system

Abstract

The system according to this embodiment aims to analyze the risk of hay fever and suggest appropriate actions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a monitoring unit. The reception unit receives user input. The analysis unit analyzes pollen risk based on the information received by the reception unit. The proposal unit proposes appropriate actions based on the results analyzed by the analysis unit. The monitoring unit monitors the user's physical condition.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to analyze the risk of hay fever in real time and propose appropriate actions.

[0005] The system according to the embodiment aims to analyze the risk of hay fever and propose appropriate actions.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a monitoring unit. The reception unit receives user input. The analysis unit analyzes the pollen risk based on the information received by the reception unit. The proposal unit proposes appropriate actions based on the results analyzed by the analysis unit. The monitoring unit monitors the user's physical condition. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the risk of hay fever and suggest appropriate actions. [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 smart pollen-avoidance AI agent according to an embodiment of the present invention is a new tool to support people suffering from hay fever. This smart pollen-avoidance AI agent considers daily activities and schedules and proposes optimal actions to reduce the risk of pollen. People with hay fever often experience worsening symptoms because it is difficult to predict the effects of pollen, which vary depending on the season and location. In particular, when going out, it can be difficult to decide what time is safe and which route to take. This AI agent analyzes the risk of pollen for the user in real time and proposes the optimal time and route for going out. It also works in conjunction with wearable devices such as smartwatches to monitor the user's physical condition. For example, it may choose a route that avoids areas with high pollen levels or recommend medication at the appropriate time when symptoms begin to appear. In this way, it supports users in always making the best choices. By using this AI agent, the effects of pollen can be minimized, and users can live more comfortably. Anxiety when going out is reduced, and the impact on work and school life is lessened. Furthermore, the quality of life improves, and people can spend hay fever season with peace of mind. In this way, by utilizing AI technology and smart devices, hay fever countermeasures can become more personalized and effective. As a result, smart pollen avoidance AI agents can minimize the impact of hay fever on users and support a more comfortable life.

[0029] The smart pollen avoidance AI agent according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a monitoring unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, image input, etc. The reception unit can receive information entered by the user using a smartphone or personal computer, for example. The analysis unit analyzes the pollen risk based on the information received by the reception unit. The analysis of pollen risk includes, but is not limited to, the type of pollen, concentration, and affected area, for example. The analysis unit can predict pollen dispersal based on historical data or real-time data, for example. The suggestion unit proposes the optimal action based on the results analyzed by the analysis unit. The suggestion unit can propose specific actions such as refraining from going outside, wearing a mask, or taking medication, for example. The monitoring unit monitors the user's physical condition. The monitoring unit can, for example, cooperate with a wearable device such as a smartwatch to collect the user's physical condition data in real time. As a result, the smart pollen-avoidance AI agent according to this embodiment can minimize the effects of hay fever by accepting user input, analyzing pollen risk, proposing optimal actions, and monitoring the user's physical condition.

[0030] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can use smartphones or personal computers to ask questions or report symptoms related to pollen. For example, if a user asks "What are the pollen conditions like today?" by voice, the reception desk recognizes the voice and converts it into text data. Also, if a user reports symptoms such as itchy eyes or a runny nose with an image, the reception desk receives the image and sends it to the analysis department. Furthermore, the reception desk can provide more personalized responses by considering the user's past input history and individual settings. For example, users who are particularly sensitive to a specific type of pollen will be given priority in receiving information about the pollen dispersal status of that pollen. This allows the reception desk to handle diverse user input formats and receive information quickly and accurately.

[0031] The analysis unit analyzes pollen risk based on information received by the reception unit. Pollen risk analysis includes, but is not limited to, the type, concentration, and affected area of ​​pollen. Specifically, the analysis unit predicts pollen dispersal based on historical and real-time data. For example, it combines historical pollen dispersal data and weather data to predict pollen dispersal in specific areas and time periods. It also collects data from weather sensors and pollen sensors as real-time data and analyzes current pollen concentration and dispersal range. Furthermore, the analysis unit uses AI to analyze this data and evaluate pollen dispersal patterns and risk levels. For example, it uses machine learning algorithms to learn pollen dispersal patterns from historical data and predict future dispersal conditions. It can also use anomaly detection algorithms to detect unusual pollen dispersal patterns early and issue warnings to users. This allows the analysis unit to analyze pollen risk with high accuracy and provide users with appropriate information.

[0032] The suggestion department proposes optimal actions based on the results analyzed by the analysis department. Specifically, it can suggest concrete actions such as refraining from going outside, wearing a mask, or taking medication. For example, on days with high pollen counts, it can advise users to refrain from going outside, and if going outside is unavoidable, it can recommend wearing a mask and sunglasses. It can also provide individually optimized suggestions by considering the user's past symptoms and behavioral history. For example, it can warn users who are particularly sensitive to a specific type of pollen to be especially careful on days when that pollen is expected to be dispersed. Furthermore, the suggestion department can also propose long-term measures based on the user's health condition and lifestyle. For example, it can provide advice on diet and exercise to alleviate hay fever symptoms. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. As a result, the suggestion department can propose optimal actions to users and minimize the impact of hay fever.

[0033] The monitoring unit monitors the user's physical condition. Specifically, it can collect user physical condition data in real time by linking with wearable devices such as smartwatches. For example, it can monitor changes in the user's heart rate and body temperature using the smartwatch's heart rate sensor and skin temperature sensor. It also collects information about the user's physical condition and symptom reports entered by the user, and comprehensively understands the user's health status. Furthermore, the monitoring unit can transmit the collected data to the analysis unit, which can analyze its correlation with pollen risk. For example, if a user's physical condition tends to worsen on days with high levels of a particular pollen, the unit can analyze the correlation and suggest appropriate measures to the user. The monitoring unit can also detect abnormal changes in physical condition early and issue warnings to the user. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, it will notify the user to rest. In this way, the monitoring unit can monitor the user's physical condition in real time and provide support to minimize the effects of hay fever.

[0034] The suggestion unit, using generative AI, can generate specific advice and alerts to help alleviate symptoms based on the user's profile and past behavioral data. For example, the suggestion unit provides individually optimized advice based on profile information such as the user's age, gender, and health status. It can also analyze the user's past outing history and symptom records to generate alerts that help alleviate symptoms. For instance, based on past data, the suggestion unit can issue an alert if the pollen risk is high at a specific time or location. This effectively alleviates the user's symptoms by generating specific advice and alerts based on the user's profile and past behavioral data. The generative AI generates advice and alerts for the user using technologies such as natural language generation and image generation. The generative AI takes user profile information and past behavioral data as input and outputs advice and alerts that help alleviate symptoms. This allows the suggestion unit to provide individualized advice and alerts to users using the generative AI.

[0035] The analysis unit uses generative AI to predict pollen dispersal and the timing of symptom onset based on historical and real-time data. For example, the analysis unit predicts pollen dispersal based on historical and current pollen dispersal data. It can also predict the timing of symptom onset based on the user's historical and current symptom data. For example, the analysis unit predicts pollen dispersal patterns for specific seasons and time periods based on historical data. This allows users to take preventative measures in advance. The generative AI uses, for example, prediction models and algorithms to predict pollen dispersal and the timing of symptom onset. The generative AI takes historical and real-time data as input and outputs pollen dispersal predictions and the timing of symptom onset. As a result, the analysis unit can use the generative AI to provide users with pollen dispersal predictions and the timing of symptom onset.

[0036] The monitoring unit can work in conjunction with smartwatches and other wearable devices to monitor the user's physical condition in real time. For example, the monitoring unit can use a smartwatch to collect biometric data such as the user's heart rate and body temperature. The monitoring unit can also use other wearable devices to collect data such as the user's activity level and sleep patterns. This allows for real-time monitoring of the user's physical condition and the issuance of alerts if abnormalities are detected. Based on the collected data, the monitoring unit grasps changes in the user's physical condition in real time. For example, if the monitoring unit detects a sudden change in heart rate or an abnormal rise in body temperature, it will alert the user. This allows for real-time monitoring of the user's physical condition by working in conjunction with wearable devices such as smartwatches. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data acquired from a smartwatch into a generating AI and have the generating AI execute a process to detect abnormalities in the user's physical condition.

[0037] The suggestion unit can generate personalized follow-up messages and interactive dialogues based on user selections. For example, the suggestion unit generates personalized follow-up messages based on user profile information and past behavioral data. It can also generate interactive dialogues based on user choices. For instance, if a user selects a specific action, the suggestion unit generates a follow-up message related to that action. This deepens engagement with the user. The generation AI generates follow-up messages and interactive dialogues for users, for example, using natural language generation technology. The generation AI takes user profile information and past behavioral data as input and outputs follow-up messages and interactive dialogues. This allows the suggestion unit to provide personalized follow-up messages and interactive dialogues to users using the generation AI.

[0038] The suggestion unit can automatically generate educational content, newsletters, and breaking news based on the latest research findings related to health. For example, the suggestion unit generates educational content based on the user's profile information and areas of interest. It can also generate and provide users with breaking news based on the latest research findings. For instance, the suggestion unit can automatically generate newsletters on health topics of interest to the user, ensuring they always have up-to-date information. The generation AI uses, for example, natural language generation technology to generate educational content and newsletters. The generation AI takes the user's profile information and areas of interest as input and outputs educational content and newsletters. This allows the suggestion unit to provide users with educational content and newsletters using the generation AI.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0040] The reception unit can provide input assistance tailored to the user's current situation and areas of interest based on the input content. For example, the reception unit can automatically complete relevant information based on the user's current situation. It can also customize the input content and provide appropriate information based on the user's areas of interest. Furthermore, the reception unit can simplify the input content and expedite processing according to the user's current situation and areas of interest. This streamlines the user's input process by providing input assistance tailored to the user's current situation and areas of interest. Some or all of the above-described processes in the reception unit may be performed using AI or not. For example, the reception unit can input the user's input data into a generating AI and have the generating AI perform the input assistance processing.

[0041] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location during input. For example, the reception unit can automatically retrieve relevant information based on the user's current location. The reception unit can also provide optimal information by considering the user's geographical location. Furthermore, the reception unit can prioritize displaying relevant information based on the user's current location. This allows the reception unit to provide useful information to the user by prioritizing the retrieval of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0042] The reception unit can analyze the user's social media activity and retrieve relevant information during input. For example, the reception unit can analyze the user's social media activity and automatically retrieve relevant information. The reception unit can also provide optimal information based on the user's social media activity. Furthermore, the reception unit can prioritize displaying relevant information, taking into account the user's social media activity. This allows the reception unit to retrieve and provide relevant information to the user by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant information.

[0043] The analysis unit can optimize its analysis algorithm based on past data during analysis. For example, the analysis unit can optimize the analysis algorithm based on past data to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to past data to increase efficiency. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm based on past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by considering the user's profile information during the analysis. For example, the analysis unit can optimize the analysis algorithm and improve accuracy based on profile information such as the user's age, gender, and health status. The analysis unit can also adjust the analysis algorithm to increase efficiency by considering the user's profile information. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm based on the user's profile information. In this way, by improving the accuracy of the analysis by considering the user's profile information, more reliable analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's profile information into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze relevant data based on the user's current location. The analysis unit can also select the optimal analysis method while considering the user's geographical location information. Furthermore, the analysis unit can customize the analysis results based on the user's current location. This allows the analysis unit to provide the user with useful analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature to improve accuracy. It can also adjust the analysis algorithm based on relevant data to increase efficiency. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm by referring to relevant literature and data. Thus, by referring to relevant literature and data, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0047] The proposal unit can analyze the user's past behavior data to make the most appropriate proposal. For example, the proposal unit can make the most appropriate proposal based on the user's past behavior data. The proposal unit can also analyze the user's past behavior data to make efficient proposals. Furthermore, the proposal unit can make customized proposals based on the user's past behavior data. In this way, by analyzing the user's past behavior data, the proposal unit can make the most appropriate proposals and support the user's actions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's past behavior data into a generation AI and have the generation AI generate the most appropriate proposal.

[0048] The proposal unit can customize the proposal content based on the user's current situation when making a proposal. For example, the proposal unit can make the optimal proposal based on the user's current situation. The proposal unit can also customize the proposal content considering the user's current situation. Furthermore, the proposal unit can make efficient proposals based on the user's current situation. In this way, by customizing the proposal content based on the user's current situation, it is possible to provide the optimal proposal for the user. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the proposal content.

[0049] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions based on the user's current location. The suggestion unit can also customize the suggested content by considering the user's geographical location information. Furthermore, the suggestion unit can make efficient suggestions based on the user's current location. In this way, by making optimal suggestions while considering the user's geographical location information, it is possible to provide suggestions that are beneficial to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of optimal suggestions.

[0050] The proposal unit can analyze the user's social media activity and adjust the proposal content when making a proposal. For example, the proposal unit can analyze the user's social media activity and make the most suitable proposal. The proposal unit can also customize the proposal content based on the user's social media activity. Furthermore, the proposal unit can make efficient proposals by taking the user's social media activity into consideration. In this way, by analyzing the user's social media activity, the proposal content can be adjusted and the most suitable proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0051] The monitoring unit can optimize the monitoring algorithm by referring to the user's past health data during monitoring. For example, the monitoring unit can optimize the monitoring algorithm based on the user's past health data to improve accuracy. The monitoring unit can also adjust the monitoring algorithm by referring to the user's past health data to increase efficiency. Furthermore, the monitoring unit can improve the monitoring algorithm based on the user's past health data to improve the reliability of the monitoring results. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0052] The monitoring unit can customize the monitoring content based on the user's current physical condition during monitoring. For example, the monitoring unit can provide optimal monitoring content based on the user's current physical condition. The monitoring unit can also customize the monitoring content considering the user's current physical condition. Furthermore, the monitoring unit can perform efficient monitoring based on the user's current physical condition. This allows for optimal monitoring for the user by customizing the monitoring content based on the user's current physical condition. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's current physical condition data into a generating AI and have the generating AI perform the customization of the monitoring content.

[0053] The monitoring unit can perform monitoring while considering the user's geographical location information. For example, the monitoring unit monitors relevant data based on the user's current location. The monitoring unit can also select the optimal monitoring method while considering the user's geographical location information. Furthermore, the monitoring unit can customize the monitoring results based on the user's current location. This allows the monitoring unit to provide the user with useful monitoring results by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0054] The monitoring unit can analyze the user's social media activity and adjust the monitoring content during monitoring. For example, the monitoring unit analyzes the user's social media activity and monitors related data. The monitoring unit can also customize the monitoring content based on the user's social media activity. Furthermore, the monitoring unit can perform efficient monitoring by taking the user's social media activity into consideration. This allows for adjustment of the monitoring content by analyzing the user's social media activity, enabling optimal monitoring for the user. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the monitoring content.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0057] The analysis unit can optimize the analysis algorithm based on past data during analysis. For example, it can optimize the analysis algorithm based on past data to improve accuracy. It can also adjust the analysis algorithm by referring to past data to increase efficiency. Furthermore, it can improve the reliability of the analysis results by refining the analysis algorithm based on past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0058] The monitoring unit can optimize the monitoring algorithm by referring to the user's past health data during monitoring. For example, it can optimize the monitoring algorithm based on the user's past health data to improve accuracy. It can also adjust the monitoring algorithm by referring to the user's past health data to increase efficiency. Furthermore, it can improve the monitoring algorithm based on the user's past health data to improve the reliability of the monitoring results. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0059] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, it can make optimal suggestions based on the user's current location. It can also customize the suggestion content by considering the user's geographical location information. Furthermore, it can make efficient suggestions based on the user's current location. In this way, by making optimal suggestions that consider the user's geographical location information, it is possible to provide suggestions that are beneficial to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of optimal suggestions.

[0060] The monitoring unit can perform monitoring while taking the user's geographical location information into consideration. For example, it can monitor relevant data based on the user's current location. It can also select the optimal monitoring method while considering the user's geographical location information. Furthermore, it can customize the monitoring results based on the user's current location. This allows for the provision of monitoring results that are useful to the user by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives user input. User input includes text input, voice input, image input, etc. The reception desk can accept information entered by the user using a smartphone or personal computer. Step 2: The analysis unit analyzes the pollen risk based on the information received by the reception unit. The pollen risk analysis includes the type of pollen, concentration, and affected area. The analysis unit can predict pollen dispersal based on historical data and real-time data. Step 3: The suggestion unit proposes the optimal course of action based on the results analyzed by the analysis unit. The suggestion unit can propose specific actions such as refraining from going out, wearing a mask, or taking medication. Step 4: The monitoring unit monitors the user's physical condition. The monitoring unit can work in conjunction with wearable devices such as smartwatches to collect the user's physical condition data in real time.

[0063] (Example of form 2) The smart pollen-avoidance AI agent according to an embodiment of the present invention is a new tool to support people suffering from hay fever. This smart pollen-avoidance AI agent considers daily activities and schedules and proposes optimal actions to reduce the risk of pollen. People with hay fever often experience worsening symptoms because it is difficult to predict the effects of pollen, which vary depending on the season and location. In particular, when going out, it can be difficult to decide what time is safe and which route to take. This AI agent analyzes the risk of pollen for the user in real time and proposes the optimal time and route for going out. It also works in conjunction with wearable devices such as smartwatches to monitor the user's physical condition. For example, it may choose a route that avoids areas with high pollen levels or recommend medication at the appropriate time when symptoms begin to appear. In this way, it supports users in always making the best choices. By using this AI agent, the effects of pollen can be minimized, and users can live more comfortably. Anxiety when going out is reduced, and the impact on work and school life is lessened. Furthermore, the quality of life improves, and people can spend hay fever season with peace of mind. In this way, by utilizing AI technology and smart devices, hay fever countermeasures can become more personalized and effective. As a result, smart pollen avoidance AI agents can minimize the impact of hay fever on users and support a more comfortable life.

[0064] The smart pollen avoidance AI agent according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a monitoring unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, image input, etc. The reception unit can receive information entered by the user using a smartphone or personal computer, for example. The analysis unit analyzes the pollen risk based on the information received by the reception unit. The analysis of pollen risk includes, but is not limited to, the type of pollen, concentration, and affected area, for example. The analysis unit can predict pollen dispersal based on historical data or real-time data, for example. The suggestion unit proposes the optimal action based on the results analyzed by the analysis unit. The suggestion unit can propose specific actions such as refraining from going outside, wearing a mask, or taking medication, for example. The monitoring unit monitors the user's physical condition. The monitoring unit can, for example, cooperate with a wearable device such as a smartwatch to collect the user's physical condition data in real time. As a result, the smart pollen-avoidance AI agent according to this embodiment can minimize the effects of hay fever by accepting user input, analyzing pollen risk, proposing optimal actions, and monitoring the user's physical condition.

[0065] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can use smartphones or personal computers to ask questions or report symptoms related to pollen. For example, if a user asks "What are the pollen conditions like today?" by voice, the reception desk recognizes the voice and converts it into text data. Also, if a user reports symptoms such as itchy eyes or a runny nose with an image, the reception desk receives the image and sends it to the analysis department. Furthermore, the reception desk can provide more personalized responses by considering the user's past input history and individual settings. For example, users who are particularly sensitive to a specific type of pollen will be given priority in receiving information about the pollen dispersal status of that pollen. This allows the reception desk to handle diverse user input formats and receive information quickly and accurately.

[0066] The analysis unit analyzes pollen risk based on information received by the reception unit. Pollen risk analysis includes, but is not limited to, the type, concentration, and affected area of ​​pollen. Specifically, the analysis unit predicts pollen dispersal based on historical and real-time data. For example, it combines historical pollen dispersal data and weather data to predict pollen dispersal in specific areas and time periods. It also collects data from weather sensors and pollen sensors as real-time data and analyzes current pollen concentration and dispersal range. Furthermore, the analysis unit uses AI to analyze this data and evaluate pollen dispersal patterns and risk levels. For example, it uses machine learning algorithms to learn pollen dispersal patterns from historical data and predict future dispersal conditions. It can also use anomaly detection algorithms to detect unusual pollen dispersal patterns early and issue warnings to users. This allows the analysis unit to analyze pollen risk with high accuracy and provide users with appropriate information.

[0067] The suggestion department proposes optimal actions based on the results analyzed by the analysis department. Specifically, it can suggest concrete actions such as refraining from going outside, wearing a mask, or taking medication. For example, on days with high pollen counts, it can advise users to refrain from going outside, and if going outside is unavoidable, it can recommend wearing a mask and sunglasses. It can also provide individually optimized suggestions by considering the user's past symptoms and behavioral history. For example, it can warn users who are particularly sensitive to a specific type of pollen to be especially careful on days when that pollen is expected to be dispersed. Furthermore, the suggestion department can also propose long-term measures based on the user's health condition and lifestyle. For example, it can provide advice on diet and exercise to alleviate hay fever symptoms. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. As a result, the suggestion department can propose optimal actions to users and minimize the impact of hay fever.

[0068] The monitoring unit monitors the user's physical condition. Specifically, it can collect user physical condition data in real time by linking with wearable devices such as smartwatches. For example, it can monitor changes in the user's heart rate and body temperature using the smartwatch's heart rate sensor and skin temperature sensor. It also collects information about the user's physical condition and symptom reports entered by the user, and comprehensively understands the user's health status. Furthermore, the monitoring unit can transmit the collected data to the analysis unit, which can analyze its correlation with pollen risk. For example, if a user's physical condition tends to worsen on days with high levels of a particular pollen, the unit can analyze the correlation and suggest appropriate measures to the user. The monitoring unit can also detect abnormal changes in physical condition early and issue warnings to the user. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, it will notify the user to rest. In this way, the monitoring unit can monitor the user's physical condition in real time and provide support to minimize the effects of hay fever.

[0069] The suggestion unit, using generative AI, can generate specific advice and alerts to help alleviate symptoms based on the user's profile and past behavioral data. For example, the suggestion unit provides individually optimized advice based on profile information such as the user's age, gender, and health status. It can also analyze the user's past outing history and symptom records to generate alerts that help alleviate symptoms. For instance, based on past data, the suggestion unit can issue an alert if the pollen risk is high at a specific time or location. This effectively alleviates the user's symptoms by generating specific advice and alerts based on the user's profile and past behavioral data. The generative AI generates advice and alerts for the user using technologies such as natural language generation and image generation. The generative AI takes user profile information and past behavioral data as input and outputs advice and alerts that help alleviate symptoms. This allows the suggestion unit to provide individualized advice and alerts to users using the generative AI.

[0070] The analysis unit uses generative AI to predict pollen dispersal and the timing of symptom onset based on historical and real-time data. For example, the analysis unit predicts pollen dispersal based on historical and current pollen dispersal data. It can also predict the timing of symptom onset based on the user's historical and current symptom data. For example, the analysis unit predicts pollen dispersal patterns for specific seasons and time periods based on historical data. This allows users to take preventative measures in advance. The generative AI uses, for example, prediction models and algorithms to predict pollen dispersal and the timing of symptom onset. The generative AI takes historical and real-time data as input and outputs pollen dispersal predictions and the timing of symptom onset. As a result, the analysis unit can use the generative AI to provide users with pollen dispersal predictions and the timing of symptom onset.

[0071] The monitoring unit can work in conjunction with smartwatches and other wearable devices to monitor the user's physical condition in real time. For example, the monitoring unit can use a smartwatch to collect biometric data such as the user's heart rate and body temperature. The monitoring unit can also use other wearable devices to collect data such as the user's activity level and sleep patterns. This allows for real-time monitoring of the user's physical condition and the issuance of alerts if abnormalities are detected. Based on the collected data, the monitoring unit grasps changes in the user's physical condition in real time. For example, if the monitoring unit detects a sudden change in heart rate or an abnormal rise in body temperature, it will alert the user. This allows for real-time monitoring of the user's physical condition by working in conjunction with wearable devices such as smartwatches. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data acquired from a smartwatch into a generating AI and have the generating AI execute a process to detect abnormalities in the user's physical condition.

[0072] The suggestion unit can generate personalized follow-up messages and interactive dialogues based on user selections. For example, the suggestion unit generates personalized follow-up messages based on user profile information and past behavioral data. It can also generate interactive dialogues based on user choices. For instance, if a user selects a specific action, the suggestion unit generates a follow-up message related to that action. This deepens engagement with the user. The generation AI generates follow-up messages and interactive dialogues for users, for example, using natural language generation technology. The generation AI takes user profile information and past behavioral data as input and outputs follow-up messages and interactive dialogues. This allows the suggestion unit to provide personalized follow-up messages and interactive dialogues to users using the generation AI.

[0073] The suggestion unit can automatically generate educational content, newsletters, and breaking news based on the latest research findings related to health. For example, the suggestion unit generates educational content based on the user's profile information and areas of interest. It can also generate and provide users with breaking news based on the latest research findings. For instance, the suggestion unit can automatically generate newsletters on health topics of interest to the user, ensuring they always have up-to-date information. The generation AI uses, for example, natural language generation technology to generate educational content and newsletters. The generation AI takes the user's profile information and areas of interest as input and outputs educational content and newsletters. This allows the suggestion unit to provide users with educational content and newsletters using the generation AI.

[0074] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk will prioritize inputting important information and postpone other information. If the user is relaxed, the reception desk can encourage them to input detailed information to ensure comprehensive information is received. Furthermore, if the user is in a hurry, the reception desk can prompt them to input only the most important information to expedite processing. This allows for appropriate input tailored to the user's situation by prioritizing input based on their 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 user input data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0076] The reception unit can provide input assistance tailored to the user's current situation and areas of interest based on the input content. For example, the reception unit can automatically complete relevant information based on the user's current situation. It can also customize the input content and provide appropriate information based on the user's areas of interest. Furthermore, the reception unit can simplify the input content and expedite processing according to the user's current situation and areas of interest. This streamlines the user's input process by providing input assistance tailored to the user's current situation and areas of interest. Some or all of the above-described processes in the reception unit may be performed using AI or not. For example, the reception unit can input the user's input data into a generating AI and have the generating AI perform the input assistance processing.

[0077] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for the provision of an appropriate interface tailored to the user's situation by adjusting the display method of the input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 unit may be performed using AI or not. For example, the reception unit can input user input data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location during input. For example, the reception unit can automatically retrieve relevant information based on the user's current location. The reception unit can also provide optimal information by considering the user's geographical location. Furthermore, the reception unit can prioritize displaying relevant information based on the user's current location. This allows the reception unit to provide useful information to the user by prioritizing the retrieval of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0079] The reception unit can analyze the user's social media activity and retrieve relevant information during input. For example, the reception unit can analyze the user's social media activity and automatically retrieve relevant information. The reception unit can also provide optimal information based on the user's social media activity. Furthermore, the reception unit can prioritize displaying relevant information, taking into account the user's social media activity. This allows the reception unit to retrieve and provide relevant information to the user by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, appropriate analysis results can be provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0081] The analysis unit can optimize its analysis algorithm based on past data during analysis. For example, the analysis unit can optimize the analysis algorithm based on past data to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to past data to increase efficiency. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm based on past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0082] The analysis unit can improve the accuracy of the analysis by considering the user's profile information during the analysis. For example, the analysis unit can optimize the analysis algorithm and improve accuracy based on profile information such as the user's age, gender, and health status. The analysis unit can also adjust the analysis algorithm to increase efficiency by considering the user's profile information. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm based on the user's profile information. In this way, by improving the accuracy of the analysis by considering the user's profile information, more reliable analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's profile information into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit will prioritize displaying important analysis results. It can also display detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display only the most important analysis results. This allows for the provision of appropriate analysis results tailored to the user's situation by prioritizing analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0084] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze relevant data based on the user's current location. The analysis unit can also select the optimal analysis method while considering the user's geographical location information. Furthermore, the analysis unit can customize the analysis results based on the user's current location. This allows the analysis unit to provide the user with useful analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature to improve accuracy. It can also adjust the analysis algorithm based on relevant data to increase efficiency. Furthermore, the analysis unit can improve the reliability of the analysis results by refining the analysis algorithm by referring to relevant literature and data. Thus, by referring to relevant literature and data, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0086] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable presentation. If the user is relaxed, the suggestion unit can also provide a more detailed presentation. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise presentation. By adjusting the presentation of suggestions based on the user's emotions, the system can provide appropriate suggestions tailored to the user's situation. 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-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggestions.

[0087] The proposal unit can analyze the user's past behavior data to make the most appropriate proposal. For example, the proposal unit can make the most appropriate proposal based on the user's past behavior data. The proposal unit can also analyze the user's past behavior data to make efficient proposals. Furthermore, the proposal unit can make customized proposals based on the user's past behavior data. In this way, by analyzing the user's past behavior data, the proposal unit can make the most appropriate proposals and support the user's actions. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's past behavior data into a generation AI and have the generation AI generate the most appropriate proposal.

[0088] The proposal unit can customize the proposal content based on the user's current situation when making a proposal. For example, the proposal unit can make the optimal proposal based on the user's current situation. The proposal unit can also customize the proposal content considering the user's current situation. Furthermore, the proposal unit can make efficient proposals based on the user's current situation. In this way, by customizing the proposal content based on the user's current situation, it is possible to provide the optimal proposal for the user. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the proposal content.

[0089] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit can also display more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can display only the most important suggestions. This allows the system to provide appropriate suggestions tailored to the user's situation by prioritizing suggestions based on their 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0090] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions based on the user's current location. The suggestion unit can also customize the suggested content by considering the user's geographical location information. Furthermore, the suggestion unit can make efficient suggestions based on the user's current location. In this way, by making optimal suggestions while considering the user's geographical location information, it is possible to provide suggestions that are beneficial to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of optimal suggestions.

[0091] The proposal unit can analyze the user's social media activity and adjust the proposal content when making a proposal. For example, the proposal unit can analyze the user's social media activity and make the most suitable proposal. The proposal unit can also customize the proposal content based on the user's social media activity. Furthermore, the proposal unit can make efficient proposals by taking the user's social media activity into consideration. In this way, by analyzing the user's social media activity, the proposal content can be adjusted and the most suitable proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0092] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency. Conversely, if the user is relaxed, the monitoring unit can decrease the monitoring frequency. Furthermore, if the user is in a hurry, the monitoring unit can adjust the monitoring frequency. This allows for appropriate monitoring according to the user's situation by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0093] The monitoring unit can optimize the monitoring algorithm by referring to the user's past health data during monitoring. For example, the monitoring unit can optimize the monitoring algorithm based on the user's past health data to improve accuracy. The monitoring unit can also adjust the monitoring algorithm by referring to the user's past health data to increase efficiency. Furthermore, the monitoring unit can improve the monitoring algorithm based on the user's past health data to improve the reliability of the monitoring results. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0094] The monitoring unit can customize the monitoring content based on the user's current physical condition during monitoring. For example, the monitoring unit can provide optimal monitoring content based on the user's current physical condition. The monitoring unit can also customize the monitoring content considering the user's current physical condition. Furthermore, the monitoring unit can perform efficient monitoring based on the user's current physical condition. This allows for optimal monitoring for the user by customizing the monitoring content based on the user's current physical condition. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's current physical condition data into a generating AI and have the generating AI perform the customization of the monitoring content.

[0095] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. In this way, by adjusting the display method of the monitoring results based on the user's emotions, appropriate monitoring results can be provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the monitoring results.

[0096] The monitoring unit can perform monitoring while considering the user's geographical location information. For example, the monitoring unit monitors relevant data based on the user's current location. The monitoring unit can also select the optimal monitoring method while considering the user's geographical location information. Furthermore, the monitoring unit can customize the monitoring results based on the user's current location. This allows the monitoring unit to provide the user with useful monitoring results by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0097] The monitoring unit can analyze the user's social media activity and adjust the monitoring content during monitoring. For example, the monitoring unit analyzes the user's social media activity and monitors related data. The monitoring unit can also customize the monitoring content based on the user's social media activity. Furthermore, the monitoring unit can perform efficient monitoring by taking the user's social media activity into consideration. This allows for adjustment of the monitoring content by analyzing the user's social media activity, enabling optimal monitoring for the user. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the monitoring content.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is stressed, important information will be prioritized, and other information will be postponed. If the user is relaxed, they may be encouraged to enter detailed information, ensuring that all information is covered. Furthermore, if the user is in a hurry, only the most important information will be entered, allowing for quick processing. By prioritizing input content based on the user's emotions, appropriate input tailored to the user's situation is possible. 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 user input data into a generative AI and have the generative AI perform emotion estimation.

[0100] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is nervous, it can provide a simple and easily visible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the suggestion based on the user's emotions, it is possible to provide appropriate suggestions tailored to the user's situation. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the suggestion.

[0101] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring frequency can be increased. Conversely, if the user is relaxed, the monitoring frequency can be decreased. Furthermore, if the user is in a hurry, the monitoring frequency can be adjusted. This allows for appropriate monitoring according to the user's situation by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide appropriate analysis results according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0103] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, important suggestions will be displayed first. If the user is relaxed, detailed suggestions may be displayed. Furthermore, if the user is in a hurry, only the most important suggestions may be displayed. This allows for the provision of appropriate suggestions tailored to the user's situation by prioritizing suggestions based on 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0104] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0105] The analysis unit can optimize the analysis algorithm based on past data during analysis. For example, it can optimize the analysis algorithm based on past data to improve accuracy. It can also adjust the analysis algorithm by referring to past data to increase efficiency. Furthermore, it can improve the reliability of the analysis results by refining the analysis algorithm based on past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0106] The monitoring unit can optimize the monitoring algorithm by referring to the user's past health data during monitoring. For example, it can optimize the monitoring algorithm based on the user's past health data to improve accuracy. It can also adjust the monitoring algorithm by referring to the user's past health data to increase efficiency. Furthermore, it can improve the monitoring algorithm based on the user's past health data to improve the reliability of the monitoring results. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0107] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, it can make optimal suggestions based on the user's current location. It can also customize the suggestion content by considering the user's geographical location information. Furthermore, it can make efficient suggestions based on the user's current location. In this way, by making optimal suggestions that consider the user's geographical location information, it is possible to provide suggestions that are beneficial to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of optimal suggestions.

[0108] The monitoring unit can perform monitoring while taking the user's geographical location information into consideration. For example, it can monitor relevant data based on the user's current location. It can also select the optimal monitoring method while considering the user's geographical location information. Furthermore, it can customize the monitoring results based on the user's current location. This allows for the provision of monitoring results that are useful to the user by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives user input. User input includes text input, voice input, image input, etc. The reception desk can accept information entered by the user using a smartphone or personal computer. Step 2: The analysis unit analyzes the pollen risk based on the information received by the reception unit. The pollen risk analysis includes the type of pollen, concentration, and affected area. The analysis unit can predict pollen dispersal based on historical data and real-time data. Step 3: The suggestion unit proposes the optimal course of action based on the results analyzed by the analysis unit. The suggestion unit can propose specific actions such as refraining from going out, wearing a mask, or taking medication. Step 4: The monitoring unit monitors the user's physical condition. The monitoring unit can work in conjunction with wearable devices such as smartwatches to collect the user's physical condition data in real time.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and monitoring unit, is implemented in 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 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes pollen risk. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the user's physical condition in cooperation with a wearable device such as a smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and monitoring 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 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes pollen risk. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's physical condition in cooperation with a wearable device such as a smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and monitoring unit, is implemented by, for example, 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 and receives user input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes pollen risk. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 and monitors the user's physical condition in cooperation with a wearable device such as a smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and monitoring 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 and receives user input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes pollen risk. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The monitoring unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the user's physical condition in cooperation with a wearable device such as a smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception area that receives user input, An analysis unit analyzes the pollen risk based on the information received by the reception unit, A proposal unit that proposes appropriate actions based on the results of the analysis performed by the aforementioned analysis unit, It includes a monitoring unit that monitors the user's physical condition. A system characterized by the following features. (Note 2) The aforementioned proposal section is, The AI ​​generates specific advice and alerts to help alleviate symptoms, based on the user's profile and past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using generative AI, we predict pollen dispersal and the timing of symptom onset based on historical and real-time data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, It works in conjunction with smartwatches and other wearable devices to monitor the user's health in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Generate personalized follow-up messages and interactive conversations based on user choices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Automatically generates educational content, newsletters, and breaking news based on the latest research findings related to health. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Based on the input, the system provides input assistance tailored to the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input data, the system prioritizes retrieving highly relevant information based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the user's profile information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we analyze the user's past behavioral data to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and adjust the proposal accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring content is customized based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, we analyze users' social media activity and adjust the monitoring settings accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 that receives user input, An analysis unit analyzes the pollen risk based on the information received by the reception unit, A proposal unit that proposes appropriate actions based on the results of the analysis performed by the aforementioned analysis unit, It includes a monitoring unit that monitors the user's physical condition. A system characterized by the following features.

2. The aforementioned proposal section is, The AI ​​generates specific advice and alerts to help alleviate symptoms, based on the user's profile and past behavioral data. The system according to feature 1.

3. The aforementioned analysis unit, Using generative AI, we predict pollen dispersal and the timing of symptom onset based on historical and real-time data. The system according to feature 1.

4. The monitoring unit, It works in conjunction with smartwatches and other wearable devices to monitor the user's health in real time. The system according to feature 1.

5. The aforementioned proposal section is, Generate personalized follow-up messages and interactive conversations based on user choices. The system according to feature 1.

6. The aforementioned proposal section is, Automatically generates educational content, newsletters, and breaking news based on the latest research findings related to health. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is Based on the input, the system provides input assistance tailored to the user's current situation and areas of interest. The system according to feature 1.

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