The anomaly identification unit can also perform specific weighting by taking market value into account. The anomaly identification unit can also perform anomaly identification by reflecting fluctuations in market value. In this way, specific weighting can be performed by taking market value into account. Some or all of the above-mentioned processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input market value data to a generation AI and have the generation AI perform the identification.

[0060] When providing the risk, the risk providing unit can improve the accuracy of the risk by taking into account the interrelationships of the anomalies identified by the anomaly identification unit. Consideration of the interrelationships includes, for example, but is not limited to, the method of calculating the correlation coefficient and the analysis algorithm to be used. The risk providing unit can improve the accuracy of the risk, for example, based on the data from the anomaly identification unit. The risk providing unit can also provide the risk by taking into account the interrelationships of the anomalies. The risk providing unit can also improve the accuracy of the risk by reflecting the results of the anomaly identification. In this way, the accuracy of the risk is improved by taking into account the interrelationships of the anomalies. Some or all of the above-described processing in the risk providing unit may be performed, for example, using AI or without AI. For example, the risk providing unit can input data from the anomaly identification unit into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the risk.

[0061] When providing a risk, the risk providing unit can provide a risk by taking into consideration attribute information of the submitter of the abnormality identified by the anomaly identification unit. Consideration of the submitter's attribute information includes, for example, age, gender, medical history, etc., but is not limited to these examples. The risk providing unit provides a risk by taking into consideration, for example, the submitter's age and gender. The risk providing unit can also provide a risk by taking into consideration the submitter's health condition and lifestyle habits. The risk providing unit can also provide a risk based on the submitter's attribute information. In this way, by taking into consideration the submitter's attribute information, more accurate risk provision can be performed. Some or all of the above-mentioned processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input the submitter's attribute information into the generation AI and cause the generation AI to provide the risk.

[0062] When providing a risk, the risk providing unit can weight the risk based on the submission frequency of the anomaly identified by the anomaly identification unit. Consideration of the submission frequency includes, for example, a frequency calculation method, a weighting standard, and the like, but is not limited to these examples. For example, the risk providing unit can provide a risk by prioritizing data submitted frequently. The risk providing unit can also provide a risk by complementing data submitted infrequently. The risk providing unit can also weight the risk based on the submission frequency. In this way, weighting the risk based on the submission frequency can provide a more reliable risk. Some or all of the above-described processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input submission frequency data to a generation AI and have the generation AI perform weighting.

[0063] When providing risks, the risk providing unit can provide risks by taking into account the geographical distribution of the anomalies identified by the anomaly identification unit. Examples of consideration of the geographical distribution include, but are not limited to, a method for collecting location information and a relevance evaluation standard. The risk providing unit can provide region-specific risks, for example, based on the geographical distribution of the anomalies. The risk providing unit can also improve the accuracy of the risks by taking the geographical distribution into account. The risk providing unit can also provide risks by comparing data for each region. In this way, region-specific risks can be provided by taking the geographical distribution into account. Some or all of the above-described processing in the risk providing unit can be performed, for example, using AI or without using AI. For example, the risk providing unit can input geographical distribution data to a generation AI and cause the generation AI to provide risks.

[0064] When providing a risk, the risk providing unit can improve the accuracy of the risk by referring to literature related to the anomaly identified by the anomaly identification unit. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the risk providing unit provides the risk of the anomaly by referring to related literature. The risk providing unit can also improve the accuracy of the risk based on literature data. The risk providing unit can also incorporate the latest research results to provide the risk. By referring to related literature, the accuracy of the risk is improved. Some or all of the above-described processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input related literature data into the generation AI and have the generation AI provide the risk.

[0065] When providing risk, the risk providing unit can provide risk by taking into account the market value of the anomaly identified by the anomaly identification unit. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the risk providing unit provides risk by prioritizing data with high market value. The risk providing unit can also weight risk by taking market value into account. The risk providing unit can also provide risk by reflecting fluctuations in market value. In this way, risk can be weighted by taking market value into account. Some or all of the above-mentioned processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input market value data to the generation AI and cause the generation AI to provide risk.

[0066] When providing a solution, the solution providing unit can improve the accuracy of the solution by taking into account the interrelationships of risks provided by the risk providing unit. Consideration of the interrelationships includes, for example, a method for calculating a correlation coefficient and an analysis algorithm to be used, but is not limited to these examples. The solution providing unit improves the accuracy of the solution, for example, based on data from the risk providing unit. The solution providing unit can also provide a solution by taking into account the interrelationships of risks. The solution providing unit can also improve the accuracy of the solution by reflecting the results of the risk provision. In this way, the accuracy of the solution is improved by taking into account the interrelationships of risks. Some or all of the above-described processing in the solution providing unit may be performed, for example, using AI or without AI. For example, the solution providing unit can input data from the risk providing unit into a generation AI and cause the generation AI to perform analysis to improve the accuracy of the solution.

[0067] When providing a solution, the solution providing unit can provide a solution by taking into consideration attribute information of the submitter of the risk provided by the risk providing unit. Consideration of the submitter's attribute information includes, but is not limited to, age, gender, medical history, etc. The solution providing unit provides a solution by taking into consideration, for example, the submitter's age and gender. The solution providing unit can also provide a solution by taking into consideration the submitter's health condition and lifestyle habits. The solution providing unit can also provide a solution based on the submitter's attribute information. By taking into consideration the submitter's attribute information, more accurate solutions can be provided. Some or all of the above-described processing in the solution providing unit can be performed, for example, using AI or without AI. For example, the solution providing unit can input the submitter's attribute information into the generation AI and cause the generation AI to provide a solution.

[0068] When providing a solution, the solution providing unit can weight the solution based on the submission frequency of the risk provided by the risk providing unit. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the solution providing unit provides a solution by prioritizing data submitted frequently. The solution providing unit can also provide a solution by complementing data submitted infrequently. The solution providing unit can also weight the solution based on the submission frequency. Thus, by weighting the solution based on the submission frequency, more reliable solutions can be provided. Some or all of the above-described processing in the solution providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the solution providing unit can input submission frequency data to a generation AI and have the generation AI perform weighting.

[0069] When providing a solution, the solution providing unit may provide a solution by taking into account the geographical distribution of the risk provided by the risk providing unit. Consideration of the geographical distribution includes, but is not limited to, for example, a method for collecting location information and a relevance evaluation criterion. The solution providing unit may provide, for example, a region-specific solution based on the geographical distribution of the risk. The solution providing unit may also improve the accuracy of the solution by taking the geographical distribution into account. The solution providing unit may also provide a solution by comparing data for each region. In this way, by taking the geographical distribution into account, a region-specific solution can be provided. Some or all of the above-described processing in the solution providing unit may be performed using, or without, AI. For example, the solution providing unit may input geographical distribution data into a generation AI and cause the generation AI to provide a solution.

[0070] When providing a solution, the solution providing unit can improve the accuracy of the solution by referring to literature related to the risk provided by the risk providing unit. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. The solution providing unit, for example, refers to related literature to provide a solution to the risk. The solution providing unit can also improve the accuracy of the solution based on literature data. The solution providing unit can also incorporate the latest research results to provide a solution. By referring to related literature, the accuracy of the solution is improved. Some or all of the above-described processing in the solution providing unit can be performed, for example, using AI or without AI. For example, the solution providing unit can input related literature data into the generation AI and have the generation AI provide a solution.

[0071] When providing a solution, the solution providing unit can provide a solution by taking into account the market value of the risk provided by the risk providing unit. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the solution providing unit provides a solution by emphasizing data with high market value. The solution providing unit can also weight solutions by taking market value into account. The solution providing unit can also provide solutions by reflecting fluctuations in market value. In this way, solutions can be weighted by taking market value into account. Some or all of the above-mentioned processing in the solution providing unit may be performed, for example, using AI or without using AI. For example, the solution providing unit can input market value data into a generation AI and have the generation AI provide a solution.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The health checkup support system can also collect lifestyle habit data from users and use it for analysis. For example, by collecting data on the user's diet, exercise habits, sleep patterns, etc. and combining this data with test values ​​and image data for analysis, it is possible to identify health risks with greater accuracy. It can also make specific improvement suggestions to the user based on the lifestyle habit data. For example, it could suggest improvements to diet or provide an exercise plan. This allows the user to take specific actions in their daily lives and help maintain their health.

[0074] The test value collection unit can collect the user's genetic information and use it for analysis. For example, by collecting the user's genetic test results and analyzing them in combination with other test values ​​and image data, it is possible to identify health risks that take genetic risk factors into account. It can also provide the user with personalized health advice based on the genetic information. For example, it could suggest lifestyle habits to reduce the risk of diseases caused by specific gene mutations. This allows the user to manage their health while taking their own genetic background into account.

[0075] The image collection unit can collect environmental data about the user and use it for analysis. For example, by collecting data about the user's living environment and working environment and analyzing this data in combination with test values ​​and image data, it becomes possible to identify health risks that take into account the impact of environmental factors on health. Specific improvement suggestions can also be made to the user based on the environmental data. For example, suggestions for improving the living environment or the working environment can be provided. This allows the user to manage their health while taking environmental factors into account.

[0076] The correlation analysis unit can collect socioeconomic data on users and use it for analysis. For example, by collecting data such as a user's income, education level, and occupation, and analyzing this data in combination with test results and image data, it is possible to identify health risks that take into account the impact of socioeconomic factors on health. Specific support measures can also be proposed to users based on the socioeconomic data. For example, this could include information on how to use health insurance or introducing local health support services. This allows users to manage their health while taking into account their socioeconomic background.

[0077] The anomaly identification unit can collect psychological data on the user and use it for analysis. For example, by evaluating the user's stress level and psychological state and analyzing this data in combination with test values ​​and image data, it is possible to identify health risks that take into account the impact of psychological factors on health. It can also propose specific mental health support measures to the user based on the psychological data. For example, it could introduce stress management methods or counseling services. This allows the user to manage their health while taking psychological factors into account.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The test value collection unit collects various test values. For example, it collects various test values ​​such as blood tests, urine tests, electrocardiogram data, and hormone levels. It can also estimate the user's emotions and adjust the timing of test value collection based on the estimated emotions. For example, if the user is feeling stressed, it can collect test values ​​during times when the user is able to relax. Step 2: The image collection unit collects image data. For example, it collects medical images such as X-ray images, MRI images, ultrasound images, and CT scan images. It can also estimate the user's emotions and adjust the timing of image data collection based on the estimated emotions. For example, if the user is relaxed, it collects image data at the usual timing. Step 3: The correlation analysis unit analyzes the data collected by the test value collection unit and the image collection unit. For example, it analyzes the association between each test value and image data, and improves the accuracy of the analysis by taking into account the interrelationship between the test values ​​and image data. It can also estimate the user's emotions and adjust the criteria for correlation analysis based on the estimated emotions. For example, if the user is feeling stressed, it performs correlation analysis using simple criteria. Step 4: The anomaly identification unit identifies the location of an anomaly based on the data analyzed by the correlation analysis unit. For example, the accuracy of identification can be improved by taking into account the correlation between the data analyzed by the correlation analysis unit. Identification can also be performed by taking into account the test values ​​and attribute information of the person who submitted the image data. Furthermore, the user's emotions can be estimated and the criteria for identifying an anomaly can be adjusted based on the estimated emotions. For example, if the user is relaxed, anomalies can be identified using detailed criteria. Step 5: The risk providing unit provides health risks to the user based on the anomalies identified by the anomaly identifying unit. For example, the accuracy of the risks is improved by taking into account the correlation between the anomalies identified by the anomaly identifying unit. The risk providing unit can also provide risks by taking into account attribute information of the person who submitted the anomaly identified by the anomaly identifying unit. Furthermore, the user's emotions can be estimated and the method of providing risks can be adjusted based on the estimated emotions. For example, if the user is in a hurry, the risk is provided quickly. Step 6: The solution providing unit provides a solution based on the risks provided by the risk providing unit. For example, the solution providing unit improves the accuracy of the solution by taking into account the interrelationships of the risks provided by the risk providing unit. The solution providing unit can also provide a solution by taking into account attribute information of the submitter of the risk provided by the risk providing unit. Furthermore, the user's emotions can be estimated and the method of providing the solution can be adjusted based on the estimated emotions. For example, if the user is relaxed, the solution is provided in a detailed manner.

[0080] (Example 2) A system according to an embodiment of the present invention uses multimodal AI to analyze the correlation between test values ​​and images during health checkups to identify conditions that may be overlooked by ordinary physicians. This system inputs test values ​​and image data into the multimodal AI, which then analyzes the correlation between the test values ​​and the images to identify potential abnormalities. Based on the analysis results, the AI ​​then provides the user with individual health risks and solutions as a second opinion. For example, the system inputs various test values, such as blood and urine tests, and medical images, such as X-rays and MRI images. The multimodal AI then analyzes the correlation between the input test values ​​and the image data to identify potential abnormalities. Based on the analysis results, the AI ​​then provides the user with individual health risks and solutions. This allows the system to identify conditions that may be overlooked by ordinary physicians. Furthermore, by providing users with individual health risks and solutions, the system can serve as a second opinion. This allows the system to analyze the correlation between test values ​​and images during health checkups to identify conditions that may be overlooked by ordinary physicians. It can also act as a second opinion by providing users with individual health risks and solutions. For example, if AI identifies an abnormality, the user can seek the opinion of another doctor. This will lead to more accurate diagnoses and appropriate treatment.

[0081] A health checkup support system according to an embodiment includes a test value collection unit, an image collection unit, a correlation analysis unit, an abnormality identification unit, a risk reporting unit, and a solution reporting unit. The test value collection unit collects test values. For example, the test value collection unit collects various test values, such as blood test values ​​and urine test values. The test value collection unit can also collect test values ​​such as electrocardiogram data and hormone levels. The test value collection unit can also estimate a user's emotions and adjust the timing of collecting test values ​​based on the estimated user's emotions. For example, if a user is feeling stressed, the test values ​​are collected during a time when the user is able to relax. The image collection unit collects image data. For example, the image collection unit collects medical images, such as X-ray images and MRI images. The image collection unit can also collect medical images, such as ultrasound images and CT scan images. The image collection unit can also estimate a user's emotions and adjust the timing of collecting image data based on the estimated user's emotions. For example, if a user is relaxed, the image collection unit collects image data at a normal time. The correlation analysis unit analyzes the data collected by the test value collection unit and the image collection unit. For example, the correlation analysis unit analyzes the association between each test value and image data. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the test values ​​and the image data. The correlation analysis unit can also estimate the user's emotions and adjust the criteria for the correlation analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the correlation analysis can be performed using simple criteria. The anomaly identification unit identifies the location of an abnormality based on the data analyzed by the correlation analysis unit. For example, the anomaly identification unit can improve the accuracy of the identification by taking into account the interrelationships between the data analyzed by the correlation analysis unit. The anomaly identification unit can also perform the identification by taking into account attribute information of the submitter of the test value and the image data. The anomaly identification unit can also estimate the user's emotions and adjust the criteria for the anomaly identification based on the estimated user's emotions. For example, if the user is relaxed, the anomaly identification can be performed using detailed criteria. The risk providing unit provides the user with health risks based on the abnormalities identified by the anomaly identification unit. For example, the risk providing unit improves the accuracy of the risk by taking into account the interrelationships between the anomalies identified by the anomaly identification unit.The risk providing unit can also provide risks taking into account attribute information of the person who submitted the abnormality identified by the abnormality identifying unit. Furthermore, the risk providing unit can estimate the user's emotions and adjust the method of providing risks based on the estimated user emotions. For example, if the user is in a hurry, the risk can be provided quickly. The solution providing unit provides solutions based on the risks provided by the risk providing unit. For example, the solution providing unit can improve the accuracy of the solutions by taking into account the interrelationships between the risks provided by the risk providing unit. Furthermore, the solution providing unit can estimate the user's emotions and adjust the method of providing solutions based on the estimated user emotions. For example, if the user is relaxed, the solution can be provided in a detailed manner. This allows the health checkup support system according to the embodiment to analyze the correlation between each test value and image data, identify possible abnormalities, and provide the user with health risks and solutions.

[0082] The test value collection unit can collect various test values ​​from blood tests or urine tests. The various test values ​​include, but are not limited to, blood test values, urine test values, and hormone levels. The test value collection unit, for example, collects blood test results. The test value collection unit can also collect urine test results. The test value collection unit can also collect hormone level results. By collecting various test values, it is possible to understand the health condition in more detail. Some or all of the above-mentioned processing in the test value collection unit may be performed, for example, using AI or without using AI. For example, the test value collection unit can input blood test results into the generation AI and have the generation AI analyze the test values.

[0083] The image collection unit can collect medical images such as X-ray images or MRI images. Medical images include, but are not limited to, X-ray images, MRI images, and ultrasound images. The image collection unit, for example, collects X-ray images. The image collection unit can also collect MRI images. The image collection unit can also collect ultrasound images. This allows for the detection of visual abnormalities by collecting medical images. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input X-ray images to the generation AI and have the generation AI analyze the image data.

[0084] The correlation analysis unit can analyze the correlation between each test value and the image data. The analysis of the correlation includes, but is not limited to, for example, a method for calculating a correlation coefficient and an analysis algorithm to be used. For example, the correlation analysis unit calculates a correlation coefficient to analyze the correlation between each test value and the image data. The correlation analysis unit can also analyze the correlation based on the analysis algorithm to be used. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the interrelationship between the test value and the image data. This improves the accuracy of identifying possible abnormalities by analyzing the correlation between each test value and the image data. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input the test value and image data into a generation AI and have the generation AI perform a correlation analysis.

[0085] The anomaly identification unit can identify a location where an anomaly may exist based on the data analyzed by the correlation analysis unit. Identification of a location where an anomaly may exist includes, but is not limited to, an anomaly threshold, an image analysis technique, and the like. The anomaly identification unit can identify a location where an anomaly may exist based on, for example, an anomaly threshold. The anomaly identification unit can also identify a location where an anomaly may exist using an image analysis technique. The anomaly identification unit can also improve the accuracy of identification by taking into account the interrelationships between the data analyzed by the correlation analysis unit. This enables early detection by identifying a location where an anomaly may exist. Some or all of the above-described processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input the data analyzed by the correlation analysis unit into a generation AI and have the generation AI identify an anomaly.

[0086] The risk providing unit can provide a health risk to the user based on the abnormality identified by the anomaly identification unit. The provision of health risks includes, for example, the type of risk and criteria for risk assessment, but is not limited to these examples. For example, the risk providing unit evaluates the risk of the abnormality identified by the anomaly identification unit and provides the risk to the user. The risk providing unit can also provide an appropriate risk depending on the type of risk. The risk providing unit can also improve the accuracy of the risk by taking into account the interrelationships between the abnormalities identified by the anomaly identification unit. This allows the user to take appropriate measures by providing the health risk to the user. Some or all of the above-described processing by the risk providing unit may be performed using, or without, AI. For example, the risk providing unit can input data on the abnormality identified by the anomaly identification unit into a generation AI and cause the generation AI to evaluate the risk.

[0087] The solution providing unit can provide a solution based on the risk provided by the risk providing unit. The provision of solutions includes, but is not limited to, treatments, preventive measures, and lifestyle improvement methods. For example, the solution providing unit can provide an appropriate treatment based on the risk provided by the risk providing unit. The solution providing unit can also provide preventive measures. The solution providing unit can also provide lifestyle improvement methods. By providing solutions, the user can take appropriate measures. Some or all of the above-described processing in the solution providing unit can be performed using, or without, AI. For example, the solution providing unit can input risk data provided by the risk providing unit into a generation AI and have the generation AI execute a proposed solution.

[0088] The test value collection unit can estimate the user's emotions and adjust the timing of test value collection based on the estimated user emotions. For example, if the user is feeling stressed, the test value collection unit collects test values ​​during a time when the user is able to relax. Furthermore, if the user is relaxed, the test value collection unit can also collect test values ​​at a normal timing. Furthermore, if the user is in a hurry, the test value collection unit can also collect test values ​​quickly. By adjusting the timing of test value collection according to the user's emotions, test values ​​can be collected at a more appropriate timing. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the test value collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the test value collection unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0089] The test value collection unit can analyze the user's past test history and select an appropriate collection method. Analysis of the past test history includes, but is not limited to, the type of history data and the analysis algorithm. For example, the test value collection unit prioritizes the selection of a test method that the user has frequently used in the past. The test value collection unit can also select the most efficient collection method based on the user's past test history. The test value collection unit can also suggest the optimal collection method based on the user's past test results. This allows the optimal collection method to be selected by analyzing the past test history. Some or all of the above-described processing in the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's past test history data into a generation AI and have the generation AI select a collection method.

[0090] When collecting test values, the test value collection unit can filter the test values ​​based on the user's current health condition and lifestyle habits. Filtering of the current health condition and lifestyle habits includes, but is not limited to, health condition evaluation criteria and lifestyle habit data collection methods. For example, the test value collection unit collects only necessary test values ​​based on the user's current health condition. The test value collection unit can also prioritize collection of highly relevant test values ​​based on the user's lifestyle habits. The test value collection unit can also collect optimal test values ​​by comprehensively considering the user's health condition and lifestyle habits. This allows collection of only necessary test values ​​taking into account the user's health condition and lifestyle habits. Some or all of the above-described processing by the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's health condition data and lifestyle habit data into a generation AI and have the generation AI perform filtering.

[0091] When collecting test values, the test value collection unit can select the optimal collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the test value collection unit collects test values ​​via voice. Furthermore, if the user desires text input, the test value collection unit can also collect test values ​​via text. Furthermore, if the user desires image input, the test value collection unit can also collect test values ​​via image. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the test value collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the test value collection unit may input the user's input data into a generation AI and have the generation AI select a collection means.

[0092] The test value collection unit can estimate the user's emotions and determine the priority of test values ​​to be collected based on the estimated user emotions. For example, when the user is stressed, the test value collection unit prioritizes collecting important test values. Furthermore, when the user is relaxed, the test value collection unit can also prioritize collecting test values ​​that can be collected quickly when the user is in a hurry. This allows important test values ​​to be collected preferentially according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the test value collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the test value collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] When collecting test values, the test value collection unit can prioritize collecting test values ​​that are highly relevant by taking into account the user's geographical location information. Consideration of geographical location information includes, but is not limited to, the method for collecting location information and the criteria for evaluating relevance. For example, if the user is in a specific area, the test value collection unit prioritizes collecting test values ​​related to that area. The test value collection unit can also collect necessary test values ​​based on the user's geographical location information. The test value collection unit can also collect optimal test values ​​by taking into account the user's location information. This allows necessary test values ​​to be collected based on the user's geographical location information. Some or all of the above-described processing by the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's location information data into the generation AI and have the generation AI select the test values ​​to collect.

[0094] The test value collection unit can analyze the user's social media activity when collecting test values ​​and collect related test values. Analysis of social media activity includes, but is not limited to, activity data collection methods and analysis algorithms. For example, the test value collection unit identifies necessary test values ​​from the user's social media activity. The test value collection unit can also analyze the content of the user's posts and collect related test values. The test value collection unit can also collect optimal test values ​​by referring to the user's social media activity. This allows necessary test values ​​to be collected by referring to the user's social media activity. Some or all of the above-described processing by the test value collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the test value collection unit can input the user's social media data into a generation AI and have the generation AI select test values.

[0095] When collecting test values, the test value collection unit can customize the collection method by reflecting the user's past feedback. Reflecting past feedback includes, but is not limited to, the method for collecting feedback data and the criteria for customization. For example, the test value collection unit selects the optimal collection method based on the user's past feedback. The test value collection unit can also improve the collection method by reflecting the user's feedback. The test value collection unit can also customize the collection method by incorporating the user's opinions. This allows the optimal collection method to be selected based on the user's past feedback. Some or all of the above-described processing in the test value collection unit may be performed using, or without, AI. For example, the test value collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0096] The image collection unit can estimate the user's emotions and adjust the timing of image data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the image collection unit collects image data during a time when the user is able to relax. Furthermore, if the user is relaxed, the image collection unit can also collect image data at a normal time. Furthermore, if the user is in a hurry, the image collection unit can also collect image data quickly. By adjusting the timing of image data collection according to the user's emotions, image data can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The image collection unit can analyze the user's past image data and select the optimal collection method. Analysis of past image data includes, but is not limited to, the type of image data and the analysis algorithm. For example, the image collection unit preferentially selects an image collection method that the user has frequently used in the past. The image collection unit can also select the most efficient collection method from the user's past image data. The image collection unit can also suggest the optimal collection method based on the user's past image data. In this way, the optimal collection method can be selected by analyzing the past image data. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's past image data into a generation AI and have the generation AI select a collection method.

[0098] When collecting image data, the image collection unit can perform filtering based on the user's current health condition and lifestyle habits. Filtering of the current health condition and lifestyle habits includes, but is not limited to, health condition evaluation criteria and lifestyle habit data collection methods. The image collection unit, for example, collects only necessary image data based on the user's current health condition. The image collection unit can also prioritize collection of highly relevant image data based on the user's lifestyle habits. The image collection unit can also collect optimal image data by comprehensively considering the user's health condition and lifestyle habits. This allows collection of only necessary image data by considering the user's health condition and lifestyle habits. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's health condition data and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0099] When collecting image data, the image collection unit can select the optimal collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the image collection unit collects image data by voice. Furthermore, if the user desires text input, the image collection unit can also collect image data by text. Furthermore, if the user desires image input, the image collection unit can also collect image data by image. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0100] The image collection unit can estimate the user's emotions and determine the priority of image data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the image collection unit prioritizes collecting important image data. Furthermore, when the user is relaxed, the image collection unit can also prioritize collecting image data that can be collected quickly when the user is in a hurry. This allows important image data to be collected preferentially according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] When collecting image data, the image collection unit can prioritize collecting highly relevant image data by taking into account the user's geographical location information. Consideration of geographical location information includes, but is not limited to, for example, a location information collection method and relevance evaluation criteria. For example, if the user is in a specific area, the image collection unit prioritizes collecting image data related to that area. The image collection unit can also collect necessary image data based on the user's geographical location information. The image collection unit can also collect optimal image data by taking into account the user's location information. This allows necessary image data to be collected based on the user's geographical location information. Some or all of the above-described processing in the image collection unit may be performed using, or without, AI. For example, the image collection unit can input the user's location information data into a generation AI and have the generation AI select the image data to collect.

[0102] When collecting image data, the image collection unit can analyze the user's social media activity and collect related image data. Analysis of social media activity includes, but is not limited to, activity data collection methods and analysis algorithms. For example, the image collection unit can identify necessary image data from the user's social media activity. The image collection unit can also analyze the content of the user's posts and collect related image data. The image collection unit can also collect optimal image data by referring to the user's social media activity. This makes it possible to collect necessary image data by referring to the user's social media activity. Some or all of the above-described processing in the image collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the image collection unit can input the user's social media data into a generation AI and have the generation AI select image data.

[0103] When collecting image data, the image collection unit can customize the collection method by reflecting the user's past feedback. Reflecting the past feedback includes, for example, a feedback data collection method and customization criteria, but is not limited to these examples. The image collection unit selects, for example, an optimal collection method based on the user's past feedback. The image collection unit can also improve the collection method by reflecting the user's feedback. The image collection unit can also customize the collection method by incorporating the user's opinions. This allows the optimal collection method to be selected based on the user's past feedback. Some or all of the above-described processing in the image collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the image collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0104] The correlation analysis unit can estimate the user's emotions and adjust the criteria for correlation analysis based on the estimated user emotions. For example, if the user is feeling stressed, the correlation analysis unit can perform correlation analysis using simple criteria. Furthermore, if the user is relaxed, the correlation analysis unit can also perform correlation analysis using detailed criteria. Furthermore, if the user is in a hurry, the correlation analysis unit can also perform correlation analysis quickly. This allows for more appropriate analysis by adjusting the criteria for correlation analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, an AI, or without an AI. For example, the correlation analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] The correlation analysis unit can improve the accuracy of the analysis by taking into account the correlation between the test value and the image data during the correlation analysis. Consideration of the correlation includes, for example, the method of calculating the correlation coefficient and the analysis algorithm to be used, but is not limited to these examples. The correlation analysis unit can, for example, analyze the correlation between the test value and the image data in detail to improve the accuracy. The correlation analysis unit can also improve the accuracy of the analysis by taking into account the association between the test value and the image data. The correlation analysis unit can also improve the accuracy of the analysis based on the correlation between the test value and the image data. In this way, the accuracy of the analysis is improved by taking into account the correlation between the test value and the image data. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the correlation analysis unit can input the test value and the image data into a generation AI and have the generation AI analyze the correlation.

[0106] During correlation analysis, the correlation analysis unit can perform the analysis while taking into account attribute information of the submitter of the test values ​​and image data. Consideration of the submitter's attribute information includes, but is not limited to, age, gender, and medical history. The correlation analysis unit performs the correlation analysis while taking into account, for example, the submitter's age and gender. The correlation analysis unit can also perform the correlation analysis while taking into account the submitter's health condition and lifestyle habits. The correlation analysis unit can also perform the correlation analysis based on the submitter's attribute information. By taking the submitter's attribute information into account, more accurate analysis can be performed. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input the submitter's attribute information into the generation AI and have the generation AI perform the analysis.

[0107] During correlation analysis, the correlation analysis unit can weight the analysis based on the frequency of submission of test values ​​and image data. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the correlation analysis unit performs correlation analysis by prioritizing data submitted frequently. The correlation analysis unit can also perform correlation analysis by complementing data submitted infrequently. The correlation analysis unit can also weight the analysis based on the submission frequency. By weighting the analysis based on the submission frequency, more reliable analysis can be performed. Some or all of the above-described processing in the correlation analysis unit may be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input submission frequency data into a generation AI and have the generation AI perform weighting.

[0108] The correlation analysis unit can estimate the user's emotions and adjust the display order of the correlation analysis results based on the estimated user emotions. For example, when the user is stressed, the correlation analysis unit can prioritize displaying important results. Furthermore, when the user is relaxed, the correlation analysis unit can also display detailed results in an orderly manner. Furthermore, when the user is in a hurry, the correlation analysis unit can display results that focus on the key points so that the user can quickly confirm them. By adjusting the display order of the results according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, an AI, or without an AI. For example, the correlation analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0109] The correlation analysis unit can perform correlation analysis while taking into account the geographical distribution of the test values ​​and image data. Consideration of the geographical distribution includes, but is not limited to, for example, the method of collecting location information and the criteria for evaluating the relevance. The correlation analysis unit can also analyze region-specific health risks based on the geographical distribution of the test values ​​and image data. The correlation analysis unit can also improve the accuracy of the analysis by taking the geographical distribution into account. The correlation analysis unit can also perform correlation analysis by comparing data for each region. This allows region-specific health risks to be analyzed by taking the geographical distribution into account. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input geographical distribution data into a generation AI and have the generation AI perform the analysis.

[0110] During correlation analysis, the correlation analysis unit can improve the accuracy of the analysis by referring to literature related to the test values ​​and image data. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the correlation analysis unit analyzes the correlation between the test values ​​and image data by referring to related literature. The correlation analysis unit can also improve the accuracy of the analysis based on literature data. The correlation analysis unit can also perform correlation analysis by incorporating the latest research results. By referring to related literature, the accuracy of the analysis is improved. Some or all of the above-described processing in the correlation analysis unit can be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input related literature data into a generation AI and have the generation AI perform the analysis.

[0111] During correlation analysis, the correlation analysis unit can perform the analysis taking into account the market value of the test values ​​and image data. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the correlation analysis unit performs correlation analysis by placing emphasis on data with high market value. The correlation analysis unit can also weight the analysis taking market value into account. The correlation analysis unit can also perform correlation analysis reflecting fluctuations in market value. In this way, the analysis can be weighted by taking market value into account. Some or all of the above-mentioned processing in the correlation analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the correlation analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0112] The anomaly identification unit can estimate the user's emotions and adjust the criteria for anomaly identification based on the estimated user emotions. For example, if the user is stressed, the anomaly identification unit can identify anomalies using simple criteria. Furthermore, if the user is relaxed, the anomaly identification unit can also identify anomalies using detailed criteria. Furthermore, if the user is in a hurry, the anomaly identification unit can quickly identify anomalies. By adjusting the criteria for anomaly identification according to the user's emotions, more appropriate anomaly identification can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, an AI, or without an AI. For example, the anomaly identification unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] When identifying an anomaly, the anomaly identification unit can improve the accuracy of the identification by taking into account the interrelationships of the data analyzed by the correlation analysis unit. Consideration of the interrelationships includes, for example, the method of calculating the correlation coefficient and the analysis algorithm used, but is not limited to these examples. The anomaly identification unit improves the accuracy of the anomaly identification based on, for example, the data from the correlation analysis unit. The anomaly identification unit can also identify an anomaly by taking into account the interrelationships of the data. The anomaly identification unit can also improve the accuracy of the anomaly identification by reflecting the results of the correlation analysis. Thus, by taking the interrelationships of the data into account, the accuracy of the anomaly identification is improved. Some or all of the above-described processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input data from the correlation analysis unit into a generation AI and cause the generation AI to perform an analysis to improve the identification accuracy.

[0114] When identifying an anomaly, the anomaly identification unit can take into consideration the attribute information of the person who submitted the test values ​​and image data. Consideration of the attribute information of the person who submitted the image data includes, but is not limited to, age, gender, and medical history. The anomaly identification unit can, for example, take into consideration the age and gender of the person who submitted the image data into identifying an anomaly. The anomaly identification unit can also, for example, take into consideration the health condition and lifestyle of the person who submitted the image data into identifying an anomaly. The anomaly identification unit can also, for example, take into consideration the attribute information of the person who submitted the image data into identifying an anomaly. By taking into consideration the attribute information of the person who submitted the image data, anomaly identification can be performed with higher accuracy. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, AI, or without AI. For example, the anomaly identification unit can input the attribute information of the person who submitted the image data into the generation AI and have the generation AI perform the identification.

[0115] When identifying an anomaly, the anomaly identification unit can assign specific weights based on the frequency of submission of test values ​​and image data. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the anomaly identification unit can assign specific weights based on the submission frequency to identify an anomaly. The anomaly identification unit can also assign specific weights based on the submission frequency. By assigning specific weights based on the submission frequency, anomaly identification can be performed with higher reliability. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, AI, or without AI. For example, the anomaly identification unit can input the submission frequency data to a generation AI and have the generation AI perform the weighting.

[0116] The anomaly identification unit can estimate the user's emotions and adjust the order in which the anomaly identification results are displayed based on the estimated user's emotions. For example, when the user is feeling stressed, the anomaly identification unit can prioritize displaying important results. Furthermore, when the user is relaxed, the anomaly identification unit can also display detailed results in an orderly manner. Furthermore, when the user is in a hurry, the anomaly identification unit can display results that focus on the key points so that the user can quickly confirm them. By adjusting the display order of the results according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the anomaly identification unit can be performed using, for example, an AI. For example, the anomaly identification unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0117] The anomaly identification unit can identify anomalies by taking into account the geographical distribution of test values ​​and image data. Examples of consideration of the geographical distribution include, but are not limited to, the method of collecting location information and the criteria for evaluating the relevance. The anomaly identification unit can identify region-specific anomalies based on the geographical distribution of test values ​​and image data. The anomaly identification unit can also improve the accuracy of anomaly identification by taking the geographical distribution into account. The anomaly identification unit can also identify anomalies by comparing data for each region. This allows region-specific anomalies to be identified by taking the geographical distribution into account. Some or all of the above-described processing in the anomaly identification unit can be performed using, or without, AI. For example, the anomaly identification unit can input geographical distribution data into a generation AI and have the generation AI perform the identification.

[0118] When identifying an anomaly, the anomaly identification unit can improve the accuracy of identification by referring to literature related to the test values ​​and image data. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the anomaly identification unit identifies an anomaly in the test values ​​and image data by referring to related literature. The anomaly identification unit can also improve the accuracy of identification based on literature data. The anomaly identification unit can also incorporate the latest research results to identify anomalies. In this way, referring to related literature improves the accuracy of identification. Some or all of the above-described processing in the anomaly identification unit may be performed using, or without, AI. For example, the anomaly identification unit can input related literature data into a generation AI and have the generation AI perform the identification.

[0119] When identifying an anomaly, the anomaly identification unit can take into account the market value of the test value and the image data. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the anomaly identification unit can identify an anomaly by prioritizing data with high market value. The anomaly identification unit can also perform specific weighting by taking market value into account. The anomaly identification unit can also perform anomaly identification by reflecting fluctuations in market value. In this way, specific weighting can be performed by taking market value into account. Some or all of the above-mentioned processing in the anomaly identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the anomaly identification unit can input market value data to a generation AI and have the generation AI perform the identification.

[0120] The risk providing unit can estimate the user's emotions and adjust the method of risk provision based on the estimated user's emotions. For example, if the user is feeling stressed, the risk providing unit can provide risks in a simple manner. Furthermore, if the user is relaxed, the risk providing unit can provide risks in a detailed manner. Furthermore, if the user is in a hurry, the risk providing unit can provide risks quickly. This allows for more appropriate risk provision by adjusting the method of risk provision according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the risk providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0121] When providing the risk, the risk providing unit can improve the accuracy of the risk by taking into account the interrelationships of the anomalies identified by the anomaly identification unit. Consideration of the interrelationships includes, for example, but is not limited to, the method of calculating the correlation coefficient and the analysis algorithm to be used. The risk providing unit can improve the accuracy of the risk, for example, based on the data from the anomaly identification unit. The risk providing unit can also provide the risk by taking into account the interrelationships of the anomalies. The risk providing unit can also improve the accuracy of the risk by reflecting the results of the anomaly identification. In this way, the accuracy of the risk is improved by taking into account the interrelationships of the anomalies. Some or all of the above-described processing in the risk providing unit may be performed, for example, using AI or without AI. For example, the risk providing unit can input data from the anomaly identification unit into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the risk.

[0122] When providing a risk, the risk providing unit can provide a risk by taking into consideration attribute information of the submitter of the abnormality identified by the anomaly identification unit. Consideration of the submitter's attribute information includes, for example, age, gender, medical history, etc., but is not limited to these examples. The risk providing unit provides a risk by taking into consideration, for example, the submitter's age and gender. The risk providing unit can also provide a risk by taking into consideration the submitter's health condition and lifestyle habits. The risk providing unit can also provide a risk based on the submitter's attribute information. In this way, by taking into consideration the submitter's attribute information, more accurate risk provision can be performed. Some or all of the above-mentioned processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input the submitter's attribute information into the generation AI and cause the generation AI to provide the risk.

[0123] When providing a risk, the risk providing unit can weight the risk based on the submission frequency of the anomaly identified by the anomaly identification unit. Consideration of the submission frequency includes, for example, a frequency calculation method, a weighting standard, and the like, but is not limited to these examples. For example, the risk providing unit can provide a risk by prioritizing data submitted frequently. The risk providing unit can also provide a risk by complementing data submitted infrequently. The risk providing unit can also weight the risk based on the submission frequency. In this way, weighting the risk based on the submission frequency can provide a more reliable risk. Some or all of the above-described processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input submission frequency data to a generation AI and have the generation AI perform weighting.

[0124] The risk providing unit can estimate the user's emotions and adjust the order in which the risk providing results are displayed based on the estimated user's emotions. For example, when the user is feeling stressed, the risk providing unit can prioritize displaying important results. Furthermore, when the user is relaxed, the risk providing unit can also display detailed results in an orderly manner. Furthermore, when the user is in a hurry, the risk providing unit can display results that focus on the key points so that the user can quickly confirm them. By adjusting the display order of the results according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the risk providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk providing unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0125] When providing risks, the risk providing unit can provide risks by taking into account the geographical distribution of the anomalies identified by the anomaly identification unit. Examples of consideration of the geographical distribution include, but are not limited to, a method for collecting location information and a relevance evaluation standard. The risk providing unit can provide region-specific risks, for example, based on the geographical distribution of the anomalies. The risk providing unit can also improve the accuracy of the risks by taking the geographical distribution into account. The risk providing unit can also provide risks by comparing data for each region. In this way, region-specific risks can be provided by taking the geographical distribution into account. Some or all of the above-described processing in the risk providing unit can be performed, for example, using AI or without using AI. For example, the risk providing unit can input geographical distribution data to a generation AI and cause the generation AI to provide risks.

[0126] When providing a risk, the risk providing unit can improve the accuracy of the risk by referring to literature related to the anomaly identified by the anomaly identification unit. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the risk providing unit provides the risk of the anomaly by referring to related literature. The risk providing unit can also improve the accuracy of the risk based on literature data. The risk providing unit can also incorporate the latest research results to provide the risk. By referring to related literature, the accuracy of the risk is improved. Some or all of the above-described processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input related literature data into the generation AI and have the generation AI provide the risk.

[0127] When providing risk, the risk providing unit can provide risk by taking into account the market value of the anomaly identified by the anomaly identification unit. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the risk providing unit provides risk by prioritizing data with high market value. The risk providing unit can also weight risk by taking market value into account. The risk providing unit can also provide risk by reflecting fluctuations in market value. In this way, risk can be weighted by taking market value into account. Some or all of the above-mentioned processing in the risk providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk providing unit can input market value data to the generation AI and cause the generation AI to provide risk.

[0128] The solution providing unit can estimate the user's emotions and adjust the method of providing a solution based on the estimated user's emotions. For example, if the user is feeling stressed, the solution providing unit can provide a simple solution. Furthermore, if the user is relaxed, the solution providing unit can provide a detailed solution. Furthermore, if the user is in a hurry, the solution providing unit can provide a quick solution. By adjusting the method of providing a solution according to the user's emotions, a more appropriate solution can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the solution providing unit can be performed using, for example, an AI, or without an AI. For example, the solution providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0129] When providing a solution, the solution providing unit can improve the accuracy of the solution by taking into account the interrelationships of risks provided by the risk providing unit. Consideration of the interrelationships includes, for example, a method for calculating a correlation coefficient and an analysis algorithm to be used, but is not limited to these examples. The solution providing unit improves the accuracy of the solution, for example, based on data from the risk providing unit. The solution providing unit can also provide a solution by taking into account the interrelationships of risks. The solution providing unit can also improve the accuracy of the solution by reflecting the results of the risk provision. In this way, the accuracy of the solution is improved by taking into account the interrelationships of risks. Some or all of the above-described processing in the solution providing unit may be performed, for example, using AI or without AI. For example, the solution providing unit can input data from the risk providing unit into a generation AI and cause the generation AI to perform analysis to improve the accuracy of the solution.

[0130] When providing a solution, the solution providing unit can provide a solution by taking into consideration attribute information of the submitter of the risk provided by the risk providing unit. Consideration of the submitter's attribute information includes, but is not limited to, age, gender, medical history, etc. The solution providing unit provides a solution by taking into consideration, for example, the submitter's age and gender. The solution providing unit can also provide a solution by taking into consideration the submitter's health condition and lifestyle habits. The solution providing unit can also provide a solution based on the submitter's attribute information. By taking into consideration the submitter's attribute information, more accurate solutions can be provided. Some or all of the above-described processing in the solution providing unit can be performed, for example, using AI or without AI. For example, the solution providing unit can input the submitter's attribute information into the generation AI and cause the generation AI to provide a solution.

[0131] When providing a solution, the solution providing unit can weight the solution based on the submission frequency of the risk provided by the risk providing unit. Consideration of the submission frequency includes, but is not limited to, for example, a method for calculating the frequency and a weighting standard. For example, the solution providing unit provides a solution by prioritizing data submitted frequently. The solution providing unit can also provide a solution by complementing data submitted infrequently. The solution providing unit can also weight the solution based on the submission frequency. Thus, by weighting the solution based on the submission frequency, more reliable solutions can be provided. Some or all of the above-described processing in the solution providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the solution providing unit can input submission frequency data to a generation AI and have the generation AI perform weighting.

[0132] The solution providing unit can estimate the user's emotions and adjust the order in which the solution results are displayed based on the estimated user emotions. For example, when the user is feeling stressed, the solution providing unit can prioritize displaying important results. Furthermore, when the user is relaxed, the solution providing unit can also display detailed results in an orderly manner. Furthermore, when the user is in a hurry, the solution providing unit can display results that focus on the key points so that the user can quickly confirm them. By adjusting the display order of the results according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the solution providing unit can be performed using, for example, an AI, or without an AI. For example, the solution providing unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0133] When providing a solution, the solution providing unit may provide a solution by taking into account the geographical distribution of the risk provided by the risk providing unit. Consideration of the geographical distribution includes, but is not limited to, for example, a method for collecting location information and a relevance evaluation criterion. The solution providing unit may provide, for example, a region-specific solution based on the geographical distribution of the risk. The solution providing unit may also improve the accuracy of the solution by taking the geographical distribution into account. The solution providing unit may also provide a solution by comparing data for each region. In this way, by taking the geographical distribution into account, a region-specific solution can be provided. Some or all of the above-described processing in the solution providing unit may be performed using, or without, AI. For example, the solution providing unit may input geographical distribution data into a generation AI and cause the generation AI to provide a solution.

[0134] When providing a solution, the solution providing unit can improve the accuracy of the solution by referring to literature related to the risk provided by the risk providing unit. Referencing related literature includes, but is not limited to, literature selection criteria and reference methods. The solution providing unit, for example, refers to related literature to provide a solution to the risk. The solution providing unit can also improve the accuracy of the solution based on literature data. The solution providing unit can also incorporate the latest research results to provide a solution. By referring to related literature, the accuracy of the solution is improved. Some or all of the above-described processing in the solution providing unit can be performed, for example, using AI or without AI. For example, the solution providing unit can input related literature data into the generation AI and have the generation AI provide a solution.

[0135] When providing a solution, the solution providing unit can provide a solution by taking into account the market value of the risk provided by the risk providing unit. Consideration of market value includes, for example, value evaluation criteria, weighting methods, etc., but is not limited to these examples. For example, the solution providing unit provides a solution by emphasizing data with high market value. The solution providing unit can also weight solutions by taking market value into account. The solution providing unit can also provide solutions by reflecting fluctuations in market value. In this way, solutions can be weighted by taking market value into account. Some or all of the above-mentioned processing in the solution providing unit may be performed, for example, using AI or without using AI. For example, the solution providing unit can input market value data into a generation AI and have the generation AI provide a solution. === Hard Collateral 1-1 === Each of the multiple elements including the above-described test value collection unit, image collection unit, correlation analysis unit, abnormality identification unit, risk providing unit, and solution method providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the test value collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the image collection unit is realized by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the correlation analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the abnormality identification unit is realized by the specific processing unit 290 of the data processing device 12. For example, the risk providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the solution method providing unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned test value collection unit, image collection unit, correlation analysis unit, anomaly identification unit, risk providing unit, and solution method providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the test value collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the image collection unit is realized by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the correlation analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the anomaly identification unit is realized by the specific processing unit 290 of the data processing device 12. For example, the risk providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the solution method providing unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned test value collection unit, image collection unit, correlation analysis unit, abnormality identification unit, risk providing unit, and solution method providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the test value collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the image collection unit is realized by the camera 42 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the correlation analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the abnormality identification unit is realized by the specific processing unit 290 of the data processing device 12. For example, the risk providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the solution method providing unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-described test value collection unit, image collection unit, correlation analysis unit, anomaly identification unit, risk provision unit, and solution method provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the test value collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the image collection unit is realized by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the correlation analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the anomaly identification unit is realized by the specific processing unit 290 of the data processing device 12. For example, the risk provision unit is realized by the specific processing unit 290 of the data processing device 12. For example, the solution method provision unit is realized by the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The health checkup support system can also collect lifestyle habit data from users and use it for analysis. For example, by collecting data on the user's diet, exercise habits, sleep patterns, etc. and combining this data with test values ​​and image data for analysis, it is possible to identify health risks with greater accuracy. It can also make specific improvement suggestions to the user based on the lifestyle habit data. For example, it could suggest improvements to diet or provide an exercise plan. This allows the user to take specific actions in their daily lives and help maintain their health.

[0138] The test value collection unit can collect the user's genetic information and use it for analysis. For example, by collecting the user's genetic test results and analyzing them in combination with other test values ​​and image data, it is possible to identify health risks that take genetic risk factors into account. It can also provide the user with personalized health advice based on the genetic information. For example, it could suggest lifestyle habits to reduce the risk of diseases caused by specific gene mutations. This allows the user to manage their health while taking their own genetic background into account.

[0139] The image collection unit can collect environmental data about the user and use it for analysis. For example, by collecting data about the user's living environment and working environment and analyzing this data in combination with test values ​​and image data, it becomes possible to identify health risks that take into account the impact of environmental factors on health. Specific improvement suggestions can also be made to the user based on the environmental data. For example, suggestions for improving the living environment or the working environment can be provided. This allows the user to manage their health while taking environmental factors into account.

[0140] The correlation analysis unit can collect socioeconomic data on users and use it for analysis. For example, by collecting data such as a user's income, education level, and occupation, and analyzing this data in combination with test results and image data, it is possible to identify health risks that take into account the impact of socioeconomic factors on health. Specific support measures can also be proposed to users based on the socioeconomic data. For example, this could include information on how to use health insurance or introducing local health support services. This allows users to manage their health while taking into account their socioeconomic background.

[0141] The anomaly identification unit can collect psychological data on the user and use it for analysis. For example, by evaluating the user's stress level and psychological state and analyzing this data in combination with test values ​​and image data, it is possible to identify health risks that take into account the impact of psychological factors on health. It can also propose specific mental health support measures to the user based on the psychological data. For example, it could introduce stress management methods or counseling services. This allows the user to manage their health while taking psychological factors into account.

[0142] The risk provision unit can estimate the user's emotions and adjust the content of the risk provision based on the estimated user's emotions. For example, if the user is feeling anxious, the risk provision unit can explain the risks in terms that give a sense of security. If the user is relaxed, the risk provision unit can also provide detailed risk information. Furthermore, if the user is in a hurry, the risk provision unit can also provide risk information that briefly summarizes the main points. In this way, by adjusting the content of the risk provision according to the user's emotions, more appropriate information can be provided.

[0143] The solution providing unit can estimate the user's emotions and adjust the proposed solutions based on the estimated user's emotions. For example, if the user is feeling stressed, the solution providing unit can prioritize suggesting methods that are effective in reducing stress. Also, if the user is relaxed, the solution providing unit can provide detailed solutions. Furthermore, if the user is in a hurry, the solution providing unit can suggest solutions that can be implemented quickly. In this way, more appropriate support can be provided by adjusting the proposed solutions according to the user's emotions.

[0144] The test value collection unit can estimate the user's emotions and adjust the types of test values ​​to be collected based on the estimated user emotions. For example, if the user is feeling stressed, stress-related test values ​​can be collected with priority. Alternatively, if the user is relaxed, regular test values ​​can be collected. Furthermore, if the user is in a hurry, test values ​​that can be collected quickly can be collected with priority. In this way, adjusting the types of test values ​​to be collected depending on the user's emotions allows for more appropriate data collection.

[0145] The image collection unit can estimate the user's emotions and adjust the type of image data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, stress-related image data can be collected preferentially. If the user is relaxed, normal image data can be collected. Furthermore, if the user is in a hurry, image data that can be collected quickly can be collected preferentially. In this way, by adjusting the type of image data to be collected according to the user's emotions, more appropriate data collection can be performed.

[0146] The correlation analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, important analysis results can be displayed concisely. If the user is relaxed, detailed analysis results can be displayed in an orderly manner. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed quickly. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The test value collection unit collects various test values. For example, it collects various test values ​​such as blood tests, urine tests, electrocardiogram data, and hormone levels. It can also estimate the user's emotions and adjust the timing of test value collection based on the estimated emotions. For example, if the user is feeling stressed, it can collect test values ​​during times when the user is able to relax. Step 2: The image collection unit collects image data. For example, it collects medical images such as X-ray images, MRI images, ultrasound images, and CT scan images. It can also estimate the user's emotions and adjust the timing of image data collection based on the estimated emotions. For example, if the user is relaxed, it collects image data at the usual timing. Step 3: The correlation analysis unit analyzes the data collected by the test value collection unit and the image collection unit. For example, it analyzes the association between each test value and image data, and improves the accuracy of the analysis by taking into account the interrelationship between the test values ​​and image data. It can also estimate the user's emotions and adjust the criteria for correlation analysis based on the estimated emotions. For example, if the user is feeling stressed, it performs correlation analysis using simple criteria. Step 4: The anomaly identification unit identifies the location of an anomaly based on the data analyzed by the correlation analysis unit. For example, the accuracy of identification can be improved by taking into account the correlation between the data analyzed by the correlation analysis unit. Identification can also be performed by taking into account the test values ​​and attribute information of the person who submitted the image data. Furthermore, the user's emotions can be estimated and the criteria for identifying an anomaly can be adjusted based on the estimated emotions. For example, if the user is relaxed, anomalies can be identified using detailed criteria. Step 5: The risk providing unit provides health risks to the user based on the anomalies identified by the anomaly identifying unit. For example, the accuracy of the risks is improved by taking into account the correlation between the anomalies identified by the anomaly identifying unit. The risk providing unit can also provide risks by taking into account attribute information of the person who submitted the anomaly identified by the anomaly identifying unit. Furthermore, the user's emotions can be estimated and the method of providing risks can be adjusted based on the estimated emotions. For example, if the user is in a hurry, the risk is provided quickly. Step 6: The solution providing unit provides a solution based on the risks provided by the risk providing unit. For example, the solution providing unit improves the accuracy of the solution by taking into account the interrelationships of the risks provided by the risk providing unit. The solution providing unit can also provide a solution by taking into account attribute information of the submitter of the risk provided by the risk providing unit. Furthermore, the user's emotions can be estimated and the method of providing the solution can be adjusted based on the estimated emotions. For example, if the user is relaxed, the solution is provided in a detailed manner.

[0149] 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.

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

[0151] 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.

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

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 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.

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

[0156] The 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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 test value collection unit that collects each test value; an image collection unit that collects image data; a correlation analysis unit that analyzes the data collected by the test value collection unit and the image collection unit; an abnormality identification unit that identifies an abnormality location based on the data analyzed by the correlation analysis unit; a risk providing unit that provides a health risk to a user based on the abnormality identified by the abnormality identifying unit; a solution providing unit that provides a solution based on the risk provided by the risk providing unit; Equipped with A system characterized by:

2. The test value collection unit Collect various blood or urine test results 2. The system of claim 1.

3. The image acquisition unit Collect medical images such as X-rays or MRIs 2. The system of claim 1.

4. The correlation analysis unit Analyze the relationship between each test value and image data 2. The system of claim 1.

5. The abnormality identification unit Identifying a location where there is a possibility of an abnormality based on the data analyzed by the correlation analysis unit 2. The system of claim 1.

6. The risk provider Providing health risks to a user based on the abnormality identified by the abnormality identifying unit 2. The system of claim 1.

7. The solution providing unit: Providing a solution based on the risk provided by the risk provider 2. The system of claim 1.

8. The test value collection unit Estimates the user's emotions and adjusts the timing of test value collection based on the estimated user emotions.

2. The system of claim 1.

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