system

Abstract

The system according to this embodiment aims to accurately understand the patient's symptoms and the degree of pain, and to propose appropriate improvement measures. [Solution] The system according to the embodiment comprises an analysis unit, a visualization unit, an inference unit, a proposal unit, and a report generation unit. The analysis unit analyzes the user's words, facial expressions, and voice. The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. The inference unit infers the name of the disease and its cause based on the information visualized by the visualization unit. The proposal unit proposes personalized improvement measures based on the information inferred by the inference unit. The report generation unit generates a medical report based on the information proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 prior art, it is difficult to accurately grasp the symptoms and pain level of a patient and propose appropriate improvement measures, which may increase the medical treatment time in a medical institution.

[0005] The system according to the embodiment aims to accurately grasp the symptoms and pain level of a patient and propose appropriate improvement measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a visualization unit, an inference unit, a proposal unit, and a report generation unit. The analysis unit analyzes the user's words, facial expressions, and voice. The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. The inference unit infers the name of the disease and its cause based on the information visualized by the visualization unit. The proposal unit proposes personalized improvement measures based on the information inferred by the inference unit. The report generation unit generates a medical report based on the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately grasp the patient's symptoms and the degree of pain, and propose appropriate improvement measures. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 Care AI agent system according to an embodiment of the present invention is a system that analyzes symptoms during times of physical and psychological distress and proposes solutions. This Care AI agent system analyzes the user's words, facial expressions, and voice, visualizes symptoms and their severity in real time, and infers the name and cause of the illness. Next, it proposes personalized solutions and provides empathetic support through a personalized AI character. This enables the generation of medical reports for healthcare institutions, allowing for rapid and appropriate treatment, and is expected to shorten consultation times and improve the accuracy of medical care. It also aims to reduce the psychological burden on patients and alleviate physical symptoms. Furthermore, reminder functions for regular online consultations, hospital visits, and medication adherence enable daily and continuous care. The Care AI agent evolves through continuous self-learning, and is constantly updated with the latest medical knowledge. This allows it to become an empathetic and supportive presence for users who feel distressed but do not want to burden their family or those around them. For example, the Care AI agent system analyzes the user's words, facial expressions, and voice. For example, the Care AI agent system analyzes the user's words to identify emotions and symptoms. Next, the Care AI agent system analyzes the user's facial expressions to determine the degree of pain or discomfort. Furthermore, the Care AI agent system analyzes the user's voice to estimate the level of stress and fatigue. This allows the Care AI agent system to comprehensively understand the user's condition and propose appropriate solutions. As a result, the Care AI agent system can analyze the user's symptoms in real time and propose appropriate solutions.

[0029] The Care AI agent system according to this embodiment comprises an analysis unit, a visualization unit, an inference unit, a suggestion unit, and a report generation unit. The analysis unit analyzes the user's words, facial expressions, and voice. For example, the analysis unit analyzes the user's words to identify emotions and symptoms. For example, the analysis unit analyzes the user's facial expressions to determine the degree of pain or discomfort. For example, the analysis unit analyzes the user's voice to infer the level of stress or fatigue. The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. For example, the visualization unit displays the intensity of pain in a graph. For example, the visualization unit indicates the level of fatigue with color. For example, the visualization unit represents the degree of stress numerically. The inference unit infers the name and cause of a disease based on the information visualized by the visualization unit. For example, the inference unit infers the name of a disease from the location and intensity of pain. For example, the inference unit identifies the cause of fatigue. For example, the inference unit analyzes the factors contributing to stress. The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. For example, the suggestion unit proposes methods for pain relief. For example, the suggestion unit proposes exercises for fatigue recovery. For example, the suggestion unit proposes relaxation methods for stress reduction. The report generation unit generates a medical report based on the information proposed by the suggestion unit. For example, the report generation unit includes information on the intensity and location of pain in the medical report. For example, the report generation unit includes information on the level and cause of fatigue in the medical report. For example, the report generation unit includes information on the degree and factors of stress in the medical report. As a result, the Care AI agent system according to the embodiment can analyze the user's words, facial expressions, and voice, visualize symptoms and their severity in real time, predict the name and cause of the illness, propose personalized improvement measures, and generate a medical report.

[0030] The analysis unit analyzes the user's words, facial expressions, and voice. For example, the analysis unit analyzes the user's words to identify emotions and symptoms. Specifically, it uses natural language processing technology to analyze the content of the user's statements and extract emotions and symptoms from keywords and context. For example, if a user says, "My head hurts," the analysis unit extracts the keyword "pain" and identifies that the pain is in the head. It also analyzes the user's facial expressions to determine the degree of pain or discomfort. For facial expression analysis, it uses image recognition technology to analyze the movement of facial muscles, eye opening and closing, and the degree to which the corners of the mouth are turned up. For example, if a user is frowning, the analysis unit will determine from the expression that they are feeling pain or discomfort. Furthermore, it analyzes the user's voice to estimate the level of stress and fatigue. For voice analysis, it uses speech recognition technology to analyze the tone, pitch, and speaking speed of the voice. For example, if a user's voice is trembling, the analysis unit will infer from the voice that they are feeling stressed. In this way, the analysis unit can collect information from multiple angles from the user's words, facial expressions, and voice to identify emotions and symptoms.

[0031] The visualization unit visualizes symptoms and their severity, such as pain, in real time based on information analyzed by the analysis unit. Specifically, it displays the user's pain intensity in a graph based on data obtained from the analysis unit. For example, it can represent pain intensity on a scale from 0 to 10 and display the change in pain intensity over time as a line graph. It also indicates the level of fatigue with color. For example, low fatigue is displayed in green, moderate fatigue in yellow, and high fatigue in red. Furthermore, it represents the degree of stress numerically. For example, it quantifies the degree of stress on a scale from 0 to 100, visually showing how much stress the user is experiencing. This allows the visualization unit to intuitively understand the user's symptoms and their severity, and supports real-time situation assessment. In addition, the visualization unit can save and compare the symptoms and severity the user has experienced in the past as a history. This makes it easier to understand changes in the user's symptoms and trends in improvement.

[0032] The prediction unit predicts the name and cause of a disease based on the information visualized by the visualization unit. Specifically, it predicts the name of a disease from the location and intensity of pain. For example, if headaches occur frequently and are above a certain intensity, it predicts the possibility of migraines. It also identifies the cause of fatigue. For example, if long hours of work or lack of sleep continue, it predicts that the cause of fatigue is overwork or lack of sleep. Furthermore, it analyzes the factors of stress. For example, if a user is experiencing stress in a particular situation or environment, it predicts that the factors are work pressure or interpersonal problems. The prediction unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely name and cause of the disease. As a result, the prediction unit can predict the name and cause of a disease with high accuracy based on the user's symptoms and severity, and provide information to take appropriate measures. In addition, the prediction unit can also utilize past data and statistical information to perform long-term risk assessment and trend analysis.

[0033] The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. Specifically, it suggests methods for pain relief. For example, in the case of a headache, it suggests applying a cold compress or getting rest. It also suggests exercise for fatigue recovery. For example, it suggests methods to reduce fatigue by doing light stretching or walking. Furthermore, it suggests relaxation methods to reduce stress. For example, it suggests methods to reduce stress by doing deep breathing or meditation. The suggestion unit can provide the most suitable improvement measures according to the user's individual circumstances and needs. In this way, the suggestion unit can help users take appropriate measures according to their symptoms and their severity, and promote improvement in their health. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions.

[0034] The report generation unit generates medical reports based on the information proposed by the proposal unit. Specifically, the medical report includes information on the intensity and location of pain. For example, it might record that the headache intensity is 7 out of 10 and the location is the frontal region. It also includes information on the level and cause of fatigue. For example, it might record that the fatigue level is 8 out of 10 and the cause is lack of sleep. Furthermore, it includes information on the degree and factors of stress. For example, it might record that the stress level is 70 out of 100 and the factor is work pressure. The report generation unit can centrally compile this information and provide it in a format that is easily understood by healthcare professionals. This allows the report generation unit to support healthcare professionals in quickly understanding the user's symptoms and their severity and providing appropriate medical care. In addition, the report generation unit can save past reports and track changes and improvements in the user's symptoms. This makes it easier for healthcare professionals to understand the user's long-term health status.

[0035] The reminder function provides reminders for regular online consultations, hospital visits, medication adherence, etc. For example, the reminder function may remind users of weekly online consultations, monthly hospital visits, or daily medication adherence. By providing reminder functions for regular online consultations, hospital visits, medication adherence, etc., daily and continuous care becomes possible. Some or all of the above-described processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input the user's schedule information into a generation AI and have the generation AI generate reminders.

[0036] The learning unit performs continuous self-learning. The learning unit performs self-learning using, for example, machine learning algorithms. The learning unit performs self-learning by, for example, setting the data update frequency. The learning unit performs self-learning based on, for example, user feedback. As a result, continuous self-learning improves the accuracy of the system and allows it to constantly update with the latest medical knowledge. Some or all of the above processes in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input user feedback data into a generating AI and have the generating AI perform self-learning.

[0037] The update unit updates the latest medical knowledge. For example, the update unit updates new treatment methods. For example, the update unit updates the latest research findings. For example, the update unit updates new drug information. By updating the latest medical knowledge, it becomes possible to provide care that is always based on the latest information. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the latest research findings data into a generating AI and have the generating AI update the medical knowledge.

[0038] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit may refer to the user's past medical records and perform an analysis by comparing them with the user's current symptoms. For example, the analysis unit may analyze the user's current health status based on the user's past health checkup results. For example, the analysis unit may refer to the user's past medication history and perform an analysis considering the effects of medication. In this way, the accuracy of the analysis is improved by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI improve the accuracy of the analysis.

[0039] The analysis unit can perform analysis while considering the user's lifestyle and environmental information. For example, the analysis unit can perform analysis while considering the user's diet and exercise habits. For example, the analysis unit can perform analysis while considering the user's living environment and work environment. For example, the analysis unit can perform analysis while considering the user's sleep patterns and stress levels. This allows for more accurate analysis by considering the user's lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the analysis.

[0040] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can perform analysis while considering the climate and environment of the user's current location. For example, the analysis unit can perform analysis while considering the medical resources in the area where the user lives. For example, the analysis unit can perform analysis while considering diseases prevalent in a particular area based on the user's geographical location. This makes it possible to perform more accurate analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. 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.

[0041] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can estimate the stress level from the user's social media posts and reflect it in the analysis. For example, the analysis unit can perform the analysis considering the frequency of the user's activity on social media. For example, the analysis unit can analyze the user's friendships and communication patterns on social media and reflect them in the analysis. This makes it possible to perform a more accurate analysis by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the analysis.

[0042] The visualization unit can adjust the level of detail of the visualization based on the importance of the symptoms during visualization. For example, the visualization unit provides a detailed graph for symptoms of high importance. For example, the visualization unit provides a simplified graph for symptoms of low importance. For example, the visualization unit provides graphs of different colors and shapes depending on the importance of the symptoms. This allows for more appropriate visualization by adjusting the level of detail of the visualization based on the importance of the symptoms. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom importance data into a generating AI and have the generating AI adjust the level of detail.

[0043] The visualization unit can apply different visualization methods depending on the symptom category during visualization. For example, the visualization unit provides a heatmap for pain symptoms, a line graph for mental symptoms, and a bar graph for physical symptoms. By applying different visualization methods depending on the symptom category, more appropriate visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom category data into a generating AI and have the generating AI apply the visualization method.

[0044] The visualization unit can determine the priority of visualization based on the timing of symptom occurrence during visualization. For example, the visualization unit may prioritize the visualization of recently occurring symptoms. For example, the visualization unit may determine the order of visualization by considering the timing of past symptom occurrences. For example, the visualization unit may determine the priority of visualization based on the frequency of symptom occurrences. This allows for more appropriate visualization by determining the priority of visualization based on the timing of symptom occurrences. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may input symptom occurrence timing data into a generating AI and have the generating AI determine the priority.

[0045] The visualization unit can adjust the order of visualization based on the relevance of symptoms during visualization. For example, the visualization unit may visualize highly relevant symptoms together. For example, the visualization unit may visualize less relevant symptoms individually. For example, the visualization unit may provide graphs of different colors and shapes based on the relevance of symptoms. This allows for more appropriate visualization by adjusting the order of visualization based on the relevance of symptoms. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom relevance data into a generating AI and have the generating AI adjust the order.

[0046] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of symptoms. For example, the prediction unit analyzes the interrelationships of multiple symptoms to predict the name of a disease. For example, the prediction unit makes predictions by considering the order in which symptoms occur. For example, the prediction unit makes predictions based on the intensity and frequency of symptoms. This improves the accuracy of the predictions by considering the interrelationships of symptoms. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationship data of symptoms into a generating AI and have the generating AI improve the accuracy of the predictions.

[0047] The prediction unit can perform predictions while considering the user's attribute information. For example, the prediction unit can make predictions while considering the user's age and gender. For example, the prediction unit can make predictions while considering the user's occupation and lifestyle. For example, the prediction unit can make predictions while considering the user's medical history and family history. This makes it possible to make more accurate predictions by considering the user's attribute information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's attribute information into a generating AI and have the generating AI perform the prediction.

[0048] The prediction unit can perform predictions while considering the geographical distribution of symptoms. For example, the prediction unit can make predictions by considering diseases prevalent in a particular region. For example, the prediction unit can make predictions by considering the medical resources in the user's area of ​​residence. For example, the prediction unit can make predictions by considering symptoms that are likely to occur in a particular region based on the user's geographical location. This allows for more accurate predictions by considering the geographical distribution of symptoms. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical distribution data of symptoms into a generating AI and have the generating AI perform the prediction.

[0049] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit makes inferences by referring to the latest medical papers. For example, the inference unit makes inferences based on past case reports. For example, the inference unit makes inferences by referring to relevant medical books. In this way, the accuracy of the inference is improved by referring to relevant literature. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature data into a generating AI and have the generating AI improve the accuracy of the inferences.

[0050] The suggestion unit can adjust the level of detail of its suggestions based on the severity of the symptoms. For example, the suggestion unit can provide detailed suggestions for symptoms of high severity, or simplified suggestions for symptoms of low severity. For example, it can provide suggestions with different colors or shapes depending on the severity of the symptoms. By adjusting the level of detail of suggestions based on the severity of the symptoms, more appropriate suggestions can be made. 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 symptom severity data into a generating AI and have the generating AI adjust the level of detail.

[0051] The suggestion unit can apply different suggestion algorithms depending on the symptom category when making a suggestion. For example, the suggestion unit may suggest a specific treatment for pain symptoms. For example, the suggestion unit may suggest counseling for mental symptoms. For example, the suggestion unit may suggest exercise therapy for physical symptoms. By applying different suggestion algorithms depending on the symptom category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom category data into a generating AI and have the generating AI apply the suggestion algorithm.

[0052] The suggestion unit can determine the priority of suggestions based on the timing of symptom occurrence when making suggestions. For example, the suggestion unit may prioritize suggestions for recently occurring symptoms. For example, the suggestion unit may determine the order of suggestions by considering the timing of past symptom occurrences. For example, the suggestion unit may determine the priority of suggestions based on the frequency of symptom occurrences. This allows for more appropriate suggestions by determining the priority of suggestions based on the timing of symptom occurrences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input symptom occurrence timing data into a generating AI and have the generating AI determine the priority.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of symptoms. For example, the suggestion unit may suggest highly relevant symptoms together. For example, the suggestion unit may suggest less relevant symptoms individually. For example, the suggestion unit may provide suggestions for different colors or shapes based on the relevance of symptoms. By adjusting the order of suggestions based on the relevance of symptoms, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom relevance data into a generating AI and have the generating AI adjust the order.

[0054] The report generation unit can adjust the level of detail in a report based on the severity of the symptoms during report generation. For example, the report generation unit provides a detailed report for symptoms of high severity. For example, the report generation unit provides a simplified report for symptoms of low severity. For example, the report generation unit provides reports with different colors or shapes depending on the severity of the symptoms. By adjusting the level of detail in the report based on the severity of the symptoms, more appropriate reports can be generated. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom severity data into a generation AI and have the generation AI adjust the level of detail.

[0055] The report generation unit can apply different report generation algorithms depending on the symptom category when generating a report. For example, the report generation unit may apply a specific report generation algorithm for pain symptoms. For example, it may apply a different report generation algorithm for mental symptoms. For example, it may apply yet another report generation algorithm for physical symptoms. By applying different report generation algorithms depending on the symptom category, more appropriate reports can be generated. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom category data into a generation AI and have the generation AI apply the report generation algorithm.

[0056] The report generation unit can determine the priority of reports based on the timing of symptom occurrence when generating reports. For example, the report generation unit may prioritize the inclusion of recently occurring symptoms in the report. For example, the report generation unit may determine the order of reports by considering the timing of past symptom occurrences. For example, the report generation unit may determine the priority of reports based on the frequency of symptom occurrences. This allows for more appropriate reports by determining the priority of reports based on the timing of symptom occurrences. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom occurrence timing data into a generation AI and have the generation AI determine the priority.

[0057] The report generation unit can adjust the order of reports based on the relevance of symptoms during report generation. For example, the report generation unit may group highly relevant symptoms together in a report. For example, the report generation unit may include less relevant symptoms individually in a report. For example, the report generation unit may provide reports with different colors or shapes based on the relevance of symptoms. This allows for more appropriate reports by adjusting the order of reports based on the relevance of symptoms. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom relevance data into a generation AI and have the generation AI adjust the order.

[0058] The reminder unit can select the optimal notification method by referring to the user's past behavior history when sending a reminder notification. For example, the reminder unit selects the optimal notification method based on the user's past behavior history. For example, the reminder unit analyzes the user's past behavior patterns and selects the optimal notification timing. For example, the reminder unit refers to the user's past behavior history and selects the optimal notification content. In this way, the optimal notification method can be selected by referring to the user's past behavior history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the user's past behavior history data into a generating AI and have the generating AI select the notification method.

[0059] The reminder unit can select the optimal notification method when sending a reminder notification, taking into account the user's device information. For example, if the user is using a smartphone, the reminder unit provides a notification method that matches the screen size. For example, if the user is using a tablet, the reminder unit provides a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the reminder unit provides a concise and highly visible notification method. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's device information into a generating AI and have the generating AI select the notification method.

[0060] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI optimize the learning algorithm.

[0061] The learning unit can weight the training data based on the timing of symptom occurrence during training. For example, the learning unit can train by weighting data of recently occurring symptoms. For example, the learning unit can weight the training data by considering the timing of past symptom occurrences. For example, the learning unit can weight the training data based on the frequency of symptom occurrences. This allows for more appropriate training by weighting the training data based on the timing of symptom occurrences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input symptom occurrence timing data into a generating AI and have the generating AI perform the weighting.

[0062] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit selects the optimal update algorithm based on past update data. For example, the update unit analyzes past update data and adjusts the parameters of the update algorithm. For example, the update unit improves the accuracy of the update algorithm by referring to past update data. In this way, the update algorithm can be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into a generating AI and have the generating AI optimize the update algorithm.

[0063] The update unit can weight the updated data based on the timing of symptom occurrence during the update process. For example, the update unit may weight the data for recently occurring symptoms. For example, the update unit may weight the updated data considering the timing of past symptom occurrences. For example, the update unit may weight the updated data based on the frequency of symptom occurrences. This allows for more appropriate updates by weighting the updated data based on the timing of symptom occurrences. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input symptom occurrence timing data into a generating AI and have the generating AI perform the weighting.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis process. For example, it can refer to the user's past medical records and perform an analysis by comparing them with the user's current symptoms. It can also analyze the user's current health status based on the user's past health checkup results. Furthermore, it can refer to the user's past medication use history and perform an analysis while considering the effects of medication. In this way, the accuracy of the analysis is improved by referring to the user's past health data.

[0066] The analysis unit can perform analyses while considering the user's lifestyle and environmental information. For example, it can consider the user's diet and exercise habits. It can also consider the user's living and working environment. Furthermore, it can consider the user's sleep patterns and stress levels. By considering the user's lifestyle and environmental information, more accurate analyses become possible.

[0067] The analysis unit can perform analyses while considering the user's geographical location. For example, it can consider the climate and environment of the user's current location. It can also consider the medical resources in the user's area. Furthermore, it can consider diseases prevalent in a specific region based on the user's geographical location. This allows for more accurate analysis by considering the user's geographical location.

[0068] The analysis unit can analyze users' social media activity during analysis and incorporate relevant information into the analysis. For example, it can estimate stress levels from users' social media posts and incorporate this into the analysis. It can also perform analysis considering the frequency of users' social media activity. Furthermore, it can analyze users' social media friendships and communication patterns and incorporate this into the analysis. As a result, more accurate analysis becomes possible by analyzing users' social media activity.

[0069] The visualization unit can adjust the level of detail of the visualization based on the importance of the symptoms. For example, it can provide detailed graphs for highly important symptoms and simplified graphs for less important symptoms. Furthermore, it can provide graphs with different colors and shapes depending on the importance of the symptoms. By adjusting the level of detail of the visualization based on the importance of the symptoms, more appropriate visualization becomes possible.

[0070] The visualization unit can apply different visualization methods depending on the symptom category during visualization. For example, it can provide a heatmap for pain symptoms, a line graph for mental symptoms, and a bar graph for physical symptoms. This allows for more appropriate visualization by applying different visualization methods according to the symptom category.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The analysis unit analyzes the user's words, facial expressions, and voice. For example, it analyzes the user's words to identify emotions and symptoms. It analyzes the user's facial expressions to determine the degree of pain or discomfort. It analyzes the user's voice to estimate the level of stress or fatigue. Step 2: The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. For example, it displays the intensity of pain as a graph, the level of fatigue as a color, and the degree of stress as a numerical value. Step 3: The prediction unit predicts the name and cause of the disease based on the information visualized by the visualization unit. For example, it predicts the name of the disease from the location and intensity of pain, identifies the cause of fatigue, and analyzes the factors causing stress. Step 4: The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. For example, it might suggest methods for pain relief, exercises for fatigue recovery, or relaxation methods for stress reduction. Step 5: The report generation unit generates a medical report based on the information proposed by the proposal unit. For example, the medical report may include the intensity and location of pain, the level and cause of fatigue, and the degree and factors of stress.

[0073] (Example of form 2) The Care AI agent system according to an embodiment of the present invention is a system that analyzes symptoms during times of physical and psychological distress and proposes solutions. This Care AI agent system analyzes the user's words, facial expressions, and voice, visualizes symptoms and their severity in real time, and infers the name and cause of the illness. Next, it proposes personalized solutions and provides empathetic support through a personalized AI character. This enables the generation of medical reports for healthcare institutions, allowing for rapid and appropriate treatment, and is expected to shorten consultation times and improve the accuracy of medical care. It also aims to reduce the psychological burden on patients and alleviate physical symptoms. Furthermore, reminder functions for regular online consultations, hospital visits, and medication adherence enable daily and continuous care. The Care AI agent evolves through continuous self-learning, and is constantly updated with the latest medical knowledge. This allows it to become an empathetic and supportive presence for users who feel distressed but do not want to burden their family or those around them. For example, the Care AI agent system analyzes the user's words, facial expressions, and voice. For example, the Care AI agent system analyzes the user's words to identify emotions and symptoms. Next, the Care AI agent system analyzes the user's facial expressions to determine the degree of pain or discomfort. Furthermore, the Care AI agent system analyzes the user's voice to estimate the level of stress and fatigue. This allows the Care AI agent system to comprehensively understand the user's condition and propose appropriate solutions. As a result, the Care AI agent system can analyze the user's symptoms in real time and propose appropriate solutions.

[0074] The Care AI agent system according to this embodiment comprises an analysis unit, a visualization unit, an inference unit, a suggestion unit, and a report generation unit. The analysis unit analyzes the user's words, facial expressions, and voice. For example, the analysis unit analyzes the user's words to identify emotions and symptoms. For example, the analysis unit analyzes the user's facial expressions to determine the degree of pain or discomfort. For example, the analysis unit analyzes the user's voice to infer the level of stress or fatigue. The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. For example, the visualization unit displays the intensity of pain in a graph. For example, the visualization unit indicates the level of fatigue with color. For example, the visualization unit represents the degree of stress numerically. The inference unit infers the name and cause of a disease based on the information visualized by the visualization unit. For example, the inference unit infers the name of a disease from the location and intensity of pain. For example, the inference unit identifies the cause of fatigue. For example, the inference unit analyzes the factors contributing to stress. The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. For example, the suggestion unit proposes methods for pain relief. For example, the suggestion unit proposes exercises for fatigue recovery. For example, the suggestion unit proposes relaxation methods for stress reduction. The report generation unit generates a medical report based on the information proposed by the suggestion unit. For example, the report generation unit includes information on the intensity and location of pain in the medical report. For example, the report generation unit includes information on the level and cause of fatigue in the medical report. For example, the report generation unit includes information on the degree and factors of stress in the medical report. As a result, the Care AI agent system according to the embodiment can analyze the user's words, facial expressions, and voice, visualize symptoms and their severity in real time, predict the name and cause of the illness, propose personalized improvement measures, and generate a medical report.

[0075] The analysis unit analyzes the user's words, facial expressions, and voice. For example, the analysis unit analyzes the user's words to identify emotions and symptoms. Specifically, it uses natural language processing technology to analyze the content of the user's statements and extract emotions and symptoms from keywords and context. For example, if a user says, "My head hurts," the analysis unit extracts the keyword "pain" and identifies that the pain is in the head. It also analyzes the user's facial expressions to determine the degree of pain or discomfort. For facial expression analysis, it uses image recognition technology to analyze the movement of facial muscles, eye opening and closing, and the degree to which the corners of the mouth are turned up. For example, if a user is frowning, the analysis unit will determine from the expression that they are feeling pain or discomfort. Furthermore, it analyzes the user's voice to estimate the level of stress and fatigue. For voice analysis, it uses speech recognition technology to analyze the tone, pitch, and speaking speed of the voice. For example, if a user's voice is trembling, the analysis unit will infer from the voice that they are feeling stressed. In this way, the analysis unit can collect information from multiple angles from the user's words, facial expressions, and voice to identify emotions and symptoms.

[0076] The visualization unit visualizes symptoms and their severity, such as pain, in real time based on information analyzed by the analysis unit. Specifically, it displays the user's pain intensity in a graph based on data obtained from the analysis unit. For example, it can represent pain intensity on a scale from 0 to 10 and display the change in pain intensity over time as a line graph. It also indicates the level of fatigue with color. For example, low fatigue is displayed in green, moderate fatigue in yellow, and high fatigue in red. Furthermore, it represents the degree of stress numerically. For example, it quantifies the degree of stress on a scale from 0 to 100, visually showing how much stress the user is experiencing. This allows the visualization unit to intuitively understand the user's symptoms and their severity, and supports real-time situation assessment. In addition, the visualization unit can save and compare the symptoms and severity the user has experienced in the past as a history. This makes it easier to understand changes in the user's symptoms and trends in improvement.

[0077] The prediction unit predicts the name and cause of a disease based on the information visualized by the visualization unit. Specifically, it predicts the name of a disease from the location and intensity of pain. For example, if headaches occur frequently and are above a certain intensity, it predicts the possibility of migraines. It also identifies the cause of fatigue. For example, if long hours of work or lack of sleep continue, it predicts that the cause of fatigue is overwork or lack of sleep. Furthermore, it analyzes the factors of stress. For example, if a user is experiencing stress in a particular situation or environment, it predicts that the factors are work pressure or interpersonal problems. The prediction unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely name and cause of the disease. As a result, the prediction unit can predict the name and cause of a disease with high accuracy based on the user's symptoms and severity, and provide information to take appropriate measures. In addition, the prediction unit can also utilize past data and statistical information to perform long-term risk assessment and trend analysis.

[0078] The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. Specifically, it suggests methods for pain relief. For example, in the case of a headache, it suggests applying a cold compress or getting rest. It also suggests exercise for fatigue recovery. For example, it suggests methods to reduce fatigue by doing light stretching or walking. Furthermore, it suggests relaxation methods to reduce stress. For example, it suggests methods to reduce stress by doing deep breathing or meditation. The suggestion unit can provide the most suitable improvement measures according to the user's individual circumstances and needs. In this way, the suggestion unit can help users take appropriate measures according to their symptoms and their severity, and promote improvement in their health. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions.

[0079] The report generation unit generates medical reports based on the information proposed by the proposal unit. Specifically, the medical report includes information on the intensity and location of pain. For example, it might record that the headache intensity is 7 out of 10 and the location is the frontal region. It also includes information on the level and cause of fatigue. For example, it might record that the fatigue level is 8 out of 10 and the cause is lack of sleep. Furthermore, it includes information on the degree and factors of stress. For example, it might record that the stress level is 70 out of 100 and the factor is work pressure. The report generation unit can centrally compile this information and provide it in a format that is easily understood by healthcare professionals. This allows the report generation unit to support healthcare professionals in quickly understanding the user's symptoms and their severity and providing appropriate medical care. In addition, the report generation unit can save past reports and track changes and improvements in the user's symptoms. This makes it easier for healthcare professionals to understand the user's long-term health status.

[0080] The reminder function provides reminders for regular online consultations, hospital visits, medication adherence, etc. For example, the reminder function may remind users of weekly online consultations, monthly hospital visits, or daily medication adherence. By providing reminder functions for regular online consultations, hospital visits, medication adherence, etc., daily and continuous care becomes possible. Some or all of the above-described processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input the user's schedule information into a generation AI and have the generation AI generate reminders.

[0081] The learning unit performs continuous self-learning. The learning unit performs self-learning using, for example, machine learning algorithms. The learning unit performs self-learning by, for example, setting the data update frequency. The learning unit performs self-learning based on, for example, user feedback. As a result, continuous self-learning improves the accuracy of the system and allows it to constantly update with the latest medical knowledge. Some or all of the above processes in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input user feedback data into a generating AI and have the generating AI perform self-learning.

[0082] The update unit updates the latest medical knowledge. For example, the update unit updates new treatment methods. For example, the update unit updates the latest research findings. For example, the update unit updates new drug information. By updating the latest medical knowledge, it becomes possible to provide care that is always based on the latest information. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the latest research findings data into a generating AI and have the generating AI update the medical knowledge.

[0083] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit improves the accuracy of the analysis by considering emotional fluctuations. For example, if the user is relaxed, the analysis unit performs the analysis assuming emotional stability. For example, if the user is anxious, the analysis unit performs the analysis by correcting for emotional instability. By adjusting the accuracy of the analysis based on the user's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit may refer to the user's past medical records and perform an analysis by comparing them with the user's current symptoms. For example, the analysis unit may analyze the user's current health status based on the user's past health checkup results. For example, the analysis unit may refer to the user's past medication history and perform an analysis considering the effects of medication. In this way, the accuracy of the analysis is improved by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI improve the accuracy of the analysis.

[0085] The analysis unit can perform analysis while considering the user's lifestyle and environmental information. For example, the analysis unit can perform analysis while considering the user's diet and exercise habits. For example, the analysis unit can perform analysis while considering the user's living environment and work environment. For example, the analysis unit can perform analysis while considering the user's sleep patterns and stress levels. This allows for more accurate analysis by considering the user's lifestyle and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0087] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can perform analysis while considering the climate and environment of the user's current location. For example, the analysis unit can perform analysis while considering the medical resources in the area where the user lives. For example, the analysis unit can perform analysis while considering diseases prevalent in a particular area based on the user's geographical location. This makes it possible to perform more accurate analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. 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.

[0088] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can estimate the stress level from the user's social media posts and reflect it in the analysis. For example, the analysis unit can perform the analysis considering the frequency of the user's activity on social media. For example, the analysis unit can analyze the user's friendships and communication patterns on social media and reflect them in the analysis. This makes it possible to perform a more accurate analysis by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the analysis.

[0089] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on the estimated emotions. For example, if the user is stressed, the visualization unit provides a simple and highly visible graph. If the user is relaxed, the visualization unit provides a graph with detailed data. If the user is anxious, the visualization unit provides a graph with reassuring colors. By adjusting the visualization's presentation based on the user's emotions, more appropriate visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0090] The visualization unit can adjust the level of detail of the visualization based on the importance of the symptoms during visualization. For example, the visualization unit provides a detailed graph for symptoms of high importance. For example, the visualization unit provides a simplified graph for symptoms of low importance. For example, the visualization unit provides graphs of different colors and shapes depending on the importance of the symptoms. This allows for more appropriate visualization by adjusting the level of detail of the visualization based on the importance of the symptoms. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom importance data into a generating AI and have the generating AI adjust the level of detail.

[0091] The visualization unit can apply different visualization methods depending on the symptom category during visualization. For example, the visualization unit provides a heatmap for pain symptoms, a line graph for mental symptoms, and a bar graph for physical symptoms. By applying different visualization methods depending on the symptom category, more appropriate visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom category data into a generating AI and have the generating AI apply the visualization method.

[0092] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the user is in a hurry, the visualization unit provides a short, concise graph. For example, if the user is relaxed, the visualization unit provides a longer graph with detailed explanations. For example, if the user is excited, the visualization unit provides a graph with visually stimulating effects. By adjusting the length of the visualization based on the user's emotions, more appropriate visualizations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the length.

[0093] The visualization unit can determine the priority of visualization based on the timing of symptom occurrence during visualization. For example, the visualization unit may prioritize the visualization of recently occurring symptoms. For example, the visualization unit may determine the order of visualization by considering the timing of past symptom occurrences. For example, the visualization unit may determine the priority of visualization based on the frequency of symptom occurrences. This allows for more appropriate visualization by determining the priority of visualization based on the timing of symptom occurrences. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may input symptom occurrence timing data into a generating AI and have the generating AI determine the priority.

[0094] The visualization unit can adjust the order of visualization based on the relevance of symptoms during visualization. For example, the visualization unit may visualize highly relevant symptoms together. For example, the visualization unit may visualize less relevant symptoms individually. For example, the visualization unit may provide graphs of different colors and shapes based on the relevance of symptoms. This allows for more appropriate visualization by adjusting the order of visualization based on the relevance of symptoms. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input symptom relevance data into a generating AI and have the generating AI adjust the order.

[0095] The inference unit can estimate the user's emotions and adjust the inference criteria based on the estimated user emotions. For example, if the user is stressed, the inference unit adjusts the inference criteria to account for emotional fluctuations. For example, if the user is relaxed, the inference unit makes inferences based on the assumption of emotional stability. For example, if the user is anxious, the inference unit makes inferences by correcting for emotional instability. This allows for more accurate inferences by adjusting the inference criteria 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input user emotion data into the generative AI and have the generative AI adjust the inference criteria.

[0096] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of symptoms. For example, the prediction unit analyzes the interrelationships of multiple symptoms to predict the name of a disease. For example, the prediction unit makes predictions by considering the order in which symptoms occur. For example, the prediction unit makes predictions based on the intensity and frequency of symptoms. This improves the accuracy of the predictions by considering the interrelationships of symptoms. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationship data of symptoms into a generating AI and have the generating AI improve the accuracy of the predictions.

[0097] The prediction unit can perform predictions while considering the user's attribute information. For example, the prediction unit can make predictions while considering the user's age and gender. For example, the prediction unit can make predictions while considering the user's occupation and lifestyle. For example, the prediction unit can make predictions while considering the user's medical history and family history. This makes it possible to make more accurate predictions by considering the user's attribute information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's attribute information into a generating AI and have the generating AI perform the prediction.

[0098] The prediction unit can estimate the user's emotions and adjust the display order of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display order. For example, if the user is relaxed, the prediction unit provides a display order that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display order that gets straight to the point. By adjusting the display order of the prediction results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display order.

[0099] The prediction unit can perform predictions while considering the geographical distribution of symptoms. For example, the prediction unit can make predictions by considering diseases prevalent in a particular region. For example, the prediction unit can make predictions by considering the medical resources in the user's area of ​​residence. For example, the prediction unit can make predictions by considering symptoms that are likely to occur in a particular region based on the user's geographical location. This allows for more accurate predictions by considering the geographical distribution of symptoms. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical distribution data of symptoms into a generating AI and have the generating AI perform the prediction.

[0100] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit makes inferences by referring to the latest medical papers. For example, the inference unit makes inferences based on past case reports. For example, the inference unit makes inferences by referring to relevant medical books. In this way, the accuracy of the inference is improved by referring to relevant literature. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature data into a generating AI and have the generating AI improve the accuracy of the inferences.

[0101] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide suggestions that include detailed information. If the user is anxious, the suggestion unit will provide suggestions in a reassuring way. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. 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 adjust the presentation.

[0102] The suggestion unit can adjust the level of detail of its suggestions based on the severity of the symptoms. For example, the suggestion unit can provide detailed suggestions for symptoms of high severity, or simplified suggestions for symptoms of low severity. For example, it can provide suggestions with different colors or shapes depending on the severity of the symptoms. By adjusting the level of detail of suggestions based on the severity of the symptoms, more appropriate suggestions can be made. 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 symptom severity data into a generating AI and have the generating AI adjust the level of detail.

[0103] The suggestion unit can apply different suggestion algorithms depending on the symptom category when making a suggestion. For example, the suggestion unit may suggest a specific treatment for pain symptoms. For example, the suggestion unit may suggest counseling for mental symptoms. For example, the suggestion unit may suggest exercise therapy for physical symptoms. By applying different suggestion algorithms depending on the symptom category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom category data into a generating AI and have the generating AI apply the suggestion algorithm.

[0104] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 length of suggestions.

[0105] The suggestion unit can determine the priority of suggestions based on the timing of symptom occurrence when making suggestions. For example, the suggestion unit may prioritize suggestions for recently occurring symptoms. For example, the suggestion unit may determine the order of suggestions by considering the timing of past symptom occurrences. For example, the suggestion unit may determine the priority of suggestions based on the frequency of symptom occurrences. This allows for more appropriate suggestions by determining the priority of suggestions based on the timing of symptom occurrences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input symptom occurrence timing data into a generating AI and have the generating AI determine the priority.

[0106] The suggestion unit can adjust the order of suggestions based on the relevance of symptoms. For example, the suggestion unit may suggest highly relevant symptoms together. For example, the suggestion unit may suggest less relevant symptoms individually. For example, the suggestion unit may provide suggestions for different colors or shapes based on the relevance of symptoms. By adjusting the order of suggestions based on the relevance of symptoms, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input symptom relevance data into a generating AI and have the generating AI adjust the order.

[0107] The report generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, the report generation unit provides a simple and easy-to-read report. For example, if the user is relaxed, the report generation unit provides a report with detailed information. For example, if the user is anxious, the report generation unit provides a report using reassuring language. By adjusting the report's presentation based on the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI, or not. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation.

[0108] The report generation unit can adjust the level of detail in a report based on the severity of the symptoms during report generation. For example, the report generation unit provides a detailed report for symptoms of high severity. For example, the report generation unit provides a simplified report for symptoms of low severity. For example, the report generation unit provides reports with different colors or shapes depending on the severity of the symptoms. By adjusting the level of detail in the report based on the severity of the symptoms, more appropriate reports can be generated. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom severity data into a generation AI and have the generation AI adjust the level of detail.

[0109] The report generation unit can apply different report generation algorithms depending on the symptom category when generating a report. For example, the report generation unit may apply a specific report generation algorithm for pain symptoms. For example, it may apply a different report generation algorithm for mental symptoms. For example, it may apply yet another report generation algorithm for physical symptoms. By applying different report generation algorithms depending on the symptom category, more appropriate reports can be generated. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom category data into a generation AI and have the generation AI apply the report generation algorithm.

[0110] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the report generation unit will provide a short, concise report. For example, if the user is relaxed, the report generation unit will provide a longer report with detailed explanations. For example, if the user is excited, the report generation unit will provide a report with visually stimulating effects. By adjusting the length of the report based on the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the report.

[0111] The report generation unit can determine the priority of reports based on the timing of symptom occurrence when generating reports. For example, the report generation unit may prioritize the inclusion of recently occurring symptoms in the report. For example, the report generation unit may determine the order of reports by considering the timing of past symptom occurrences. For example, the report generation unit may determine the priority of reports based on the frequency of symptom occurrences. This allows for more appropriate reports by determining the priority of reports based on the timing of symptom occurrences. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom occurrence timing data into a generation AI and have the generation AI determine the priority.

[0112] The report generation unit can adjust the order of reports based on the relevance of symptoms during report generation. For example, the report generation unit may group highly relevant symptoms together in a report. For example, the report generation unit may include less relevant symptoms individually in a report. For example, the report generation unit may provide reports with different colors or shapes based on the relevance of symptoms. This allows for more appropriate reports by adjusting the order of reports based on the relevance of symptoms. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input symptom relevance data into a generation AI and have the generation AI adjust the order.

[0113] The reminder unit can estimate the user's emotions and adjust the reminder notification method based on the estimated emotions. For example, if the user is stressed, the reminder unit provides a simple and highly visible notification method. If the user is relaxed, the reminder unit provides a notification method that includes detailed information. If the user is anxious, the reminder unit provides a reassuring notification method. By adjusting the reminder notification method based on the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI adjust the notification method.

[0114] The reminder unit can select the optimal notification method by referring to the user's past behavior history when sending a reminder notification. For example, the reminder unit selects the optimal notification method based on the user's past behavior history. For example, the reminder unit analyzes the user's past behavior patterns and selects the optimal notification timing. For example, the reminder unit refers to the user's past behavior history and selects the optimal notification content. In this way, the optimal notification method can be selected by referring to the user's past behavior history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the user's past behavior history data into a generating AI and have the generating AI select the notification method.

[0115] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will prioritize important reminders. If the user is relaxed, the reminder unit will notify all reminders equally. If the user is anxious, the reminder unit will prioritize reminders that provide reassurance. By prioritizing reminders based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI determine the priorities.

[0116] The reminder unit can select the optimal notification method when sending a reminder notification, taking into account the user's device information. For example, if the user is using a smartphone, the reminder unit provides a notification method that matches the screen size. For example, if the user is using a tablet, the reminder unit provides a notification method optimized for a larger screen. For example, if the user is using a smartwatch, the reminder unit provides a concise and highly visible notification method. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's device information into a generating AI and have the generating AI select the notification method.

[0117] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit will prioritize learning data related to stress reduction. For example, if the user is relaxed, the learning unit will prioritize learning data to maintain that relaxed state. For example, if the user is anxious, the learning unit will prioritize learning data related to anxiety reduction. This allows for more appropriate learning by selecting training data 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI select the training data.

[0118] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI optimize the learning algorithm.

[0119] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the user's burden. For example, if the user is relaxed, the learning unit can increase the learning frequency to improve the accuracy of the data. For example, if the user is anxious, the learning unit can adjust the learning frequency to reduce the user's anxiety. This allows for more appropriate learning by adjusting the learning 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0120] The learning unit can weight the training data based on the timing of symptom occurrence during training. For example, the learning unit can train by weighting data of recently occurring symptoms. For example, the learning unit can weight the training data by considering the timing of past symptom occurrences. For example, the learning unit can weight the training data based on the frequency of symptom occurrences. This allows for more appropriate training by weighting the training data based on the timing of symptom occurrences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input symptom occurrence timing data into a generating AI and have the generating AI perform the weighting.

[0121] The update unit can estimate the user's emotions and select update data based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating data related to stress reduction. For example, if the user is relaxed, the update unit will update data to maintain that relaxed state. For example, if the user is anxious, the update unit will prioritize updating data related to anxiety reduction. This allows for more appropriate updates by selecting update data based on the user's emotions. Emotion estimation can be adjusted based on, for example, an emotion engine or generated AY to control the update frequency. For example, if the user is stressed, the update unit will reduce the update frequency to lessen the user's burden. For example, if the user is relaxed, the update unit will increase the update frequency to improve data accuracy. For example, if the user is anxious, the update unit will adjust the update frequency to reduce the user's anxiety. This allows for more appropriate updates by adjusting the update frequency based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generated AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the update unit may be performed using AI, or not using AI. For example, the update unit can input user sentiment data into the generating AI and have the generating AI adjust the update frequency.

[0122] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit can select the optimal update algorithm based on past update data. For example, the update unit can analyze past update data and adjust the parameters of the update algorithm. For example, the update unit can refer to past update data to constantly update the latest medical knowledge. This allows it to become a supportive and empathetic presence for users who are suffering but do not want to burden their family or those around them.

[0123] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit selects the optimal update algorithm based on past update data. For example, the update unit analyzes past update data and adjusts the parameters of the update algorithm. For example, the update unit improves the accuracy of the update algorithm by referring to past update data. In this way, the update algorithm can be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into a generating AI and have the generating AI optimize the update algorithm.

[0124] The update unit can weight the updated data based on the timing of symptom occurrence during the update process. For example, the update unit may weight the data for recently occurring symptoms. For example, the update unit may weight the updated data considering the timing of past symptom occurrences. For example, the update unit may weight the updated data based on the frequency of symptom occurrences. This allows for more appropriate updates by weighting the updated data based on the timing of symptom occurrences. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input symptom occurrence timing data into a generating AI and have the generating AI perform the weighting.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the accuracy of the analysis can be improved by taking emotional fluctuations into account. If the user is relaxed, the analysis can be performed assuming emotional stability. Furthermore, if the user is anxious, the analysis can be performed while correcting for emotional instability. In this way, by adjusting the accuracy of the analysis based on the user's emotions, more accurate analysis becomes possible.

[0127] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis process. For example, it can refer to the user's past medical records and perform an analysis by comparing them with the user's current symptoms. It can also analyze the user's current health status based on the user's past health checkup results. Furthermore, it can refer to the user's past medication use history and perform an analysis while considering the effects of medication. In this way, the accuracy of the analysis is improved by referring to the user's past health data.

[0128] The analysis unit can perform analyses while considering the user's lifestyle and environmental information. For example, it can consider the user's diet and exercise habits. It can also consider the user's living and working environment. Furthermore, it can consider the user's sleep patterns and stress levels. By considering the user's lifestyle and environmental information, more accurate analyses become possible.

[0129] 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, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible.

[0130] The analysis unit can perform analyses while considering the user's geographical location. For example, it can consider the climate and environment of the user's current location. It can also consider the medical resources in the user's area. Furthermore, it can consider diseases prevalent in a specific region based on the user's geographical location. This allows for more accurate analysis by considering the user's geographical location.

[0131] The analysis unit can analyze users' social media activity during analysis and incorporate relevant information into the analysis. For example, it can estimate stress levels from users' social media posts and incorporate this into the analysis. It can also perform analysis considering the frequency of users' social media activity. Furthermore, it can analyze users' social media friendships and communication patterns and incorporate this into the analysis. As a result, more accurate analysis becomes possible by analyzing users' social media activity.

[0132] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible graph. If the user is relaxed, it can provide a graph with detailed data. Furthermore, if the user is anxious, it can provide a graph with reassuring colors. By adjusting the visualization's presentation based on the user's emotions, more appropriate visualization becomes possible.

[0133] The visualization unit can adjust the level of detail of the visualization based on the importance of the symptoms. For example, it can provide detailed graphs for highly important symptoms and simplified graphs for less important symptoms. Furthermore, it can provide graphs with different colors and shapes depending on the importance of the symptoms. By adjusting the level of detail of the visualization based on the importance of the symptoms, more appropriate visualization becomes possible.

[0134] The visualization unit can apply different visualization methods depending on the symptom category during visualization. For example, it can provide a heatmap for pain symptoms, a line graph for mental symptoms, and a bar graph for physical symptoms. This allows for more appropriate visualization by applying different visualization methods according to the symptom category.

[0135] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on those emotions. For example, if the user is in a hurry, a short, concise graph can be provided. If the user is relaxed, a longer graph with detailed explanations can be provided. Furthermore, if the user is excited, a graph with visually stimulating effects can be provided. By adjusting the length of the visualization based on the user's emotions, more appropriate visualizations can be achieved.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The analysis unit analyzes the user's words, facial expressions, and voice. For example, it analyzes the user's words to identify emotions and symptoms. It analyzes the user's facial expressions to determine the degree of pain or discomfort. It analyzes the user's voice to estimate the level of stress or fatigue. Step 2: The visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the analysis unit. For example, it displays the intensity of pain as a graph, the level of fatigue as a color, and the degree of stress as a numerical value. Step 3: The prediction unit predicts the name and cause of the disease based on the information visualized by the visualization unit. For example, it predicts the name of the disease from the location and intensity of pain, identifies the cause of fatigue, and analyzes the factors causing stress. Step 4: The suggestion unit proposes personalized improvement measures based on the information inferred by the prediction unit. For example, it might suggest methods for pain relief, exercises for fatigue recovery, or relaxation methods for stress reduction. Step 5: The report generation unit generates a medical report based on the information proposed by the proposal unit. For example, the medical report may include the intensity and location of pain, the level and cause of fatigue, and the degree and factors of stress.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the analysis unit, visualization unit, prediction unit, proposal unit, report generation unit, reminder unit, learning unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the user's words, facial expressions, and voice using the camera 42 and microphone 38B of the smart device 14, and identifies emotions and symptoms using the control unit 46A. The visualization unit displays the intensity of pain and the level of fatigue using the display 40A of the smart device 14. The prediction unit predicts the name of the disease and its cause using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes personalized improvement measures using the identification processing unit 290 of the data processing unit 12. The report generation unit generates a medical report using the identification processing unit 290 of the data processing unit 12. The reminder unit provides reminder functions for regular online consultations, hospital visits, medication, etc., using the control unit 46A of the smart device 14. The learning unit performs continuous self-learning, for example, through the specific processing unit 290 of the data processing device 12. The update unit updates the latest medical knowledge, for example, through the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the analysis unit, visualization unit, prediction unit, proposal unit, report generation unit, reminder unit, learning unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the user's words, facial expressions, and voice using the camera 42 and microphone 238 of the smart glasses 214, and identifies emotions and symptoms using the control unit 46A. The visualization unit displays, for example, the intensity of pain or the level of fatigue using the display of the smart glasses 214. The prediction unit predicts the name of the disease and its cause using, for example, the identification processing unit 290 of the data processing unit 12. The proposal unit proposes personalized improvement measures using, for example, the identification processing unit 290 of the data processing unit 12. The report generation unit generates a medical report using, for example, the identification processing unit 290 of the data processing unit 12. The reminder unit provides reminder functions for regular online consultations, hospital visits, medication, etc., using, for example, the control unit 46A of the smart glasses 214. The learning unit performs continuous self-learning, for example, through the specific processing unit 290 of the data processing device 12. The update unit updates the latest medical knowledge, for example, through the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the analysis unit, visualization unit, prediction unit, proposal unit, report generation unit, reminder unit, learning unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the user's words, facial expressions, and voice using the camera 42 and microphone 238 of the headset terminal 314, and identifies emotions and symptoms using the control unit 46A. The visualization unit displays the intensity of pain and the level of fatigue using the display 343 of the headset terminal 314. The prediction unit predicts the name of the disease and its cause using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes personalized improvement measures using the identification processing unit 290 of the data processing unit 12. The report generation unit generates a medical report using the identification processing unit 290 of the data processing unit 12. The reminder unit provides reminder functions for regular online consultations, hospital visits, medication, etc., using the control unit 46A of the headset terminal 314. The learning unit performs continuous self-learning, for example, through the specific processing unit 290 of the data processing device 12. The update unit updates the latest medical knowledge, for example, through the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the analysis unit, visualization unit, prediction unit, proposal unit, report generation unit, reminder unit, learning unit, and update unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the user's words, facial expressions, and voice using the camera 42 and microphone 238 of the robot 414, and identifies emotions and symptoms using the control unit 46A. The visualization unit displays, for example, the intensity of pain and the level of fatigue using the display of the robot 414. The prediction unit predicts the name of the disease and its cause using, for example, the identification processing unit 290 of the data processing unit 12. The proposal unit proposes personalized improvement measures using, for example, the identification processing unit 290 of the data processing unit 12. The report generation unit generates a medical report using, for example, the identification processing unit 290 of the data processing unit 12. The reminder unit provides reminder functions for regular online consultations, hospital visits, medication, etc., using, for example, the control unit 46A of the robot 414. The learning unit performs continuous self-learning, for example, through the specific processing unit 290 of the data processing device 12. The update unit updates the latest medical knowledge, for example, through the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) An analysis unit that analyzes the user's words, facial expressions, and voice, A visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the aforementioned analysis unit. An inference unit that infers the name of the disease and its cause based on the information visualized by the visualization unit, A proposal unit proposes personalized improvement measures based on the information inferred by the aforementioned prediction unit, The system includes a report generation unit that generates a medical report based on the information proposed by the proposal unit. A system characterized by the following features. (Note 2) It includes a reminder section that provides reminders for regular online consultations, hospital visits, medication adherence, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a learning department for continuous self-study. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with an update department to keep up-to-date with the latest medical knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During the analysis, the user's lifestyle and environmental information are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned visualization unit, When visualizing, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned visualization unit, When visualizing, different visualization methods are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing, prioritize visualizations based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, When visualizing, adjust the order of visualizations based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned inference unit, It estimates the user's emotions and adjusts the estimation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned inference unit, When making predictions, consider the interrelationships between symptoms to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inference unit, When making predictions, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inference unit, It estimates the user's emotions and adjusts the display order of the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inference unit, When making estimations, the geographical distribution of symptoms should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inference unit, When making inferences, refer to relevant literature to improve the accuracy of the inferences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a suggestion, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 29) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The report generation unit, When generating a report, adjust the level of detail in the report based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 31) The report generation unit, When generating reports, different report generation algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The report generation unit, It estimates the user's sentiment and adjusts the report length based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The report generation unit, When generating reports, prioritize reports based on when the symptoms occurred. The system described in Appendix 1, characterized by the features described herein. (Note 34) The report generation unit, When generating reports, the order of reports is adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 35) The reminder unit is, It estimates the user's emotions and adjusts how reminders are notified based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The reminder unit is, When sending reminder notifications, the system selects the most suitable notification method by referring to the user's past behavior history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The reminder unit is, When sending reminder notifications, the system selects the optimal notification method based on the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned learning unit, During training, the training data is weighted based on the timing of symptom onset. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned update unit is, The system estimates the user's emotions and selects update data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned update unit is, During updates, the update algorithm is optimized by referring to past update data. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned update unit is, It estimates user sentiment and adjusts the update frequency based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned update unit is, During updates, update data is weighted based on when the symptoms first appeared. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0210] 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. An analysis unit that analyzes the user's words, facial expressions, and voice, A visualization unit visualizes symptoms and their severity, such as pain, in real time based on the information analyzed by the aforementioned analysis unit. An inference unit that infers the name of the disease and its cause based on the information visualized by the visualization unit, A proposal unit proposes personalized improvement measures based on the information inferred by the aforementioned prediction unit, The system includes a report generation unit that generates a medical report based on the information proposed by the proposal unit. A system characterized by the following features.

2. It includes a reminder section that provides reminders for regular online consultations, hospital visits, medication adherence, etc. The system according to feature 1.

3. It has a learning department for continuous self-study. The system according to feature 1.

4. Equipped with an update department to keep up-to-date with the latest medical knowledge. The system according to feature 1.

5. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the user's past health data. The system according to feature 1.

7. The aforementioned analysis unit, During the analysis, the user's lifestyle and environmental information are taken into consideration. The system according to feature 1.

8. 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 according to feature 1.

9. The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system according to feature 1.

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