system

Abstract

The system according to this embodiment aims to predict the risk of developing a disease based on lifestyle habits and changes in physical condition, and to present this to the user. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit receives input about lifestyle habits and changes in physical condition. The analysis unit analyzes the data entered by the reception unit and predicts diseases that the user may contract. The presentation unit presents the probability of the disease predicted by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the risk of getting sick based on changes in lifestyle and physical condition has not been sufficiently predicted, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the risk of getting sick based on changes in lifestyle and physical condition and present it to the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a presentation unit. The reception unit inputs changes in lifestyle and physical condition. The analysis unit analyzes the data input by the reception unit and predicts the diseases that may be suffered. The presentation unit presents the probabilities of the diseases predicted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the risk of developing a disease based on lifestyle habits and changes in physical condition, and present this to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disease prediction system according to an embodiment of the present invention is a system that uses a generating AI to input changes in lifestyle and physical condition, and predicts diseases that the user may currently or in the future, along with their probabilities. Specifically, it consists of the following steps. First, the user inputs changes in lifestyle and physical condition, sleep data, and dietary data into the generating AI. Next, the generating AI analyzes this data, predicts possible diseases, and presents their probabilities. This mechanism allows the user to know areas for improvement and precautions in their lifestyle, and to prevent diseases before they occur. For example, the user inputs recent sleep duration, diet, exercise levels, etc. This information is input into the generating AI. Next, the generating AI analyzes the input information. Based on changes in lifestyle and physical condition, sleep data, and dietary data, the generating AI predicts diseases that the user may contract. For example, the generating AI analyzes the user's lack of sleep and irregular eating habits and identifies diseases that the user may contract in the future based on this. The generating AI presents the probabilities of the predicted diseases. For example, if the generating AI analyzes the user's data and predicts a 70% risk of subarachnoid hemorrhage, it presents that probability to the user. This allows users to understand their own health status and take necessary measures. This system enables users to learn about areas for improvement and precautions in their lifestyle, thus preventing illness. For example, if the generating AI analyzes a user's data and determines that sleep deprivation is the cause, the user can take measures such as increasing their sleep time. Similarly, if the generating AI analyzes a user's data and determines that dietary improvements are needed, the user can strive for a balanced diet. Furthermore, this system can also be used as an employee benefit service by companies. For example, companies can collect employee health data and analyze it using the generating AI to understand their health status and take necessary measures. This is expected to contribute to maintaining employee health and reducing medical expenses. Thus, disease prediction tools using generating AI contribute to maintaining user health and reducing medical costs. They can also be used as employee benefit services by companies, contributing to maintaining employee health and reducing medical expenses.This allows disease prediction systems to contribute to maintaining users' health and reducing healthcare costs.

[0029] The disease prediction system according to this embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit receives input on lifestyle habits and changes in physical condition. Lifestyle habits include, but are not limited to, exercise habits, eating habits, and sleep habits. Changes in physical condition include, but are not limited to, changes in body temperature, weight, and fatigue. The reception unit allows users to input, for example, their recent sleep duration, diet, and exercise level. The reception unit can also receive data using a smartphone application. For example, a smartphone application allows users to easily input data. The analysis unit uses a generative AI to analyze the data entered by the reception unit and predict diseases that the user may contract. The generative AI is implemented using, for example, a deep learning model or a natural language processing model. Based on lifestyle habits, changes in physical condition, sleep data, and eating data, the generative AI predicts diseases that the user may contract. For example, the generative AI analyzes the user's sleep deprivation and irregular eating habits and identifies diseases that the user may contract in the future based on this. The generating AI can analyze user data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. The presentation unit displays the probability of the disease predicted by the analysis unit. For example, the presentation unit presents the user with the probability of the disease predicted by the generating AI. This allows the user to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle. For example, if the generating AI determines that sleep deprivation is the cause of the disease after analyzing the user's data, the user can take measures such as increasing their sleep time. Also, if the generating AI determines that dietary improvements are necessary after analyzing the user's data, the user can strive to eat a balanced diet. In this way, the disease prediction system according to this embodiment can contribute to maintaining the user's health and reducing medical expenses.

[0030] The reception desk receives input about lifestyle habits and changes in physical condition. Lifestyle habits include, but are not limited to, exercise habits, eating habits, and sleep habits. Specifically, users can input details such as daily exercise volume and type, diet content and calorie intake, sleep duration and quality. Changes in physical condition include, but are not limited to, changes in body temperature, weight changes, and fatigue levels. Users record daily fluctuations in body temperature and weight, fatigue levels, and stress levels, and input this data into the reception desk. The reception desk can, for example, allow users to input recent sleep duration, diet content, and exercise volume. The reception desk can also input data using a smartphone app. For example, the smartphone app allows users to easily input data. The smartphone app has an intuitive and user-friendly interface, allowing users to easily input data and review past data. Furthermore, the smartphone app can automatically collect data by linking with wearable devices. For example, smartwatches and fitness trackers can measure the user's heart rate, steps, and calories burned, and automatically send this data to the reception desk. This allows users to collect detailed data without hassle and accurately understand their health status. The reception department centrally manages this data and prepares it for provision to the analysis department.

[0031] The analysis unit uses generative AI to analyze data entered by the reception unit and predict diseases that the user may be susceptible to. Generative AI is implemented using, for example, deep learning models and natural language processing models. Specifically, the deep learning model receives data on the user's lifestyle, changes in physical condition, sleep data, and dietary data as input, and analyzes this data using a multi-layered neural network. Based on lifestyle, changes in physical condition, sleep data, and dietary data, the generative AI predicts diseases that the user may be susceptible to. For example, the generative AI analyzes the user's lack of sleep and irregular eating habits and identifies diseases that the user may be susceptible to in the future based on this. The generative AI can also analyze the user's data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. The generative AI learns from past data and statistical information and compares it with the user's data to predict disease risk with high accuracy. For example, the generative AI evaluates the risk of the current user contracting a similar disease based on data from users who have shown similar lifestyle and physical condition changes in the past. Furthermore, the generating AI can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis unit to monitor the user's health status in real time, detect anomalies early, and take appropriate measures. In addition, the analysis unit can continuously learn from user data and improve its prediction accuracy.

[0032] The presentation unit displays the probability of disease predicted by the analysis unit. For example, the presentation unit presents the probability of disease predicted by the generation AI to the user. Specifically, it visually displays the predicted disease risk through the user interface. For example, it uses graphs and charts to allow users to intuitively understand how high the risk of disease is. It can also provide detailed explanations and preventive measures for high-risk diseases. This allows users to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle. For example, if the generation AI determines that sleep deprivation is the cause after analyzing the user's data, the user can take measures such as increasing their sleep time. Also, if the generation AI determines that dietary improvements are necessary after analyzing the user's data, the user can strive for a balanced diet. The presentation unit proposes specific action plans and goal setting to support users in taking actual action. For example, it provides a function to monitor progress toward goals set by the user and provide feedback on the degree of achievement. This allows users to maintain motivation and achieve a healthy lifestyle. Furthermore, the display unit can collect user feedback and continuously improve the system's accuracy and ease of use. This allows the display unit to provide users with useful information and contribute to maintaining and improving their health.

[0033] The reception desk can input lifestyle habits, changes in physical condition, sleep data, and dietary data. For example, the reception desk can allow users to input their recent sleep duration, diet, and exercise levels. The reception desk can also input data using a smartphone app. For example, using a smartphone app allows users to easily input data. This makes it possible to predict illnesses more accurately by inputting data based on the user's lifestyle habits and changes in physical condition. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle habits and changes in physical condition into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can use generative AI to analyze lifestyle habits, changes in physical condition, sleep data, and dietary data to predict diseases that a user may be susceptible to. For example, the analysis unit can use generative AI to analyze a user's sleep deprivation and irregular eating habits and, based on this, identify diseases that the user may be susceptible to in the future. The analysis unit can also use generative AI to analyze the user's data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. Thus, by using generative AI, it becomes possible to predict diseases based on lifestyle habits and changes in physical condition. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input data on the user's lifestyle habits and changes in physical condition into the generative AI and have the generative AI perform disease prediction.

[0035] The analysis unit can use a generative AI to analyze user data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. For example, the analysis unit can use the generative AI to analyze user data and predict a 70% risk of subarachnoid hemorrhage. The analysis unit can also use the generative AI to analyze user data and predict a 50% risk of acute glaucoma. This makes it possible to predict specific diseases using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may be performed without it. For example, the analysis unit can input user data into the generative AI and have the generative AI perform the prediction of a specific disease.

[0036] The presentation unit can present the user with the probability of a disease predicted by the generating AI. For example, if the generating AI analyzes the user's data and predicts a 70% risk of subarachnoid hemorrhage, the presentation unit will present that probability to the user. Similarly, if the generating AI analyzes the user's data and predicts a 50% risk of acute glaucoma, the presentation unit can also present that probability to the user. This allows the user to understand their own health status and take necessary measures by presenting the probability of a disease predicted by the generating AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can provide an interface for presenting the user with the probability of a disease predicted by the generating AI.

[0037] The presentation unit can present the user with areas for improvement and points to be aware of in their lifestyle. For example, if the generation AI analyzes the user's data and determines that sleep deprivation is the cause, the user can take measures such as increasing their sleep time. Also, if the generation AI analyzes the user's data and determines that dietary improvements are necessary, the user can strive for a balanced diet. In this way, by knowing areas for improvement and points to be aware of in their lifestyle, the user can prevent illness. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can present the user with areas for improvement and points to be aware of in their lifestyle based on the results of analysis by the generation AI.

[0038] The reception desk can input data using a smartphone app. For example, the reception desk allows users to easily input data using a smartphone app. The smartphone app includes, for example, a data input interface and notification functions. This allows users to easily input data using a smartphone app. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input data entered using the smartphone app into a generating AI and have the generating AI perform data analysis.

[0039] The analysis unit can analyze employee health data and predict the health status of employees. For example, the analysis unit can collect employee health data from a company, analyze it using a generative AI, understand the health status of employees, and take necessary measures. This allows the company to understand the health status of its employees and take necessary measures. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis unit can input employee health data from a company into a generative AI and have the generative AI perform a prediction of the employee's health status.

[0040] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the optimal input timing. Furthermore, the reception desk can automatically suggest data that is often entered during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0041] The reception desk can filter data entry based on the user's current health status and lifestyle. For example, if the user is tired, the reception desk can simplify the input process by reducing the number of input fields. Conversely, if the user is in good health, the reception desk can prompt for more detailed data entry. Furthermore, if the user has a specific illness, the reception desk can prioritize data entry related to that illness. This allows for more appropriate data entry by filtering data based on the user's current health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data about the user's current health status and lifestyle into a generating AI and have the generating AI perform the data entry filtering.

[0042] The reception desk can prioritize inputting highly relevant data based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception desk will prioritize inputting health data related to that region. Furthermore, if the user is traveling, the reception desk can input data related to health risks in the travel destination. Additionally, if the user is at home, the reception desk can prioritize inputting data related to daily life. This allows for more appropriate data entry by prioritizing highly relevant data based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI input highly relevant data.

[0043] The reception desk can analyze a user's social media activity and input relevant data during data entry. For example, if a user has made health-related posts on social media, the reception desk can prompt data entry based on that content. The reception desk can also input data related to events if a user has participated in specific events on social media. Furthermore, if a user has shared a particular meal on social media, the reception desk can input data related to that meal. This allows for the input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data that has a significant impact on the user's health. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. It can also apply a nutrition analysis algorithm to food and drink data. Furthermore, it can apply a health risk analysis algorithm to health condition change data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also prioritize the analysis of data submitted regularly. Furthermore, the analysis unit may prioritize the analysis of data submitted by users after a specific event. By prioritizing analysis based on the data submission timing, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission timing into the generative AI and have the generative AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit may prioritize the analysis of data that has a significant impact on the user's health. By adjusting the order of analysis based on the relevance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0048] The presentation unit can adjust the level of detail in the presentation based on the predicted severity of the disease. For example, the presentation unit provides detailed information for serious diseases. It can also provide simplified information for common diseases. Furthermore, it can provide particularly detailed information for diseases that significantly affect the user's health. This allows for more appropriate information to be provided by adjusting the level of detail in the presentation based on the predicted severity of the disease. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the predicted severity of the disease into a generating AI and have the generating AI perform the adjustment of the level of detail in the presentation.

[0049] The presentation unit can apply different presentation algorithms depending on the predicted disease category at the time of presentation. For example, for heart disease, the presentation unit can apply a presentation algorithm specifically for heart disease. Similarly, for diabetes, it can apply a presentation algorithm specifically for diabetes. Furthermore, for cancer, it can apply a presentation algorithm specifically for cancer. This allows for the provision of more appropriate information by applying different presentation algorithms depending on the predicted disease category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the predicted disease category into a generating AI and have the generating AI apply different presentation algorithms.

[0050] The presentation unit can determine the priority of presentations based on the timing of predicted disease submissions. For example, the presentation unit may prioritize presenting recently predicted diseases. It can also prioritize presenting diseases that are predicted regularly. Furthermore, the presentation unit may prioritize presenting diseases predicted after a specific event the user has experienced. This allows for the provision of more appropriate information by prioritizing presentations based on the timing of predicted disease submissions. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the timing of predicted disease submissions into a generating AI and have the generating AI determine the presentation priority.

[0051] The presentation unit can adjust the order of presentation based on the relevance of predicted diseases. For example, the presentation unit can prioritize presenting diseases with high relevance. It can also postpone presenting diseases with low relevance. Furthermore, the presentation unit can prioritize presenting diseases that have a significant impact on the user's health. This allows for the provision of more appropriate information by adjusting the order of presentation based on the relevance of predicted diseases. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the relevance of predicted diseases into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The reception unit allows users to input their genetic information in addition to changes in their lifestyle and physical condition. For example, users can input their genetic risk of certain diseases and health risks based on their family history. The analysis unit uses this data to analyze the impact of the user's genetic information on their health and predict diseases they may be susceptible to. For example, it can predict the risk of diabetes for a user who is genetically at high risk. The presentation unit can provide users with methods for health management and preventive measures based on their genetic information, based on the analysis results. This allows users to understand their own genetic information and take necessary measures.

[0054] The analysis unit can analyze the user's environmental data in addition to changes in the user's lifestyle and physical condition. For example, it can analyze environmental data such as air quality, water quality, and noise levels in the area where the user lives. This allows the system to analyze the impact of the user's environment on their health and predict potential illnesses. For instance, it can predict the risk of respiratory illnesses for users living in areas with poor air quality. Based on the analysis results, the presentation unit can provide the user with advice on how to improve their environment and mitigate health risks. This allows the user to understand their environment and take necessary measures.

[0055] The analysis unit can analyze users' occupational data in addition to changes in their lifestyle and physical condition. For example, it can analyze data such as the user's occupation, working hours, and work environment. This allows the system to analyze the impact of the user's occupation on their health and predict potential illnesses. For instance, it can analyze the health risks of long working hours and predict the risk of overwork and stress-related illnesses for users who work long hours. Based on the analysis results, the presentation unit can provide users with advice on how to improve their work environment and maintain their health. This allows users to understand their own occupational data and take necessary measures.

[0056] The reception unit allows users to input their exercise data in addition to changes in their lifestyle and physical condition. For example, it can input data such as the type, frequency, and intensity of exercise they perform. Based on this data, the analysis unit can analyze the impact of the user's exercise habits on their health and predict potential illnesses. For example, it can analyze the health risks associated with inactivity and predict the risk of obesity and diabetes for users who are sedentary. Based on the analysis results, the presentation unit can provide users with methods to improve their exercise habits and exercise plans to maintain their health. This allows users to understand their own exercise data and take necessary measures.

[0057] The analysis unit can analyze the user's dietary data in addition to changes in the user's lifestyle and physical condition. For example, it can analyze data such as the type of food the user consumes, the nutrients they consume, and the frequency of meals. This allows the system to analyze the impact of the user's dietary habits on their health and predict potential illnesses. For instance, it can analyze the health risks associated with a poorly balanced diet and predict the risk of nutritional deficiencies and lifestyle-related diseases for users who have such diets. Based on the analysis results, the presentation unit can provide the user with methods for improving their dietary habits and meal plans to maintain their health. This allows the user to understand their own dietary data and take necessary measures.

[0058] The reception unit allows users to input their sleep data in addition to changes in their lifestyle and physical condition. For example, it can input data such as the user's sleep duration, sleep quality, and sleep patterns. Based on this data, the analysis unit can analyze the impact of the user's sleep habits on their health and predict potential illnesses. For example, it can analyze the health risks of sleep deprivation and predict the risk of depression and heart disease for sleep-deprived users. Based on the analysis results, the presentation unit can provide users with methods to improve their sleep habits and sleep plans to maintain their health. This allows users to understand their own sleep data and take necessary measures.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk inputs changes in lifestyle and physical condition. Lifestyle includes exercise habits, eating habits, and sleep habits, while changes in physical condition include changes in body temperature, weight, and fatigue levels. Users can input recent sleep duration, diet, exercise levels, etc., using a smartphone app. Step 2: The analysis unit uses generative AI to analyze the data entered by the reception unit and predict potential illnesses. The generative AI is implemented using deep learning models and natural language processing models, and predicts illnesses based on lifestyle habits, changes in physical condition, sleep data, and dietary data. Step 3: The presentation unit displays the probability of disease predicted by the analysis unit. This allows the user to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle.

[0061] (Example of form 2) The disease prediction system according to an embodiment of the present invention is a system that uses a generating AI to input changes in lifestyle and physical condition, and predicts diseases that the user may currently or in the future, along with their probabilities. Specifically, it consists of the following steps. First, the user inputs changes in lifestyle and physical condition, sleep data, and dietary data into the generating AI. Next, the generating AI analyzes this data, predicts possible diseases, and presents their probabilities. This mechanism allows the user to know areas for improvement and precautions in their lifestyle, and to prevent diseases before they occur. For example, the user inputs recent sleep duration, diet, exercise levels, etc. This information is input into the generating AI. Next, the generating AI analyzes the input information. Based on changes in lifestyle and physical condition, sleep data, and dietary data, the generating AI predicts diseases that the user may contract. For example, the generating AI analyzes the user's lack of sleep and irregular eating habits and identifies diseases that the user may contract in the future based on this. The generating AI presents the probabilities of the predicted diseases. For example, if the generating AI analyzes the user's data and predicts a 70% risk of subarachnoid hemorrhage, it presents that probability to the user. This allows users to understand their own health status and take necessary measures. This system enables users to learn about areas for improvement and precautions in their lifestyle, thus preventing illness. For example, if the generating AI analyzes a user's data and determines that sleep deprivation is the cause, the user can take measures such as increasing their sleep time. Similarly, if the generating AI analyzes a user's data and determines that dietary improvements are needed, the user can strive for a balanced diet. Furthermore, this system can also be used as an employee benefit service by companies. For example, companies can collect employee health data and analyze it using the generating AI to understand their health status and take necessary measures. This is expected to contribute to maintaining employee health and reducing medical expenses. Thus, disease prediction tools using generating AI contribute to maintaining user health and reducing medical costs. They can also be used as employee benefit services by companies, contributing to maintaining employee health and reducing medical expenses.This allows disease prediction systems to contribute to maintaining users' health and reducing healthcare costs.

[0062] The disease prediction system according to this embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit receives input on lifestyle habits and changes in physical condition. Lifestyle habits include, but are not limited to, exercise habits, eating habits, and sleep habits. Changes in physical condition include, but are not limited to, changes in body temperature, weight, and fatigue. The reception unit allows users to input, for example, their recent sleep duration, diet, and exercise level. The reception unit can also receive data using a smartphone application. For example, a smartphone application allows users to easily input data. The analysis unit uses a generative AI to analyze the data entered by the reception unit and predict diseases that the user may contract. The generative AI is implemented using, for example, a deep learning model or a natural language processing model. Based on lifestyle habits, changes in physical condition, sleep data, and eating data, the generative AI predicts diseases that the user may contract. For example, the generative AI analyzes the user's sleep deprivation and irregular eating habits and identifies diseases that the user may contract in the future based on this. The generating AI can analyze user data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. The presentation unit displays the probability of the disease predicted by the analysis unit. For example, the presentation unit presents the user with the probability of the disease predicted by the generating AI. This allows the user to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle. For example, if the generating AI determines that sleep deprivation is the cause of the disease after analyzing the user's data, the user can take measures such as increasing their sleep time. Also, if the generating AI determines that dietary improvements are necessary after analyzing the user's data, the user can strive to eat a balanced diet. In this way, the disease prediction system according to this embodiment can contribute to maintaining the user's health and reducing medical expenses.

[0063] The reception desk receives input about lifestyle habits and changes in physical condition. Lifestyle habits include, but are not limited to, exercise habits, eating habits, and sleep habits. Specifically, users can input details such as daily exercise volume and type, diet content and calorie intake, sleep duration and quality. Changes in physical condition include, but are not limited to, changes in body temperature, weight changes, and fatigue levels. Users record daily fluctuations in body temperature and weight, fatigue levels, and stress levels, and input this data into the reception desk. The reception desk can, for example, allow users to input recent sleep duration, diet content, and exercise volume. The reception desk can also input data using a smartphone app. For example, the smartphone app allows users to easily input data. The smartphone app has an intuitive and user-friendly interface, allowing users to easily input data and review past data. Furthermore, the smartphone app can automatically collect data by linking with wearable devices. For example, smartwatches and fitness trackers can measure the user's heart rate, steps, and calories burned, and automatically send this data to the reception desk. This allows users to collect detailed data without hassle and accurately understand their health status. The reception department centrally manages this data and prepares it for provision to the analysis department.

[0064] The analysis unit uses generative AI to analyze data entered by the reception unit and predict diseases that the user may be susceptible to. Generative AI is implemented using, for example, deep learning models and natural language processing models. Specifically, the deep learning model receives data on the user's lifestyle, changes in physical condition, sleep data, and dietary data as input, and analyzes this data using a multi-layered neural network. Based on lifestyle, changes in physical condition, sleep data, and dietary data, the generative AI predicts diseases that the user may be susceptible to. For example, the generative AI analyzes the user's lack of sleep and irregular eating habits and identifies diseases that the user may be susceptible to in the future based on this. The generative AI can also analyze the user's data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. The generative AI learns from past data and statistical information and compares it with the user's data to predict disease risk with high accuracy. For example, the generative AI evaluates the risk of the current user contracting a similar disease based on data from users who have shown similar lifestyle and physical condition changes in the past. Furthermore, the generating AI can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis unit to monitor the user's health status in real time, detect anomalies early, and take appropriate measures. In addition, the analysis unit can continuously learn from user data and improve its prediction accuracy.

[0065] The presentation unit displays the probability of disease predicted by the analysis unit. For example, the presentation unit presents the probability of disease predicted by the generation AI to the user. Specifically, it visually displays the predicted disease risk through the user interface. For example, it uses graphs and charts to allow users to intuitively understand how high the risk of disease is. It can also provide detailed explanations and preventive measures for high-risk diseases. This allows users to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle. For example, if the generation AI determines that sleep deprivation is the cause after analyzing the user's data, the user can take measures such as increasing their sleep time. Also, if the generation AI determines that dietary improvements are necessary after analyzing the user's data, the user can strive for a balanced diet. The presentation unit proposes specific action plans and goal setting to support users in taking actual action. For example, it provides a function to monitor progress toward goals set by the user and provide feedback on the degree of achievement. This allows users to maintain motivation and achieve a healthy lifestyle. Furthermore, the display unit can collect user feedback and continuously improve the system's accuracy and ease of use. This allows the display unit to provide users with useful information and contribute to maintaining and improving their health.

[0066] The reception desk can input lifestyle habits, changes in physical condition, sleep data, and dietary data. For example, the reception desk can allow users to input their recent sleep duration, diet, and exercise levels. The reception desk can also input data using a smartphone app. For example, using a smartphone app allows users to easily input data. This makes it possible to predict illnesses more accurately by inputting data based on the user's lifestyle habits and changes in physical condition. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle habits and changes in physical condition into a generating AI and have the generating AI perform data analysis.

[0067] The analysis unit can use generative AI to analyze lifestyle habits, changes in physical condition, sleep data, and dietary data to predict diseases that a user may be susceptible to. For example, the analysis unit can use generative AI to analyze a user's sleep deprivation and irregular eating habits and, based on this, identify diseases that the user may be susceptible to in the future. The analysis unit can also use generative AI to analyze the user's data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. Thus, by using generative AI, it becomes possible to predict diseases based on lifestyle habits and changes in physical condition. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input data on the user's lifestyle habits and changes in physical condition into the generative AI and have the generative AI perform disease prediction.

[0068] The analysis unit can use a generative AI to analyze user data and predict diseases such as subarachnoid hemorrhage and acute glaucoma. For example, the analysis unit can use the generative AI to analyze user data and predict a 70% risk of subarachnoid hemorrhage. The analysis unit can also use the generative AI to analyze user data and predict a 50% risk of acute glaucoma. This makes it possible to predict specific diseases using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI, or they may be performed without it. For example, the analysis unit can input user data into the generative AI and have the generative AI perform the prediction of a specific disease.

[0069] The presentation unit can present the user with the probability of a disease predicted by the generating AI. For example, if the generating AI analyzes the user's data and predicts a 70% risk of subarachnoid hemorrhage, the presentation unit will present that probability to the user. Similarly, if the generating AI analyzes the user's data and predicts a 50% risk of acute glaucoma, the presentation unit can also present that probability to the user. This allows the user to understand their own health status and take necessary measures by presenting the probability of a disease predicted by the generating AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can provide an interface for presenting the user with the probability of a disease predicted by the generating AI.

[0070] The presentation unit can present the user with areas for improvement and points to be aware of in their lifestyle. For example, if the generation AI analyzes the user's data and determines that sleep deprivation is the cause, the user can take measures such as increasing their sleep time. Also, if the generation AI analyzes the user's data and determines that dietary improvements are necessary, the user can strive for a balanced diet. In this way, by knowing areas for improvement and points to be aware of in their lifestyle, the user can prevent illness. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can present the user with areas for improvement and points to be aware of in their lifestyle based on the results of analysis by the generation AI.

[0071] The reception desk can input data using a smartphone app. For example, the reception desk allows users to easily input data using a smartphone app. The smartphone app includes, for example, a data input interface and notification functions. This allows users to easily input data using a smartphone app. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input data entered using the smartphone app into a generating AI and have the generating AI perform data analysis.

[0072] The analysis unit can analyze employee health data and predict the health status of employees. For example, the analysis unit can collect employee health data from a company, analyze it using a generative AI, understand the health status of employees, and take necessary measures. This allows the company to understand the health status of its employees and take necessary measures. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis unit can input employee health data from a company into a generative AI and have the generative AI perform a prediction of the employee's health status.

[0073] The reception desk can estimate the user's emotions and adjust the timing of data entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can send a notification prompting them to enter data during a time when they can relax. The reception desk can also increase the frequency of notifications prompting data entry if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk can suggest postponing data entry. This allows for more appropriate data entry by adjusting the timing of data entry according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can analyze the user's past data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns in the data the user has entered in the past and suggest the optimal input timing. Furthermore, the reception desk can automatically suggest data that is often entered during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past data entry history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0075] The reception desk can filter data entry based on the user's current health status and lifestyle. For example, if the user is tired, the reception desk can simplify the input process by reducing the number of input fields. Conversely, if the user is in good health, the reception desk can prompt for more detailed data entry. Furthermore, if the user has a specific illness, the reception desk can prioritize data entry related to that illness. This allows for more appropriate data entry by filtering data based on the user's current health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data about the user's current health status and lifestyle into a generating AI and have the generating AI perform the data entry filtering.

[0076] The reception desk can estimate the user's emotions and prioritize the data to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize important data entry. If the user is relaxed, the reception desk may also encourage more detailed data entry. Furthermore, if the user is in a hurry, the reception desk may suggest minimal data entry. This allows for more appropriate data entry by prioritizing the data to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can prioritize inputting highly relevant data based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception desk will prioritize inputting health data related to that region. Furthermore, if the user is traveling, the reception desk can input data related to health risks in the travel destination. Additionally, if the user is at home, the reception desk can prioritize inputting data related to daily life. This allows for more appropriate data entry by prioritizing highly relevant data based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI input highly relevant data.

[0078] The reception desk can analyze a user's social media activity and input relevant data during data entry. For example, if a user has made health-related posts on social media, the reception desk can prompt data entry based on that content. The reception desk can also input data related to events if a user has participated in specific events on social media. Furthermore, if a user has shared a particular meal on social media, the reception desk can input data related to that meal. This allows for the input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data that has a significant impact on the user's health. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. It can also apply a nutrition analysis algorithm to food and drink data. Furthermore, it can apply a health risk analysis algorithm to health condition change data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, 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. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also prioritize the analysis of data submitted regularly. Furthermore, the analysis unit may prioritize the analysis of data submitted by users after a specific event. By prioritizing analysis based on the data submission timing, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission timing into the generative AI and have the generative AI determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit may prioritize the analysis of data that has a significant impact on the user's health. By adjusting the order of analysis based on the relevance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0085] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible presentation method. If the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise presentation method. By adjusting the presentation method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation method.

[0086] The presentation unit can adjust the level of detail in the presentation based on the predicted severity of the disease. For example, the presentation unit provides detailed information for serious diseases. It can also provide simplified information for common diseases. Furthermore, it can provide particularly detailed information for diseases that significantly affect the user's health. This allows for more appropriate information to be provided by adjusting the level of detail in the presentation based on the predicted severity of the disease. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the predicted severity of the disease into a generating AI and have the generating AI perform the adjustment of the level of detail in the presentation.

[0087] The presentation unit can apply different presentation algorithms depending on the predicted disease category at the time of presentation. For example, for heart disease, the presentation unit can apply a presentation algorithm specifically for heart disease. Similarly, for diabetes, it can apply a presentation algorithm specifically for diabetes. Furthermore, for cancer, it can apply a presentation algorithm specifically for cancer. This allows for the provision of more appropriate information by applying different presentation algorithms depending on the predicted disease category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the predicted disease category into a generating AI and have the generating AI apply different presentation algorithms.

[0088] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the presentation unit can provide a short, concise presentation. If the user is relaxed, the presentation unit can provide a longer presentation containing more detailed information. Furthermore, if the user is excited, the presentation unit can provide a visually stimulating presentation. By adjusting the length of the presentation according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the presentation.

[0089] The presentation unit can determine the priority of presentations based on the timing of predicted disease submissions. For example, the presentation unit may prioritize presenting recently predicted diseases. It can also prioritize presenting diseases that are predicted regularly. Furthermore, the presentation unit may prioritize presenting diseases predicted after a specific event the user has experienced. This allows for the provision of more appropriate information by prioritizing presentations based on the timing of predicted disease submissions. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the timing of predicted disease submissions into a generating AI and have the generating AI determine the presentation priority.

[0090] The presentation unit can adjust the order of presentation based on the relevance of predicted diseases. For example, the presentation unit can prioritize presenting diseases with high relevance. It can also postpone presenting diseases with low relevance. Furthermore, the presentation unit can prioritize presenting diseases that have a significant impact on the user's health. This allows for the provision of more appropriate information by adjusting the order of presentation based on the relevance of predicted diseases. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the relevance of predicted diseases into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The reception section allows users to input information about their lifestyle and physical condition, as well as their stress levels and psychological state. For example, users can input the degree of stress they experience on a daily basis and their psychological state (e.g., feelings of happiness or anxiety). The analysis section uses this data to analyze the impact of the user's psychological state on their health and predict potential illnesses. For example, it can predict the risk of stress-related illnesses (e.g., hypertension or heart disease) for users with high stress levels. The presentation section, based on the analysis results, can provide users with advice on stress management methods and maintaining psychological health. This allows users to understand their own psychological state and take necessary measures.

[0093] The reception unit allows users to input their genetic information in addition to changes in their lifestyle and physical condition. For example, users can input their genetic risk of certain diseases and health risks based on their family history. The analysis unit uses this data to analyze the impact of the user's genetic information on their health and predict diseases they may be susceptible to. For example, it can predict the risk of diabetes for a user who is genetically at high risk. The presentation unit can provide users with methods for health management and preventive measures based on their genetic information, based on the analysis results. This allows users to understand their own genetic information and take necessary measures.

[0094] The analysis unit can analyze the user's environmental data in addition to changes in the user's lifestyle and physical condition. For example, it can analyze environmental data such as air quality, water quality, and noise levels in the area where the user lives. This allows the system to analyze the impact of the user's environment on their health and predict potential illnesses. For instance, it can predict the risk of respiratory illnesses for users living in areas with poor air quality. Based on the analysis results, the presentation unit can provide the user with advice on how to improve their environment and mitigate health risks. This allows the user to understand their environment and take necessary measures.

[0095] The reception section allows users to input data on their lifestyle habits, changes in their physical condition, and their social activities. For example, they can input data on community activities they participate in and the frequency of their interactions with friends and family. Based on this data, the analysis section can analyze the impact of the user's social activities on their health and predict potential illnesses. For example, it can analyze the health risks of social isolation and predict the risk of depression and heart disease for isolated users. Based on the analysis results, the presentation section can provide users with advice on increasing their social activities and methods for participating in communities. This allows users to understand their own social activities and take necessary measures.

[0096] The analysis unit can analyze users' occupational data in addition to changes in their lifestyle and physical condition. For example, it can analyze data such as the user's occupation, working hours, and work environment. This allows the system to analyze the impact of the user's occupation on their health and predict potential illnesses. For instance, it can analyze the health risks of long working hours and predict the risk of overwork and stress-related illnesses for users who work long hours. Based on the analysis results, the presentation unit can provide users with advice on how to improve their work environment and maintain their health. This allows users to understand their own occupational data and take necessary measures.

[0097] The reception unit allows users to input data on their lifestyle, physical condition, and emotional state. For example, users can input emotions they experience on a daily basis (e.g., joy, sadness, anger). The analysis unit uses this data to analyze the impact of the user's emotions on their health and predict potential illnesses. For example, it can analyze the health risks associated with anger and stress and predict the risk of heart disease and high blood pressure for users who frequently experience these emotions. The presentation unit, based on the analysis results, can provide users with advice on how to manage their emotions and maintain their psychological health. This allows users to understand their own emotional data and take necessary measures.

[0098] The reception unit allows users to input their exercise data in addition to changes in their lifestyle and physical condition. For example, it can input data such as the type, frequency, and intensity of exercise they perform. Based on this data, the analysis unit can analyze the impact of the user's exercise habits on their health and predict potential illnesses. For example, it can analyze the health risks associated with inactivity and predict the risk of obesity and diabetes for users who are sedentary. Based on the analysis results, the presentation unit can provide users with methods to improve their exercise habits and exercise plans to maintain their health. This allows users to understand their own exercise data and take necessary measures.

[0099] The analysis unit can analyze the user's dietary data in addition to changes in the user's lifestyle and physical condition. For example, it can analyze data such as the type of food the user consumes, the nutrients they consume, and the frequency of meals. This allows the system to analyze the impact of the user's dietary habits on their health and predict potential illnesses. For instance, it can analyze the health risks associated with a poorly balanced diet and predict the risk of nutritional deficiencies and lifestyle-related diseases for users who have such diets. Based on the analysis results, the presentation unit can provide the user with methods for improving their dietary habits and meal plans to maintain their health. This allows the user to understand their own dietary data and take necessary measures.

[0100] The reception unit allows users to input their sleep data in addition to changes in their lifestyle and physical condition. For example, it can input data such as the user's sleep duration, sleep quality, and sleep patterns. Based on this data, the analysis unit can analyze the impact of the user's sleep habits on their health and predict potential illnesses. For example, it can analyze the health risks of sleep deprivation and predict the risk of depression and heart disease for sleep-deprived users. Based on the analysis results, the presentation unit can provide users with methods to improve their sleep habits and sleep plans to maintain their health. This allows users to understand their own sleep data and take necessary measures.

[0101] The analysis unit can analyze the user's emotional data in addition to changes in the user's lifestyle and physical condition. For example, it can analyze the emotions the user experiences on a daily basis (e.g., joy, sadness, anger, etc.). This allows for an analysis of the impact of the user's emotions on their health and the prediction of potential illnesses. For instance, it can analyze the health risks posed by anger and stress, and predict the risk of heart disease and high blood pressure for users who frequently experience these emotions. Based on the analysis results, the presentation unit can provide the user with advice on how to manage their emotions and maintain their psychological health. This allows the user to understand their own emotional data and take necessary measures.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk inputs changes in lifestyle and physical condition. Lifestyle includes exercise habits, eating habits, and sleep habits, while changes in physical condition include changes in body temperature, weight, and fatigue levels. Users can input recent sleep duration, diet, exercise levels, etc., using a smartphone app. Step 2: The analysis unit uses generative AI to analyze the data entered by the reception unit and predict potential illnesses. The generative AI is implemented using deep learning models and natural language processing models, and predicts illnesses based on lifestyle habits, changes in physical condition, sleep data, and dietary data. Step 3: The presentation unit displays the probability of disease predicted by the analysis unit. This allows the user to understand their own health status and take necessary measures. The presentation unit can also suggest areas for improvement and points to be aware of in the user's lifestyle.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the reception unit, analysis unit, and presentation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input changes in lifestyle and physical condition. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input data using generating AI and predicts diseases the user may contract. The presentation unit is implemented, for example, by the output device 40 of the smart device 14, which presents the analysis results to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the reception unit, analysis unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input changes in lifestyle and physical condition. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input data using a generating AI and predicts diseases the user may contract. The presentation unit is implemented by the speaker 240 of the smart glasses 214, which presents the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input changes in lifestyle and physical condition. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input data using a generating AI and predicts diseases the user may contract. The presentation unit is implemented by the display 343 of the headset terminal 314, which presents the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, and presentation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input changes in their lifestyle and physical condition. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input data using a generating AI and predicts diseases the user may contract. The presentation unit is implemented by, for example, the speaker 240 of the robot 414, which presents the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] 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.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) A reception area where you input changes in your lifestyle and physical condition, An analysis unit analyzes the data entered by the reception unit and predicts diseases that the patient may be susceptible to, The system includes a display unit that displays the probability of disease predicted by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter your lifestyle habits, physical condition changes, sleep data, and dietary data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The AI ​​generates data that analyzes lifestyle habits, changes in physical condition, sleep data, and dietary data to predict potential illnesses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Generative AI analyzes user data to predict diseases such as subarachnoid hemorrhage and acute glaucoma. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, The AI ​​generates and presents the user with the predicted probability of a disease. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Present areas for improvement and points to be aware of in the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Enter data using a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyzing employee health data to predict employee health status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of data entry based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past data entry history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering data, the system prioritizes inputting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When entering data, analyze the user's social media activity and input relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting the information, adjust the level of detail based on the predicted severity of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting, different presentation algorithms are applied depending on the predicted disease category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of the presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, When presenting, prioritize presentations based on the timing of the predicted illness. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting the information, adjust the order of presentation based on the predicted relevance of the disease. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you input changes in your lifestyle and physical condition, An analysis unit analyzes the data entered by the reception unit and predicts diseases that the patient may be susceptible to, The system includes a display unit that displays the probability of disease predicted by the analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter your lifestyle habits, physical condition changes, sleep data, and dietary data. The system according to feature 1.

3. The aforementioned analysis unit, Using AI-generated data, lifestyle habits, changes in physical condition, sleep data, and dietary data, the system predicts potential illnesses. The system according to feature 1.

4. The aforementioned analysis unit, Generative AI analyzes user data to predict diseases such as subarachnoid hemorrhage and acute glaucoma. The system according to feature 1.

5. The aforementioned display unit is, The AI ​​generates and presents the user with the probability of a disease predicted by the AI. The system according to feature 1.

6. The aforementioned display unit is, Present areas for improvement and points to be aware of in the user's lifestyle. The system according to feature 1.

7. The aforementioned reception unit is Enter data using a smartphone app. The system according to feature 1.

8. The aforementioned analysis unit, Analyzing employee health data to predict employee health status. The system according to feature 1.

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