system

Abstract

The system according to the embodiment aims to suggest appropriate hospitals and medical departments based on the results of health checkups and daily lifestyle habits. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The suggestion unit suggests hospitals and medical departments based on the match rate generated by the generation unit.

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 technologies do not adequately suggest appropriate hospitals and medical departments based on health checkup results and daily lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate hospitals and medical departments based on the results of health checkups and daily lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The suggestion unit suggests hospitals and medical departments based on the match rate generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate hospitals and medical departments based on the results of health checkups and daily lifestyle habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention collects data such as a user's health checkup results, physical condition, and daily lifestyle habits, and uses AI to analyze this data and generate a match rate with various diseases. Based on the generated match rate, the health management system makes suggestions to the user, such as "Could this be your disease?" The user can then compare the suggested disease name with their own physical discomfort and find an appropriate opportunity to visit an appropriate hospital or clinic. For example, the health management system collects data such as the user's health checkup results, physical condition, and daily lifestyle habits. For example, the health management system collects data such as blood test results, weight, height, blood pressure, location and severity of pain, skin roughness and hair loss, and diet and exercise habits. Next, the health management system uses AI to analyze the collected data. Based on this data, the AI ​​generates a match rate with various diseases. For example, the health management system analyzes data such as blood test results, physical condition, and lifestyle habits to calculate a match rate with diseases such as diabetes, high blood pressure, heart disease, and skin disorders. Next, the health management system makes suggestions to the user, such as "Could this be your disease?" based on the generated match rate. For example, if the AI ​​analysis results show a high match rate for diabetes, it will suggest to the user, "You might have diabetes." Based on the suggested disease name, the user can compare it with their own physical condition and get an opportunity to visit an appropriate hospital or medical department. For example, a user who receives a suggestion for diabetes might consider visiting an internal medicine or diabetes clinic. This allows the health management system to detect their own physical condition early and ensure they receive appropriate medical care. For example, a user who receives a suggestion to visit a dermatologist based on their skin condition or hair loss can visit a dermatologist early and receive appropriate treatment. This allows the health management system to detect their own physical condition early and ensure they receive appropriate medical care. For example, a user who receives a suggestion to visit a dermatologist based on their skin condition or hair loss can visit a dermatologist early and receive appropriate treatment.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects data such as a user's health checkup results, physical condition, and daily lifestyle habits. For example, the collection unit collects data such as blood test results, weight, height, blood pressure, pain location and severity, skin roughness and hair loss, and diet and exercise habits. The collection unit may collect data manually by a user or using a wearable device. For example, the collection unit may manually input data using a keyboard or a touchscreen. The collection unit may also collect data using a wearable device such as a smartwatch or fitness tracker. The analysis unit analyzes the data collected by the collection unit. The analysis unit may use a machine learning algorithm, such as a decision tree or a neural network. For example, the analysis unit may classify data using a decision tree and calculate a disease match rate. The analysis unit may also analyze data using a neural network and calculate a disease match rate. The analysis unit may also analyze data using statistical analysis. The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The generation unit calculates the match rate using, for example, a probabilistic method or a scoring system. For example, the generation unit calculates the probability of disease occurrence using a probabilistic method and generates the match rate. The generation unit can also calculate the disease match rate using a scoring system. The suggestion unit suggests appropriate hospitals and medical departments based on the match rate generated by the generation unit. The suggestion unit suggests the nearest medical institution based on, for example, the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through the smartphone app and suggests an appropriate medical institution. As a result, the health management system according to the embodiment collects and analyzes the user's health data, generates a disease match rate, and suggests appropriate medical institutions, enabling the user to receive appropriate medical care early.

[0030] The collection unit may collect data by a method including, but not limited to, manual input by a user and a method using a wearable device. For example, the collection unit may manually input data using a keyboard or a touchscreen. The collection unit may also collect data using a wearable device such as a smartwatch or fitness tracker. For example, the collection unit may use a smartwatch to collect data such as heart rate, step count, and sleep patterns. The collection unit may also use a fitness tracker to collect data such as exercise volume and calorie consumption. This increases the flexibility of data collection by including both manual input by a user and a method using a wearable device. Some or all of the above-described processing by the collection unit may be performed using, for example, AI or without AI. For example, the collection unit may input data acquired from the wearable device into AI and have the AI ​​analyze and filter the data.

[0031] The analysis unit uses a machine learning algorithm, including, but not limited to, a decision tree or a neural network. The analysis unit, for example, uses a decision tree to classify data and calculate a disease match rate. For example, the analysis unit uses a decision tree to classify the user's health data and evaluate the risk of disease. The analysis unit can also analyze data and calculate a disease match rate using a neural network. For example, the analysis unit can analyze the user's health data using a neural network to predict the probability of disease occurrence. The analysis unit can also analyze data using statistical analysis. For example, the analysis unit can analyze the user's health data using statistical analysis to evaluate the risk of disease. By using a machine learning algorithm and including a decision tree or a neural network, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's health data into AI and have the AI ​​analyze the data.

[0032] The suggestion unit can suggest the nearest medical institution based on the user's location information and notify the user using a smartphone app. The suggestion unit, for example, suggests the nearest medical institution based on the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through a smartphone app and suggests an appropriate medical institution. By suggesting the nearest medical institution based on the user's location information and notifying the user using the smartphone app, the user can quickly access an appropriate medical institution. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's location information into AI and have the AI ​​select the most appropriate medical institution.

[0033] The collection unit can select a data collection method based on the user's past health checkup results. For example, the collection unit can collect data focusing on specific items from the user's past health checkup results. The collection unit can also adjust the frequency of required data collection based on the user's past health checkup results. The collection unit can also add specific test items by referring to the user's past health checkup results. This allows the user's past health checkup results to be analyzed to select the optimal data collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health checkup results into AI and have the AI ​​select the optimal data collection method.

[0034] When collecting data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is currently on a diet, the collection unit can prioritize collecting data related to diet and exercise. Furthermore, if the user is interested in a particular disease, the collection unit can also collect data related to that disease. Furthermore, the collection unit can adjust the frequency and content of data collection according to the user's lifestyle (e.g., how busy they are at work). This allows for more relevant data to be collected by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0035] When collecting data, the collection unit can select a collection means selected according to the user's input method. For example, if the user prefers voice input, the collection unit can prioritize voice data collection. Furthermore, if the user prefers text input, the collection unit can also prioritize text data collection. Furthermore, if the user prefers image input, the collection unit can also prioritize image data collection. This improves user convenience by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's input method into AI and have the AI ​​select the optimal collection means.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit collects data related to health risks specific to high altitudes. Furthermore, if the user lives in an urban area, the collection unit can collect data related to health risks specific to urban areas. Furthermore, if the user lives overseas, the collection unit can collect data related to health risks specific to that area. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0037] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, if the user posts about health on social media, the collection unit can collect data based on the content of the posts. Furthermore, if the user expresses interest in a particular disease on social media, the collection unit can collect data related to the disease. Furthermore, the collection unit can collect data about lifestyle habits from the user's social media activity. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data about the user's social media activity into AI and have the AI ​​collect related data.

[0038] The collection unit can customize the collection method based on the user's past feedback when collecting data. For example, the collection unit can adjust the frequency of data collection based on the user's past feedback. The collection unit can also adjust the type of data to collect based on the user's past feedback. The collection unit can also adjust the collection method (audio, text, image, etc.) based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the collection method.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to blood test data. The analysis unit can also apply a different analysis algorithm to lifestyle habit data. The analysis unit can also apply yet another analysis algorithm to data regarding the location and severity of pain. In this way, by applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the data category into AI and have the AI ​​select the optimal analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the relevance of the data to AI and have the AI ​​adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis to suit the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented depending on the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis depending on the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the user's level of expertise into AI and have the AI ​​adjust the way in which the analysis results are presented.

[0045] When generating a match rate, the generation unit can improve the accuracy of the generation based on the interrelationships of data. The generation unit, for example, combines blood test results with lifestyle habit data to generate a match rate. The generation unit can also combine the location and severity of pain with past health checkup results to generate a match rate. The generation unit can also combine skin roughness and hair loss status with dietary data to generate a match rate. This improves the accuracy of generating a match rate by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information regarding the interrelationships of data into AI and have the AI ​​generate a match rate.

[0046] When generating a match rate, the generation unit can take into consideration attribute information of the data submitter. The generation unit generates the match rate by taking into consideration, for example, the user's age and gender. The generation unit can also generate the match rate by taking into consideration the user's occupation and living environment. The generation unit can also generate the match rate by taking into consideration the user's medical history and family history. In this way, by taking into consideration the attribute information of the data submitter, a more personalized match rate can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's attribute information into AI and have the AI ​​generate the match rate.

[0047] When generating a match rate, the generation unit can generate weights based on the frequency of data submission. For example, the generation unit can assign a higher weight to data that is frequently submitted. The generation unit can also assign a lower weight to data that is submitted less frequently. The generation unit can also adjust the weighting of the match rate based on the submission frequency. In this way, by assigning weights based on the frequency of data submission, a more reliable match rate can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information regarding the frequency of data submission into AI and have the AI ​​adjust the weighting.

[0048] When generating a match rate, the generation unit can generate the match rate based on the geographic distribution of data. For example, the generation unit can generate the match rate by taking into account data related to diseases specific to the area where the user lives. The generation unit can also generate the match rate by taking into account health risks at the user's travel destinations. The generation unit can also generate a match rate for related diseases based on the user's geographic movement history. In this way, by taking into account the geographic distribution of data, a match rate that reflects health risks specific to the area can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information about the user's geographic distribution into AI and have the AI ​​generate the match rate.

[0049] When generating a match rate, the generation unit can improve the accuracy of the generation by referring to literature related to the data. The generation unit, for example, generates a match rate by referring to the latest medical papers. The generation unit can also generate a match rate based on past research data. The generation unit can also improve the accuracy of the match rate by referring to medical evidence. In this way, by referring to related literature, the accuracy of the generated match rate is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about related literature into AI and have the AI ​​generate the match rate.

[0050] When generating a match rate, the generation unit can generate the match rate based on the market value of the data. For example, the generation unit can assign a higher weight to data with a high market value. The generation unit can also assign a lower weight to data with a low market value. The generation unit can also adjust the weighting of the match rate based on the market value of the data. In this way, by taking the market value of the data into consideration, a match rate that reflects more valuable data can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information regarding the market value of the data into AI and have the AI ​​adjust the weighting.

[0051] The suggestion unit can adjust the level of detail of the proposal based on the importance of the disease when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important disease. The suggestion unit can also make a simplified suggestion for an illness with a low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the disease. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the disease. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the importance of the disease to AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0052] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, the suggestion unit can apply a specific suggestion algorithm to internal diseases. The suggestion unit can also apply a different suggestion algorithm to dermatological diseases. The suggestion unit can also apply yet another suggestion algorithm to psychiatric diseases. In this way, by applying different suggestion algorithms depending on the disease category, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the disease category into AI and cause the AI ​​to select the optimal suggestion algorithm.

[0053] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion result based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0054] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time when the illness was submitted. For example, the suggestion unit can prioritize proposals for recently discovered illnesses. The suggestion unit can also postpone proposals for illnesses that were submitted earlier. The suggestion unit can also adjust the order of proposals based on the time when the illness was submitted. This enables efficient proposals by determining the priority of proposals based on the time when the illness was submitted. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information regarding the time when the illness was submitted into AI and have the AI ​​determine the priority of the proposals.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the diseases when making suggestions. For example, the suggestion unit prioritizes suggestions of highly relevant diseases. The suggestion unit can also postpone suggestions of less relevant diseases. The suggestion unit can also determine the order of suggestions based on the relevance of the diseases. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the diseases. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information on the relevance of diseases to AI and cause the AI ​​to adjust the order of suggestions.

[0056] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal to match the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terms. If the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion to be easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information regarding the user's level of expertise into AI and cause the AI ​​to adjust the way the proposal is expressed.

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

[0058] The collection unit can select a data collection method based on the user's past health checkup results. For example, the collection unit can collect data with an emphasis on specific items based on the user's past health checkup results. The collection unit can also adjust the frequency of required data collection based on the user's past health checkup results. Furthermore, the collection unit can add specific test items by referring to the user's past health checkup results. This allows the collection unit to select the optimal data collection method and efficiently collect data by analyzing the user's past health checkup results. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health checkup results into AI and have the AI ​​select the optimal data collection method.

[0059] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the analysis priority can be determined according to the importance of the data. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the importance of the data to AI and have the AI ​​adjust the level of analysis detail.

[0060] When generating a match rate, the generation unit can improve the accuracy of the generation based on the interrelationships of data. For example, the match rate can be generated by combining blood test results with lifestyle habit data. The match rate can also be generated by combining the location and severity of pain with past health checkup results. Furthermore, the match rate can be generated by combining skin roughness and hair loss status with dietary data. This improves the accuracy of generating a match rate by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information regarding the interrelationships of data into AI and have the AI ​​generate the match rate.

[0061] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the disease. For example, a detailed proposal can be made for an important disease. A simplified proposal can also be made for an illness of low importance. Furthermore, the priority of the proposal can be determined according to the importance of the disease. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the disease. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the importance of the disease into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0062] When collecting data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is currently on a diet, data related to diet and exercise can be collected preferentially. Also, if the user is interested in a particular illness, data related to that illness can be collected. Furthermore, the frequency and content of data collection can be adjusted depending on the user's lifestyle (such as how busy they are at work). This allows more relevant data to be collected by filtering data based on the user's lifestyle and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0063] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, a specific suggestion algorithm can be applied to internal medicine diseases. A different suggestion algorithm can be applied to dermatology diseases. Furthermore, yet another suggestion algorithm can be applied to psychiatric diseases. By applying different suggestion algorithms depending on the disease category, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the disease category into AI and cause the AI ​​to select the optimal suggestion algorithm.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits. For example, the collection unit collects data such as blood test results, weight, height, blood pressure, pain location and severity, skin roughness and hair loss, and diet and exercise habits. The collection unit can collect data by manually inputting the data by the user or by using a wearable device. For example, the collection unit can collect data by manually inputting the data using a keyboard or touch screen. The collection unit can also collect data using a wearable device such as a smartwatch or fitness tracker. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms, including decision trees and neural networks. For example, the analysis unit classifies the data using a decision tree and calculates a disease match rate. The analysis unit can also analyze the data using a neural network and calculate a disease match rate. The analysis unit can also analyze the data using statistical analysis. Step 3: The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The generation unit calculates the match rate using, for example, a probabilistic method or a scoring system. For example, the generation unit calculates the probability of occurrence of the disease using a probabilistic method and generates the match rate. The generation unit can also calculate the disease match rate using a scoring system. Step 4: The suggestion unit suggests appropriate hospitals and medical departments based on the match rates generated by the generation unit. The suggestion unit suggests the nearest medical institution based on, for example, the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through a smartphone app and suggests an appropriate medical institution.

[0066] (Example 2) A health management system according to an embodiment of the present invention collects data such as a user's health checkup results, physical condition, and daily lifestyle habits, and uses AI to analyze this data and generate a match rate with various diseases. Based on the generated match rate, the health management system makes suggestions to the user, such as "Could this be your disease?" The user can then compare the suggested disease name with their own physical discomfort and find an appropriate opportunity to visit an appropriate hospital or clinic. For example, the health management system collects data such as the user's health checkup results, physical condition, and daily lifestyle habits. For example, the health management system collects data such as blood test results, weight, height, blood pressure, location and severity of pain, skin roughness and hair loss, and diet and exercise habits. Next, the health management system uses AI to analyze the collected data. Based on this data, the AI ​​generates a match rate with various diseases. For example, the health management system analyzes data such as blood test results, physical condition, and lifestyle habits to calculate a match rate with diseases such as diabetes, high blood pressure, heart disease, and skin disorders. Next, the health management system makes suggestions to the user, such as "Could this be your disease?" based on the generated match rate. For example, if the AI ​​analysis results show a high match rate for diabetes, it will suggest to the user, "You might have diabetes." Based on the suggested disease name, the user can compare it with their own physical condition and get an opportunity to visit an appropriate hospital or medical department. For example, a user who receives a suggestion for diabetes might consider visiting an internal medicine or diabetes clinic. This allows the health management system to detect their own physical condition early and ensure they receive appropriate medical care. For example, a user who receives a suggestion to visit a dermatologist based on their skin condition or hair loss can visit a dermatologist early and receive appropriate treatment. This allows the health management system to detect their own physical condition early and ensure they receive appropriate medical care. For example, a user who receives a suggestion to visit a dermatologist based on their skin condition or hair loss can visit a dermatologist early and receive appropriate treatment.

[0067] A health management system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects data such as a user's health checkup results, physical condition, and daily lifestyle habits. For example, the collection unit collects data such as blood test results, weight, height, blood pressure, pain location and severity, skin roughness and hair loss, and diet and exercise habits. The collection unit may collect data manually by a user or using a wearable device. For example, the collection unit may manually input data using a keyboard or a touchscreen. The collection unit may also collect data using a wearable device such as a smartwatch or fitness tracker. The analysis unit analyzes the data collected by the collection unit. The analysis unit may use a machine learning algorithm, such as a decision tree or a neural network. For example, the analysis unit may classify data using a decision tree and calculate a disease match rate. The analysis unit may also analyze data using a neural network and calculate a disease match rate. The analysis unit may also analyze data using statistical analysis. The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The generation unit calculates the match rate using, for example, a probabilistic method or a scoring system. For example, the generation unit calculates the probability of disease occurrence using a probabilistic method and generates the match rate. The generation unit can also calculate the disease match rate using a scoring system. The suggestion unit suggests appropriate hospitals and medical departments based on the match rate generated by the generation unit. The suggestion unit suggests the nearest medical institution based on, for example, the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through the smartphone app and suggests an appropriate medical institution. As a result, the health management system according to the embodiment collects and analyzes the user's health data, generates a disease match rate, and suggests appropriate medical institutions, enabling the user to receive appropriate medical care early.

[0068] The collection unit may collect data by a method including, but not limited to, manual input by a user and a method using a wearable device. For example, the collection unit may manually input data using a keyboard or a touchscreen. The collection unit may also collect data using a wearable device such as a smartwatch or fitness tracker. For example, the collection unit may use a smartwatch to collect data such as heart rate, step count, and sleep patterns. The collection unit may also use a fitness tracker to collect data such as exercise volume and calorie consumption. This increases the flexibility of data collection by including both manual input by a user and a method using a wearable device. Some or all of the above-described processing by the collection unit may be performed using, for example, AI or without AI. For example, the collection unit may input data acquired from the wearable device into AI and have the AI ​​analyze and filter the data.

[0069] The analysis unit uses a machine learning algorithm, including, but not limited to, a decision tree or a neural network. The analysis unit, for example, uses a decision tree to classify data and calculate a disease match rate. For example, the analysis unit uses a decision tree to classify the user's health data and evaluate the risk of disease. The analysis unit can also analyze data and calculate a disease match rate using a neural network. For example, the analysis unit can analyze the user's health data using a neural network to predict the probability of disease occurrence. The analysis unit can also analyze data using statistical analysis. For example, the analysis unit can analyze the user's health data using statistical analysis to evaluate the risk of disease. By using a machine learning algorithm and including a decision tree or a neural network, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's health data into AI and have the AI ​​analyze the data.

[0070] The suggestion unit can suggest the nearest medical institution based on the user's location information and notify the user using a smartphone app. The suggestion unit, for example, suggests the nearest medical institution based on the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through a smartphone app and suggests an appropriate medical institution. By suggesting the nearest medical institution based on the user's location information and notifying the user using the smartphone app, the user can quickly access an appropriate medical institution. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's location information into AI and have the AI ​​select the most appropriate medical institution.

[0071] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can collect detailed data to acquire more information. Furthermore, if the user is in a hurry, the collection unit can collect simplified data to quickly acquire the necessary information. This reduces the user's burden and enables more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The collection unit can select a data collection method based on the user's past health checkup results. For example, the collection unit can collect data focusing on specific items from the user's past health checkup results. The collection unit can also adjust the frequency of required data collection based on the user's past health checkup results. The collection unit can also add specific test items by referring to the user's past health checkup results. This allows the user's past health checkup results to be analyzed to select the optimal data collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health checkup results into AI and have the AI ​​select the optimal data collection method.

[0073] When collecting data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is currently on a diet, the collection unit can prioritize collecting data related to diet and exercise. Furthermore, if the user is interested in a particular disease, the collection unit can also collect data related to that disease. Furthermore, the collection unit can adjust the frequency and content of data collection according to the user's lifestyle (e.g., how busy they are at work). This allows for more relevant data to be collected by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0074] When collecting data, the collection unit can select a collection means selected according to the user's input method. For example, if the user prefers voice input, the collection unit can prioritize voice data collection. Furthermore, if the user prefers text input, the collection unit can also prioritize text data collection. Furthermore, if the user prefers image input, the collection unit can also prioritize image data collection. This improves user convenience by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's input method into AI and have the AI ​​select the optimal collection means.

[0075] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress. Furthermore, if the user is relaxed, the collection unit can also collect data related to the user's overall health condition. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. By determining the priority of data based on the user's emotions, more important data can be collected preferentially. 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit collects data related to health risks specific to high altitudes. Furthermore, if the user lives in an urban area, the collection unit can collect data related to health risks specific to urban areas. Furthermore, if the user lives overseas, the collection unit can collect data related to health risks specific to that area. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0077] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, if the user posts about health on social media, the collection unit can collect data based on the content of the posts. Furthermore, if the user expresses interest in a particular disease on social media, the collection unit can collect data related to the disease. Furthermore, the collection unit can collect data about lifestyle habits from the user's social media activity. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data about the user's social media activity into AI and have the AI ​​collect related data.

[0078] The collection unit can customize the collection method based on the user's past feedback when collecting data. For example, the collection unit can adjust the frequency of data collection based on the user's past feedback. The collection unit can also adjust the type of data to collect based on the user's past feedback. The collection unit can also adjust the collection method (audio, text, image, etc.) based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the collection method.

[0079] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to blood test data. The analysis unit can also apply a different analysis algorithm to lifestyle habit data. The analysis unit can also apply yet another analysis algorithm to data regarding the location and severity of pain. In this way, by applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the data category into AI and have the AI ​​select the optimal analysis algorithm.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the length of the analysis based on the user's emotions, an analysis result of an appropriate length for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the relevance of the data to AI and have the AI ​​adjust the order of analysis.

[0086] During analysis, the analysis unit can adjust the use of technical terms in the analysis to suit the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented depending on the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis depending on the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the user's level of expertise into AI and have the AI ​​adjust the way in which the analysis results are presented.

[0087] The generation unit can estimate the user's emotions and adjust the match rate generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed match rate. If the user is in a hurry, the generation unit can also generate a concise match rate. If the user is excited, the generation unit can also generate a visually easy-to-understand match rate. By adjusting the match rate generation method based on the user's emotions, a match rate that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] When generating a match rate, the generation unit can improve the accuracy of the generation based on the interrelationships of data. The generation unit, for example, combines blood test results with lifestyle habit data to generate a match rate. The generation unit can also combine the location and severity of pain with past health checkup results to generate a match rate. The generation unit can also combine skin roughness and hair loss status with dietary data to generate a match rate. This improves the accuracy of generating a match rate by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information regarding the interrelationships of data into AI and have the AI ​​generate a match rate.

[0089] When generating a match rate, the generation unit can take into consideration attribute information of the data submitter. The generation unit generates the match rate by taking into consideration, for example, the user's age and gender. The generation unit can also generate the match rate by taking into consideration the user's occupation and living environment. The generation unit can also generate the match rate by taking into consideration the user's medical history and family history. In this way, by taking into consideration the attribute information of the data submitter, a more personalized match rate can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's attribute information into AI and have the AI ​​generate the match rate.

[0090] When generating a match rate, the generation unit can generate weights based on the frequency of data submission. For example, the generation unit can assign a higher weight to data that is frequently submitted. The generation unit can also assign a lower weight to data that is submitted less frequently. The generation unit can also adjust the weighting of the match rate based on the submission frequency. In this way, by assigning weights based on the frequency of data submission, a more reliable match rate can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information regarding the frequency of data submission into AI and have the AI ​​adjust the weighting.

[0091] The generation unit can estimate the user's emotions and adjust the display method of the generated match rate based on the estimated user emotions. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. By adjusting the display method of the match rate based on the user's emotions, a display that is easy for the user to understand 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0092] When generating a match rate, the generation unit can generate the match rate based on the geographic distribution of data. For example, the generation unit can generate the match rate by taking into account data related to diseases specific to the area where the user lives. The generation unit can also generate the match rate by taking into account health risks at the user's travel destinations. The generation unit can also generate a match rate for related diseases based on the user's geographic movement history. In this way, by taking into account the geographic distribution of data, a match rate that reflects health risks specific to the area can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information about the user's geographic distribution into AI and have the AI ​​generate the match rate.

[0093] When generating a match rate, the generation unit can improve the accuracy of the generation by referring to literature related to the data. The generation unit, for example, generates a match rate by referring to the latest medical papers. The generation unit can also generate a match rate based on past research data. The generation unit can also improve the accuracy of the match rate by referring to medical evidence. In this way, by referring to related literature, the accuracy of the generated match rate is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about related literature into AI and have the AI ​​generate the match rate.

[0094] When generating a match rate, the generation unit can generate the match rate based on the market value of the data. For example, the generation unit can assign a higher weight to data with a high market value. The generation unit can also assign a lower weight to data with a low market value. The generation unit can also adjust the weighting of the match rate based on the market value of the data. In this way, by taking the market value of the data into consideration, a match rate that reflects more valuable data can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input information regarding the market value of the data into AI and have the AI ​​adjust the weighting.

[0095] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. By adjusting the way the suggestions are expressed based on the user's emotions, the suggestions can be easily understood by the user. 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The suggestion unit can adjust the level of detail of the proposal based on the importance of the disease when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an important disease. The suggestion unit can also make a simplified suggestion for an illness with a low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the disease. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the disease. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the importance of the disease to AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0097] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, the suggestion unit can apply a specific suggestion algorithm to internal diseases. The suggestion unit can also apply a different suggestion algorithm to dermatological diseases. The suggestion unit can also apply yet another suggestion algorithm to psychiatric diseases. In this way, by applying different suggestion algorithms depending on the disease category, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the disease category into AI and cause the AI ​​to select the optimal suggestion algorithm.

[0098] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion result based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. The suggestion unit can also provide concise suggestions if the user is in a hurry. By adjusting the length of the suggestions based on the user's emotions, it is possible to provide suggestions of an appropriate length for the user. 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] When making a proposal, the suggestion unit can determine the priority of the proposals based on the time when the illness was submitted. For example, the suggestion unit can prioritize proposals for recently discovered illnesses. The suggestion unit can also postpone proposals for illnesses that were submitted earlier. The suggestion unit can also adjust the order of proposals based on the time when the illness was submitted. This enables efficient proposals by determining the priority of proposals based on the time when the illness was submitted. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information regarding the time when the illness was submitted into AI and have the AI ​​determine the priority of the proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of the diseases when making suggestions. For example, the suggestion unit prioritizes suggestions of highly relevant diseases. The suggestion unit can also postpone suggestions of less relevant diseases. The suggestion unit can also determine the order of suggestions based on the relevance of the diseases. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the diseases. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information on the relevance of diseases to AI and cause the AI ​​to adjust the order of suggestions.

[0102] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal to match the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terms. If the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion to be easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information regarding the user's level of expertise into AI and cause the AI ​​to adjust the way the proposal is expressed. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits using the camera 42 and microphone 38B of the smart device 14. The collection unit can also accept data manually entered by the user via the control unit 46A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a disease match rate based on the analyzed data. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests appropriate hospitals and medical departments based on the generated match rate. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also accept data manually input by the user via the control unit 46A of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a disease match rate based on the analyzed data. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests appropriate hospitals and medical departments based on the generated match rate. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also accept data manually entered by the user via the control unit 46A of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a disease match rate based on the analyzed data. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and suggests appropriate hospitals and medical departments based on the generated match rate. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits using the camera 42 and microphone 238 of the robot 414. The collection unit can also accept data manually input by the user via the control unit 46A of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a disease match rate based on the analyzed data. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests appropriate hospitals and medical departments based on the generated match rate.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Also, if the user is relaxed, detailed data collection can be performed to acquire more information. Furthermore, if the user is in a hurry, simplified data collection can be performed to quickly acquire the necessary information. This reduces the user's burden and enables more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0105] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] The generation unit can estimate the user's emotions and adjust the match rate generation method based on the estimated user emotions. For example, if the user is relaxed, a detailed match rate can be generated. If the user is in a hurry, a concise match rate can be generated. Furthermore, if the user is excited, a visually easy-to-understand match rate can be generated. By adjusting the match rate generation method based on the user's emotions, a match rate that is easy for the user to understand 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 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0107] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that are concise. This allows the suggestion unit to adjust the way suggestions are presented based on the user's emotions, making the suggestion easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 suggestion unit can be performed using AI, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0108] The collection unit can select a data collection method based on the user's past health checkup results. For example, the collection unit can collect data with an emphasis on specific items based on the user's past health checkup results. The collection unit can also adjust the frequency of required data collection based on the user's past health checkup results. Furthermore, the collection unit can add specific test items by referring to the user's past health checkup results. This allows the collection unit to select the optimal data collection method and efficiently collect data by analyzing the user's past health checkup results. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health checkup results into AI and have the AI ​​select the optimal data collection method.

[0109] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the analysis priority can be determined according to the importance of the data. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information regarding the importance of the data to AI and have the AI ​​adjust the level of analysis detail.

[0110] When generating a match rate, the generation unit can improve the accuracy of the generation based on the interrelationships of data. For example, the match rate can be generated by combining blood test results with lifestyle habit data. The match rate can also be generated by combining the location and severity of pain with past health checkup results. Furthermore, the match rate can be generated by combining skin roughness and hair loss status with dietary data. This improves the accuracy of generating a match rate by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information regarding the interrelationships of data into AI and have the AI ​​generate the match rate.

[0111] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the disease. For example, a detailed proposal can be made for an important disease. A simplified proposal can also be made for an illness of low importance. Furthermore, the priority of the proposal can be determined according to the importance of the disease. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the disease. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the importance of the disease into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0112] When collecting data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is currently on a diet, data related to diet and exercise can be collected preferentially. Also, if the user is interested in a particular illness, data related to that illness can be collected. Furthermore, the frequency and content of data collection can be adjusted depending on the user's lifestyle (such as how busy they are at work). This allows more relevant data to be collected by filtering data based on the user's lifestyle and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0113] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, a specific suggestion algorithm can be applied to internal medicine diseases. A different suggestion algorithm can be applied to dermatology diseases. Furthermore, yet another suggestion algorithm can be applied to psychiatric diseases. By applying different suggestion algorithms depending on the disease category, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the disease category into AI and cause the AI ​​to select the optimal suggestion algorithm.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects data such as the user's health checkup results, physical condition, and daily lifestyle habits. For example, the collection unit collects data such as blood test results, weight, height, blood pressure, pain location and severity, skin roughness and hair loss, and diet and exercise habits. The collection unit can collect data by manually inputting the data by the user or by using a wearable device. For example, the collection unit can collect data by manually inputting the data using a keyboard or touch screen. The collection unit can also collect data using a wearable device such as a smartwatch or fitness tracker. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms, including decision trees and neural networks. For example, the analysis unit classifies the data using a decision tree and calculates a disease match rate. The analysis unit can also analyze the data using a neural network and calculate a disease match rate. The analysis unit can also analyze the data using statistical analysis. Step 3: The generation unit generates a disease match rate based on the data analyzed by the analysis unit. The generation unit calculates the match rate using, for example, a probabilistic method or a scoring system. For example, the generation unit calculates the probability of occurrence of the disease using a probabilistic method and generates the match rate. The generation unit can also calculate the disease match rate using a scoring system. Step 4: The suggestion unit suggests appropriate hospitals and medical departments based on the match rates generated by the generation unit. The suggestion unit suggests the nearest medical institution based on, for example, the user's location information. For example, the suggestion unit identifies the user's location using GPS data or Wi-Fi location information and suggests the nearest medical institution. The suggestion unit can also notify the user using a smartphone app. For example, the suggestion unit notifies the user through a smartphone app and suggests an appropriate medical institution.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[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 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.

[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 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.

[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 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.

[0167] 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.

[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] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a disease match rate based on the data analyzed by the analysis unit; a suggestion unit that suggests hospitals and medical departments based on the match rate generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit This includes, but is not limited to, manual user input and wearable device usage.

2. The system of claim 1.

3. The analysis unit Uses machine learning algorithms, including but not limited to decision trees and neural networks 2. The system of claim 1.

4. The proposal unit The system suggests the nearest medical facility based on the user's location and notifies them via a smartphone app.

2. The system of claim 1.

5. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

6. The collecting unit Select a data collection method based on the user's past health checkup results.

2. The system of claim 1.

7. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

8. The collecting unit When collecting data, select the collection method selected according to the user's input method.

2. The system of claim 1.

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