System

Abstract

To provide a system that provides advice considering basic diagnosis based on a user's symptom and the history of past health and life style.SOLUTION: A system comprises a receiving unit, a diagnosis unit, a recommendation unit, and a setting unit. The receiving unit receives input of a symptom from a user. The diagnosis unit carries out diagnosis on the basis of information received by the receiving unit and the history of the user's past health. The recommendation unit recommends consultation with an expert on the basis of a result of diagnosis obtained by the diagnosis unit. The setting unit sets a health check and a reminder.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide a basic diagnosis based on a user's symptoms or advice that takes into account past health history and lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a basic diagnosis based on the user's symptoms and advice that takes into account the user's past health history and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a diagnosis unit, a recommendation unit, and a setting unit. The reception unit receives input of symptoms from a user. The diagnosis unit performs a diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The recommendation unit recommends consulting a specialist based on the diagnosis result obtained by the diagnosis unit. The setting unit sets health checks and reminders. Effect of the Invention

[0007] The system according to the embodiment can provide a preliminary diagnosis based on the user's symptoms and advice that takes into account the user's past health history and lifestyle habits. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The AI ​​assistant system for health checkups according to an embodiment of the present invention is a system that supports a basic diagnosis based on the input of symptoms from a user. In this system, the user inputs his / her own symptoms, and the AI ​​performs a basic diagnosis by taking into account the information and the user's past health history and lifestyle habits. Based on the diagnosis results, the system recommends consulting a specialist if necessary. It is also possible to set regular health checks and reminders. For example, the user inputs specific symptoms such as "I have a headache" or "I can't stop coughing." This information is input to the AI. The AI ​​then analyzes the input information. The AI ​​performs a basic diagnosis by taking into account the user's past health history and lifestyle habits. For example, it considers cases where the user has had similar symptoms in the past or where lifestyle habits may be the cause. Based on the diagnosis results, the AI ​​recommends consulting a specialist. For example, if the symptoms are severe or if self-diagnosis is difficult to handle, it recommends consulting a specialist. At this time, a method of accessing a specialist is also provided. In addition, it is also possible to set regular health checks and reminders. The AI ​​optimizes the timing of reminders based on the user's behavioral history. For example, reminders for regular health checks and reminders for when to take medicine can be set. This mechanism allows users to always be aware of their own health condition and take appropriate measures. It can also diagnose region-specific diseases and health risks. For example, region-specific diseases and health risks can be diagnosed using the user's geographic location information. In addition, it is possible to recommend experts based on the reviews and ratings of experts. This allows access to reliable experts. Finally, the diagnosis is made taking into account the user's medication history. This allows for diagnosis that takes into account drug side effects and interactions. As a result, the AI ​​assistant system for health checkups can make a basic diagnosis based on the user's symptoms input, recommend consultations with experts, and set regular health checks and reminders.

[0029] The AI ​​assistant system for health checkup according to the embodiment includes a reception unit, a diagnosis unit, a recommendation unit, and a setting unit. The reception unit receives input of symptoms from a user. The input of symptoms from the user includes, but is not limited to, for example, headache, stomachache, fever, etc. The reception unit receives, for example, the symptoms input by the user in text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives it as a symptom. The diagnosis unit performs a basic diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnosis unit considers, for example, the user's past diagnosis results, treatment history, and health history such as past illnesses. The diagnosis unit also considers the user's lifestyle habits such as eating habits, exercise habits, and sleep patterns. The diagnosis unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnosis unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. The recommendation unit recommends consulting a specialist based on the diagnosis result obtained by the diagnosis unit. For example, when symptoms are severe or when self-diagnosis is difficult to handle, the recommendation unit recommends consulting a specialist. The recommendation unit also provides a method for accessing a specialist. For example, the recommendation unit provides a means for accessing a specialist by a method such as a telephone call, email, or video call. The setting unit sets regular health checks and reminders. The setting unit optimizes the timing of reminders based on, for example, the user's behavior history. For example, the setting unit analyzes the user's past behavior history and suggests the optimal timing of reminders. The setting unit also customizes the timing of reminders based on the user's current living situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed. As a result, the AI ​​assistant system for health checkups according to the embodiment can perform a rudimentary diagnosis based on the user's symptom input, recommend consulting a specialist, and set regular health checks and reminders.

[0030] The reception unit receives input of symptoms from the user. Examples of symptoms input from the user include, but are not limited to, headache, stomachache, and fever. For example, the reception unit receives the symptoms input by the user in text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives the symptoms as symptoms. Specifically, the voice recognition technology uses a voice model using deep learning, and the user's speech can be converted into text with high accuracy. This allows the user to convey the symptoms in a natural conversational format without keyboard input. Furthermore, the reception unit also has a function of analyzing the user's input content and automatically collecting related additional information. For example, if the user inputs "headache," the reception unit automatically generates additional questions such as "frequency of headache" and "intensity of pain" and presents them to the user. This allows more detailed symptom information to be collected. The reception unit can also support multiple languages ​​and can support users who speak different languages. For example, it can accept input in multiple languages, such as English, Spanish, and Chinese, and convert the input into text appropriately. This enables the reception unit to flexibly respond to a wide range of users and collect accurate symptom information.

[0031] The diagnostic unit performs a preliminary diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnostic unit considers, for example, the user's past diagnostic results, treatment history, and health history such as past illnesses. The diagnostic unit also considers the user's lifestyle habits such as eating habits, exercise habits, and sleep patterns. The diagnostic unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnostic unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. Specifically, the neural network learns past diagnostic data and models the relationship between the symptoms and the diagnostic results. This allows the diagnostic unit to present the most likely diagnostic results for the symptoms input by the user. The diagnostic unit can also analyze the user's lifestyle data and identify the cause of the symptoms and factors that worsen them. For example, the diagnostic unit analyzes the user's meal records and exercise records to evaluate whether specific ingredients or lack of exercise are affecting the symptoms. Furthermore, the diagnostic unit can compare the user's past diagnostic results with the current symptoms and evaluate the progress of the symptoms and the effectiveness of treatment. This allows the diagnosis unit to provide the user with appropriate advice and treatment guidelines.

[0032] The recommendation unit recommends consulting with a specialist based on the diagnosis result obtained by the diagnosis unit. For example, when symptoms are severe or self-diagnosis is difficult, the recommendation unit recommends consulting with a specialist. The recommendation unit also provides a method for accessing a specialist. For example, the recommendation unit provides a means for accessing a specialist by telephone, email, video call, or other methods. Specifically, the recommendation unit automatically selects a specialist best suited to the user's symptoms and provides the contact information. The recommendation unit can also automatically make a reservation for a consultation with a specialist based on the user's schedule. For example, when a user inputs a time period when the user is free, the recommendation unit checks the time period against the schedule of the specialist and suggests the best appointment time. The recommendation unit also provides necessary information in advance so that the user can smoothly consult with the specialist. For example, the recommendation unit lists documents that the user should bring with them and questions that the user should prepare in advance, and notifies the user of the same. In this way, the recommendation unit supports the user so that the user can efficiently consult with a specialist, enabling the user to receive appropriate medical services.

[0033] The setting unit sets periodic health checks and reminders. For example, the setting unit optimizes the timing of reminders based on the user's behavior history. For example, the setting unit analyzes the user's past behavior history and suggests the optimal timing of reminders. The setting unit also customizes the timing of reminders based on the user's current living situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed. Specifically, the setting unit works with the user's calendar app and task management app to grasp the user's schedule in real time. As a result, when the user has an important meeting or appointment, the reminder notification is reduced, and when the user is relaxed, a detailed health check reminder is sent. The setting unit can also customize the contents of the reminder based on the user's health condition and past diagnosis results. For example, a reminder to measure blood pressure regularly is sent to a user who has been diagnosed with high blood pressure in the past, and a reminder to measure blood sugar level is sent to a user who has been diagnosed with diabetes in the past. Furthermore, the setting unit can collect feedback from the user and continuously improve the contents and timing of the reminder, thereby supporting the user's health management and encouraging regular health checks.

[0034] The diagnosis unit may include a database based on past health history or lifestyle habits. The database may include, for example, the user's past diagnosis results, treatment history, medical history, and other health history, but is not limited to such examples. The diagnosis unit may, for example, store the user's past diagnosis results in a database and refer to it as necessary. The diagnosis unit may also store data on the user's lifestyle habits in a database and utilize it at the time of diagnosis. For example, the diagnosis unit may store data such as the user's eating habits, exercise habits, and sleep patterns in a database and take them into consideration at the time of diagnosis. As a result, the accuracy of diagnosis is improved by using a database based on the past health history and lifestyle habits. Some or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit may input the user's past health history data into the generation AI and cause the generation AI to execute improvement of the accuracy of diagnosis.

[0035] The recommendation unit can provide a method of accessing the expert. The access method includes, but is not limited to, for example, telephone, email, video call, and the like. For example, the recommendation unit provides a telephone number so that the user can consult with the expert by telephone. The recommendation unit can also provide an email address so that the user can consult with the expert by email. Furthermore, the recommendation unit can also provide a video call link so that the user can consult with the expert using a video call. In this way, by providing a method of accessing the expert, the user can quickly consult with the expert. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's symptom data into the generation AI and cause the generation AI to execute a suggestion of an appropriate access method.

[0036] The diagnostic unit can analyze the symptoms of the user using a machine learning algorithm to improve the diagnostic accuracy. The machine learning algorithm includes, for example, a neural network, a support vector machine, and the like, but is not limited to such examples. The diagnostic unit can analyze the symptoms of the user using, for example, a neural network to improve the diagnostic accuracy. The diagnostic unit can also analyze the symptoms of the user using a support vector machine. Furthermore, the diagnostic unit can analyze the symptoms of the user using other machine learning algorithms to improve the diagnostic accuracy. For example, the diagnostic unit uses a neural network to input the symptom data of the user and outputs a diagnostic result. The support vector machine classifies the symptom data of the user and outputs the diagnostic result. As a result, the accuracy of the diagnosis is improved by using the machine learning algorithm. A part or all of the above-mentioned processing in the diagnostic unit is performed using a generation AI. For example, the diagnostic unit can input the symptom data of the user to the generation AI and cause the generation AI to output the diagnostic result.

[0037] The setting unit may have a function of adjusting the timing of the reminder based on the user's behavior history. The behavior history may include, but is not limited to, a daily activity log, an app usage history, and the like. For example, the setting unit may analyze the user's past behavior history and propose an optimal reminder timing. The setting unit may also adjust the frequency of the reminder based on the user's behavior history. Furthermore, the setting unit may customize the contents of the reminder based on the user's behavior history. For example, the setting unit may analyze the user's past behavior history and set a reminder for a specific time period. This allows for more effective reminder setting by optimizing the timing of the reminder based on the user's behavior history. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit may input the user's behavior history data to the generation AI and cause the generation AI to optimize the timing of the reminder.

[0038] The diagnosis unit can further use the geographical location information of the user to diagnose region-specific diseases and health risks. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. The diagnosis unit, for example, uses the geographical location information of the user to diagnose region-specific diseases and health risks. The diagnosis unit can also collect regional health information based on the geographical location information of the user and use it for diagnosis. Furthermore, the diagnosis unit can predict regional health risks using the geographical location information of the user. For example, the diagnosis unit inputs the geographical location information of the user and outputs region-specific diseases and health risks. As a result, region-specific diseases and health risks can be diagnosed using the geographical location information of the user. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute a diagnosis of region-specific diseases and health risks.

[0039] The recommendation unit can recommend an expert based on the review and evaluation of the expert. The review and evaluation include, for example, user feedback, evaluation score, etc., but are not limited to such examples. The recommendation unit recommends an expert based on, for example, user feedback. The recommendation unit can also recommend an expert based on the evaluation score. Furthermore, the recommendation unit can recommend an expert using other evaluation criteria. For example, the recommendation unit takes the user's feedback data as input and outputs a recommendation result of an expert. The evaluation score is calculated based on the expert's past performance and the user's evaluation. As a result, by recommending an expert based on the expert's review and evaluation, it becomes possible to access a highly reliable expert. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's feedback data to the generation AI and cause the generation AI to execute the recommendation of an expert.

[0040] The diagnostic unit can further use the user's drug taking history to make a diagnosis. The drug taking history includes, for example, a list of prescription drugs, a drug taking schedule, etc., but is not limited to such examples. The diagnostic unit makes a diagnosis based on, for example, a list of prescription drugs of the user. The diagnostic unit can also make a diagnosis based on the user's drug taking schedule. Furthermore, the diagnostic unit can make a diagnosis taking into account side effects and interactions of drugs. For example, the diagnostic unit inputs the user's drug taking history data and outputs a diagnosis result. As a result, by using the user's drug taking history, it is possible to make a diagnosis taking into account side effects and interactions of drugs. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's drug taking history data to the generation AI and output the diagnosis result to the generation AI.

[0041] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. For example, the reception unit receives the user's past symptom input history data as an input and outputs the optimal input method. In this way, the optimal input method can be provided to the user by analyzing the past symptom input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past symptom input history data to the generation AI and cause the generation AI to select the optimal input method.

[0042] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, preferentially displays highly relevant symptoms based on the user's current health condition. The reception unit can also emphasize specific symptoms based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter and display general symptoms based on the user's age and sex. For example, the reception unit receives the user's current health condition data as input and outputs highly relevant symptoms. As a result, more relevant symptoms can be displayed by filtering the input contents based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current health condition data to the generation AI and cause the generation AI to filter the input contents.

[0043] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, when the user is in a specific area, the reception unit preferentially displays symptoms of diseases prevalent in that area. In addition, when the user is traveling, the reception unit can also preferentially display symptoms related to health risks at the travel destination. Furthermore, when the user is at home, the reception unit can display highly relevant symptoms based on health information of the area. For example, the reception unit receives the geographical location information data of the user as an input and outputs highly relevant symptoms. As a result, symptoms related to diseases and health risks specific to the area can be preferentially input by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the preferential display of highly relevant symptoms.

[0044] The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms. The reception unit can suggest related symptoms based on, for example, health information shared by the user on social media. The reception unit can also analyze recent health trends from the user's social media activity and suggest related symptoms. The reception unit can also suggest related symptoms based on posts by health experts the user follows on social media. For example, the reception unit receives the user's social media activity data as input and outputs related symptoms. In this way, it is possible to suggest related symptoms to the user by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related symptoms.

[0045] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the symptoms at the time of diagnosis. For example, in the case of a serious symptom, the diagnostic unit provides detailed diagnostic information. In addition, in the case of a minor symptom, the diagnostic unit can also provide concise diagnostic information. Furthermore, the diagnostic unit can provide only necessary information according to the importance of the symptoms. For example, the diagnostic unit inputs the symptom data of the user and adjusts the level of detail of the diagnosis based on the importance of the symptoms. In this way, by adjusting the level of detail of the diagnosis based on the importance of the symptoms, it is possible to provide only necessary information. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the symptom data of the user to the generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0046] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category at the time of diagnosis. For example, in the case of a respiratory symptom, the diagnostic unit applies a specialized diagnostic algorithm. In addition, the diagnostic unit can also apply a different diagnostic algorithm in the case of a digestive symptom. Furthermore, the diagnostic unit can apply yet another diagnostic algorithm in the case of a nervous symptom. For example, the diagnostic unit inputs the user's symptom data and applies different diagnostic algorithms depending on the symptom category. This enables more accurate diagnosis by applying different diagnostic algorithms depending on the symptom category. Part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's symptom data to the generation AI and cause the generation AI to apply different diagnostic algorithms.

[0047] The diagnosis unit can determine the priority of diagnosis based on the occurrence time of the symptom at the time of diagnosis. For example, the diagnosis unit gives priority to diagnosing a symptom that has recently occurred. The diagnosis unit can also give priority to diagnosing a symptom that has continued for a long time. Furthermore, the diagnosis unit can adjust the priority of diagnosis according to the occurrence time of the symptom. For example, the diagnosis unit inputs the symptom data of the user and determines the priority of diagnosis based on the occurrence time of the symptom. As a result, by determining the priority of diagnosis based on the occurrence time of the symptom, more appropriate diagnosis is possible. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the symptom data of the user to the generation AI and cause the generation AI to execute the determination of the priority of diagnosis.

[0048] The diagnostic unit can adjust the order of diagnostic results based on the relevance of symptoms at the time of diagnosis. For example, the diagnostic unit preferentially displays highly relevant symptoms in the diagnostic results. The diagnostic unit can also postpone less relevant symptoms. Furthermore, the diagnostic unit can adjust the order of diagnostic results based on the relevance of symptoms. For example, the diagnostic unit inputs the symptom data of the user and adjusts the order of diagnostic results based on the relevance of symptoms. In this way, by adjusting the order of diagnostic results based on the relevance of symptoms, more relevant information can be provided. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI or may be performed without using AI. For example, the diagnostic unit inputs the symptom data of the user to the generation AI and causes the generation AI to adjust the order of diagnostic results.

[0049] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the expert at the time of recommendation. For example, in the case of an important expert, the recommendation unit provides detailed information. In addition, in the case of a general expert, the recommendation unit can also provide concise information. Furthermore, the recommendation unit can adjust the level of detail of the recommendation according to the importance of the expert. For example, the recommendation unit inputs evaluation data of the expert and adjusts the level of detail of the recommendation based on the importance of the expert. In this way, by adjusting the level of detail of the recommendation based on the importance of the expert, it is possible to provide only necessary information. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input evaluation data of the expert to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0050] The recommendation unit can apply different recommendation algorithms depending on the category of the expert when making a recommendation. For example, in the case of a medical expert, the recommendation unit applies a specialized recommendation algorithm. In addition, in the case of a health coach, the recommendation unit can also apply a different recommendation algorithm. Furthermore, in the case of a nutritionist, the recommendation unit can apply yet another recommendation algorithm. For example, the recommendation unit inputs category data of the expert and applies different recommendation algorithms depending on the category. In this way, by applying different recommendation algorithms depending on the category of the expert, a more appropriate expert can be recommended. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input category data of the expert to the generation AI and cause the generation AI to apply different recommendation algorithms.

[0051] The recommendation unit can determine the priority of recommendations based on the evaluation time of the experts at the time of recommendation. For example, the recommendation unit preferentially recommends experts who have been evaluated recently. The recommendation unit can also postpone experts who have not been evaluated for a long time. Furthermore, the recommendation unit can adjust the priority of recommendations based on the evaluation time of the experts. For example, the recommendation unit inputs evaluation time data of the experts and determines the priority of recommendations based on the evaluation time. In this way, by determining the priority of recommendations based on the evaluation time of the experts, more appropriate experts can be recommended. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input evaluation time data of the experts to the generation AI and cause the generation AI to determine the priority of recommendations.

[0052] The recommendation unit can adjust the order of the recommendation results based on the relevance of the experts at the time of recommendation. For example, the recommendation unit preferentially displays highly relevant experts in the recommendation results. The recommendation unit can also postpone less relevant experts. Furthermore, the recommendation unit can adjust the order of the recommendation results based on the relevance of the experts. For example, the recommendation unit inputs relevance data of the experts and adjusts the order of the recommendation results based on the relevance. As a result, by adjusting the order of the recommendation results based on the relevance of the experts, it is possible to recommend more relevant experts. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input relevance data of the experts to the generation AI and cause the generation AI to adjust the order of the recommendation results.

[0053] The setting unit can select an appropriate setting method by referring to the user's past behavior history when setting a reminder. For example, the setting unit analyzes the pattern of reminders set by the user in the past and proposes an optimal setting method. The setting unit can also set a reminder for a specific time period from the user's past behavior history. Furthermore, the setting unit can optimize the frequency of reminders based on the user's past behavior history. For example, the setting unit inputs the user's past behavior history data and outputs an optimal reminder setting method. In this way, by referring to the past behavior history, the optimal reminder setting method can be provided to the user. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's past behavior history data to the generation AI and cause the generation AI to select the optimal reminder setting method.

[0054] The setting unit can customize the timing of the reminder based on the user's current living situation when setting the reminder. For example, when the user is busy, the setting unit adjusts the timing of the reminder to reduce notifications. In addition, the setting unit can provide detailed reminders when the user is relaxed. Furthermore, the setting unit can optimize the timing of the reminder according to the user's living situation. For example, the setting unit inputs the user's living situation data and customizes the timing of the reminder. This enables more effective reminder setting by customizing the timing of the reminder based on the current living situation. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's living situation data to the generation AI and cause the generation AI to customize the timing of the reminder.

[0055] The setting unit can set an appropriate reminder in consideration of the geographical location information of the user when setting a reminder. For example, when the user is in a specific location, the setting unit sets a reminder related to the location. In addition, when the user is traveling, the setting unit can also set a reminder related to health risks at the travel destination. Furthermore, when the user is at home, the setting unit can set a reminder based on local health information. For example, the setting unit takes the geographical location information data of the user as an input and outputs an appropriate reminder. In this way, by considering the geographical location information, a reminder related to health risks specific to the region can be set. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the geographical location information data of the user to the generation AI and cause the generation AI to set an appropriate reminder.

[0056] When setting a reminder, the setting unit can analyze the user's social media activity and suggest the contents of the reminder. For example, the setting unit can suggest a related reminder based on health information shared by the user on social media. The setting unit can also analyze recent health trends from the user's social media activity and suggest a related reminder. Furthermore, the setting unit can suggest a related reminder based on posts by health experts the user follows on social media. For example, the setting unit inputs the user's social media activity data and outputs a related reminder. In this way, by analyzing the social media activity, a reminder related to the user can be suggested. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of the contents of the reminder.

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

[0058] An AI assistant system for health checkups can be equipped with a function to analyze a user's meal photos and evaluate the nutritional balance. For example, a user takes a photo of their meal and uploads it to the system. The analysis unit uses image recognition technology to identify ingredients and calculate the nutrient content. It can then evaluate the nutritional balance and suggest improvements to the user. It can also compare the results with the user's past meal history to analyze long-term trends in nutritional balance. This allows users to review their eating habits and strive to eat healthier meals.

[0059] An AI assistant system for health checkups can collect users' exercise data and support them in improving their exercise habits. For example, a user records exercise data using a smartwatch or fitness tracker. The system obtains data from these devices and analyzes the frequency and intensity of exercise. It can then suggest an exercise plan based on the user's goals and monitor progress. In addition, it can integrate the user's exercise data with other health data to evaluate the overall health status. This allows the user to develop effective exercise habits and maintain health.

[0060] The AI ​​assistant system for health checkups can be equipped with the function of analyzing a user's sleep data and providing advice to improve the quality of sleep. For example, a user records sleep data using a smartwatch or sleep tracker. The system obtains data from these devices and analyzes sleep patterns and quality. It can then suggest improvements based on the user's sleep environment and lifestyle habits. It can also compare the user's past sleep data and analyze long-term sleep trends. This allows the user to ensure high-quality sleep and maintain health.

[0061] The AI ​​assistant system for health checkups can be equipped with the function of monitoring the user's stress level and providing advice for stress reduction. For example, the user records stress data using a smartwatch or stress monitoring device. The system obtains data from these devices and analyzes the stress level. Then, based on the user's stress level, it can suggest relaxation methods and activities for stress reduction. In addition, it can compare with the user's past stress data and analyze long-term stress trends. This allows the user to effectively manage stress and maintain physical and mental health.

[0062] An AI assistant system for health checkups can analyze users' social media activities and predict health risks. For example, it can analyze the health information and posts shared by users on social media to identify health risks. It can then suggest preventive measures and areas for improvement to the user. In addition, it can analyze health trends from users' social media activities and provide relevant information. This allows users to understand their own health risks early and take appropriate measures.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives an input of symptoms from a user. Examples of symptoms input from a user include, but are not limited to, headache, stomachache, and fever. For example, the reception unit receives the symptoms input by the user in a text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives the text as a symptom. Step 2: The diagnosis unit performs a preliminary diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnosis unit considers the user's health history, such as past diagnosis results, treatment history, and medical history. The diagnosis unit also considers the user's lifestyle habits, such as eating habits, exercise habits, and sleep patterns. The diagnosis unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnosis unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. Step 3: The recommendation unit recommends consulting an expert based on the diagnosis result obtained by the diagnosis unit. For example, the recommendation unit recommends consulting an expert when symptoms are severe or when self-diagnosis is difficult to handle. The recommendation unit also provides a method for accessing an expert. For example, the recommendation unit provides a means for accessing an expert by telephone, email, video call, or other methods. Step 4: The setting unit sets regular health checks and reminders. The setting unit optimizes the timing of reminders based on the user's behavioral history, for example. For example, the setting unit analyzes the user's past behavioral history and suggests optimal reminder timing. The setting unit also customizes the timing of reminders based on the user's current life situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed.

[0065] (Example 2) The AI ​​assistant system for health checkups according to an embodiment of the present invention is a system that supports a basic diagnosis based on the input of symptoms from a user. In this system, the user inputs his / her own symptoms, and the AI ​​performs a basic diagnosis by taking into account the information and the user's past health history and lifestyle habits. Based on the diagnosis results, the system recommends consulting a specialist if necessary. It is also possible to set regular health checks and reminders. For example, the user inputs specific symptoms such as "I have a headache" or "I can't stop coughing." This information is input to the AI. The AI ​​then analyzes the input information. The AI ​​performs a basic diagnosis by taking into account the user's past health history and lifestyle habits. For example, it considers cases where the user has had similar symptoms in the past or where lifestyle habits may be the cause. Based on the diagnosis results, the AI ​​recommends consulting a specialist. For example, if the symptoms are severe or if self-diagnosis is difficult to handle, it recommends consulting a specialist. At this time, a method of accessing a specialist is also provided. In addition, it is also possible to set regular health checks and reminders. The AI ​​optimizes the timing of reminders based on the user's behavioral history. For example, reminders for regular health checks and reminders for when to take medicine can be set. This mechanism allows users to always be aware of their own health condition and take appropriate measures. It can also diagnose region-specific diseases and health risks. For example, region-specific diseases and health risks can be diagnosed using the user's geographic location information. In addition, it is possible to recommend experts based on the reviews and ratings of experts. This allows access to reliable experts. Finally, the diagnosis is made taking into account the user's medication history. This allows for diagnosis that takes into account drug side effects and interactions. As a result, the AI ​​assistant system for health checkups can make a basic diagnosis based on the user's symptoms input, recommend consultations with experts, and set regular health checks and reminders.

[0066] The AI ​​assistant system for health checkup according to the embodiment includes a reception unit, a diagnosis unit, a recommendation unit, and a setting unit. The reception unit receives input of symptoms from a user. The input of symptoms from the user includes, but is not limited to, for example, headache, stomachache, fever, etc. The reception unit receives, for example, the symptoms input by the user in text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives it as a symptom. The diagnosis unit performs a basic diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnosis unit considers, for example, the user's past diagnosis results, treatment history, and health history such as past illnesses. The diagnosis unit also considers the user's lifestyle habits such as eating habits, exercise habits, and sleep patterns. The diagnosis unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnosis unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. The recommendation unit recommends consulting a specialist based on the diagnosis result obtained by the diagnosis unit. For example, when symptoms are severe or when self-diagnosis is difficult to handle, the recommendation unit recommends consulting a specialist. The recommendation unit also provides a method for accessing a specialist. For example, the recommendation unit provides a means for accessing a specialist by a method such as a telephone call, email, or video call. The setting unit sets regular health checks and reminders. The setting unit optimizes the timing of reminders based on, for example, the user's behavior history. For example, the setting unit analyzes the user's past behavior history and suggests the optimal timing of reminders. The setting unit also customizes the timing of reminders based on the user's current living situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed. As a result, the AI ​​assistant system for health checkups according to the embodiment can perform a rudimentary diagnosis based on the user's symptom input, recommend consulting a specialist, and set regular health checks and reminders.

[0067] The reception unit receives input of symptoms from the user. Examples of symptoms input from the user include, but are not limited to, headache, stomachache, and fever. For example, the reception unit receives the symptoms input by the user in text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives the symptoms as symptoms. Specifically, the voice recognition technology uses a voice model using deep learning, and the user's speech can be converted into text with high accuracy. This allows the user to convey the symptoms in a natural conversational format without keyboard input. Furthermore, the reception unit also has a function of analyzing the user's input content and automatically collecting related additional information. For example, if the user inputs "headache," the reception unit automatically generates additional questions such as "frequency of headache" and "intensity of pain" and presents them to the user. This allows more detailed symptom information to be collected. The reception unit can also support multiple languages ​​and can support users who speak different languages. For example, it can accept input in multiple languages, such as English, Spanish, and Chinese, and convert the input into text appropriately. This enables the reception unit to flexibly respond to a wide range of users and collect accurate symptom information.

[0068] The diagnostic unit performs a preliminary diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnostic unit considers, for example, the user's past diagnostic results, treatment history, and health history such as past illnesses. The diagnostic unit also considers the user's lifestyle habits such as eating habits, exercise habits, and sleep patterns. The diagnostic unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnostic unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. Specifically, the neural network learns past diagnostic data and models the relationship between the symptoms and the diagnostic results. This allows the diagnostic unit to present the most likely diagnostic results for the symptoms input by the user. The diagnostic unit can also analyze the user's lifestyle data and identify the cause of the symptoms and factors that worsen them. For example, the diagnostic unit analyzes the user's meal records and exercise records to evaluate whether specific ingredients or lack of exercise are affecting the symptoms. Furthermore, the diagnostic unit can compare the user's past diagnostic results with the current symptoms and evaluate the progress of the symptoms and the effectiveness of treatment. This allows the diagnosis unit to provide the user with appropriate advice and treatment guidelines.

[0069] The recommendation unit recommends consulting with a specialist based on the diagnosis result obtained by the diagnosis unit. For example, when symptoms are severe or self-diagnosis is difficult, the recommendation unit recommends consulting with a specialist. The recommendation unit also provides a method for accessing a specialist. For example, the recommendation unit provides a means for accessing a specialist by telephone, email, video call, or other methods. Specifically, the recommendation unit automatically selects a specialist best suited to the user's symptoms and provides the contact information. The recommendation unit can also automatically make a reservation for a consultation with a specialist based on the user's schedule. For example, when a user inputs a time period when the user is free, the recommendation unit checks the time period against the schedule of the specialist and suggests the best appointment time. The recommendation unit also provides necessary information in advance so that the user can smoothly consult with the specialist. For example, the recommendation unit lists documents that the user should bring with them and questions that the user should prepare in advance, and notifies the user of the same. In this way, the recommendation unit supports the user so that the user can efficiently consult with a specialist, enabling the user to receive appropriate medical services.

[0070] The setting unit sets periodic health checks and reminders. For example, the setting unit optimizes the timing of reminders based on the user's behavior history. For example, the setting unit analyzes the user's past behavior history and suggests the optimal timing of reminders. The setting unit also customizes the timing of reminders based on the user's current living situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed. Specifically, the setting unit works with the user's calendar app and task management app to grasp the user's schedule in real time. As a result, when the user has an important meeting or appointment, the reminder notification is reduced, and when the user is relaxed, a detailed health check reminder is sent. The setting unit can also customize the contents of the reminder based on the user's health condition and past diagnosis results. For example, a reminder to measure blood pressure regularly is sent to a user who has been diagnosed with high blood pressure in the past, and a reminder to measure blood sugar level is sent to a user who has been diagnosed with diabetes in the past. Furthermore, the setting unit can collect feedback from the user and continuously improve the contents and timing of the reminder, thereby supporting the user's health management and encouraging regular health checks.

[0071] The diagnosis unit may include a database based on past health history or lifestyle habits. The database may include, for example, the user's past diagnosis results, treatment history, medical history, and other health history, but is not limited to such examples. The diagnosis unit may, for example, store the user's past diagnosis results in a database and refer to it as necessary. The diagnosis unit may also store data on the user's lifestyle habits in a database and utilize it at the time of diagnosis. For example, the diagnosis unit may store data such as the user's eating habits, exercise habits, and sleep patterns in a database and take them into consideration at the time of diagnosis. As a result, the accuracy of diagnosis is improved by using a database based on the past health history and lifestyle habits. Some or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit may input the user's past health history data into the generation AI and cause the generation AI to execute improvement of the accuracy of diagnosis.

[0072] The recommendation unit can provide a method of accessing the expert. The access method includes, but is not limited to, for example, telephone, email, video call, and the like. For example, the recommendation unit provides a telephone number so that the user can consult with the expert by telephone. The recommendation unit can also provide an email address so that the user can consult with the expert by email. Furthermore, the recommendation unit can also provide a video call link so that the user can consult with the expert using a video call. In this way, by providing a method of accessing the expert, the user can quickly consult with the expert. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's symptom data into the generation AI and cause the generation AI to execute a suggestion of an appropriate access method.

[0073] The diagnostic unit can analyze the symptoms of the user using a machine learning algorithm to improve the diagnostic accuracy. The machine learning algorithm includes, for example, a neural network, a support vector machine, and the like, but is not limited to such examples. The diagnostic unit can analyze the symptoms of the user using, for example, a neural network to improve the diagnostic accuracy. The diagnostic unit can also analyze the symptoms of the user using a support vector machine. Furthermore, the diagnostic unit can analyze the symptoms of the user using other machine learning algorithms to improve the diagnostic accuracy. For example, the diagnostic unit uses a neural network to input the symptom data of the user and outputs a diagnostic result. The support vector machine classifies the symptom data of the user and outputs the diagnostic result. As a result, the accuracy of the diagnosis is improved by using the machine learning algorithm. A part or all of the above-mentioned processing in the diagnostic unit is performed using a generation AI. For example, the diagnostic unit can input the symptom data of the user to the generation AI and cause the generation AI to output the diagnostic result.

[0074] The setting unit may have a function of adjusting the timing of the reminder based on the user's behavior history. The behavior history may include, but is not limited to, a daily activity log, an app usage history, and the like. For example, the setting unit may analyze the user's past behavior history and propose an optimal reminder timing. The setting unit may also adjust the frequency of the reminder based on the user's behavior history. Furthermore, the setting unit may customize the contents of the reminder based on the user's behavior history. For example, the setting unit may analyze the user's past behavior history and set a reminder for a specific time period. This allows for more effective reminder setting by optimizing the timing of the reminder based on the user's behavior history. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit may input the user's behavior history data to the generation AI and cause the generation AI to optimize the timing of the reminder.

[0075] The diagnosis unit can further use the geographical location information of the user to diagnose region-specific diseases and health risks. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. The diagnosis unit, for example, uses the geographical location information of the user to diagnose region-specific diseases and health risks. The diagnosis unit can also collect regional health information based on the geographical location information of the user and use it for diagnosis. Furthermore, the diagnosis unit can predict regional health risks using the geographical location information of the user. For example, the diagnosis unit inputs the geographical location information of the user and outputs region-specific diseases and health risks. As a result, region-specific diseases and health risks can be diagnosed using the geographical location information of the user. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute a diagnosis of region-specific diseases and health risks.

[0076] The recommendation unit can recommend an expert based on the review and evaluation of the expert. The review and evaluation include, for example, user feedback, evaluation score, etc., but are not limited to such examples. The recommendation unit recommends an expert based on, for example, user feedback. The recommendation unit can also recommend an expert based on the evaluation score. Furthermore, the recommendation unit can recommend an expert using other evaluation criteria. For example, the recommendation unit takes the user's feedback data as input and outputs a recommendation result of an expert. The evaluation score is calculated based on the expert's past performance and the user's evaluation. As a result, by recommending an expert based on the expert's review and evaluation, it becomes possible to access a highly reliable expert. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's feedback data to the generation AI and cause the generation AI to execute the recommendation of an expert.

[0077] The diagnostic unit can further use the user's drug taking history to make a diagnosis. The drug taking history includes, for example, a list of prescription drugs, a drug taking schedule, etc., but is not limited to such examples. The diagnostic unit makes a diagnosis based on, for example, a list of prescription drugs of the user. The diagnostic unit can also make a diagnosis based on the user's drug taking schedule. Furthermore, the diagnostic unit can make a diagnosis taking into account side effects and interactions of drugs. For example, the diagnostic unit inputs the user's drug taking history data and outputs a diagnosis result. As a result, by using the user's drug taking history, it is possible to make a diagnosis taking into account side effects and interactions of drugs. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's drug taking history data to the generation AI and output the diagnosis result to the generation AI.

[0078] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input symptoms quickly. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression. This enables more appropriate symptom input by adjusting the input interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0079] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. For example, the reception unit receives the user's past symptom input history data as an input and outputs the optimal input method. In this way, the optimal input method can be provided to the user by analyzing the past symptom input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past symptom input history data to the generation AI and cause the generation AI to select the optimal input method.

[0080] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, preferentially displays highly relevant symptoms based on the user's current health condition. The reception unit can also emphasize specific symptoms based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can filter and display general symptoms based on the user's age and sex. For example, the reception unit receives the user's current health condition data as input and outputs highly relevant symptoms. As a result, more relevant symptoms can be displayed by filtering the input contents based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current health condition data to the generation AI and cause the generation AI to filter the input contents.

[0081] The reception unit can estimate the user's emotion and determine the priority of symptoms to be input based on the estimated user's emotion. For example, when the user feels anxious, the reception unit prompts the user to input serious symptoms with priority. In addition, when the user is relaxed, the reception unit can also prompt the user to input detailed symptoms. Furthermore, when the user is in a hurry, the reception unit can also prompt the user to input only major symptoms. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. This enables more appropriate symptom input by determining the priority of symptoms according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, when the user is in a specific area, the reception unit preferentially displays symptoms of diseases prevalent in that area. In addition, when the user is traveling, the reception unit can also preferentially display symptoms related to health risks at the travel destination. Furthermore, when the user is at home, the reception unit can display highly relevant symptoms based on health information of the area. For example, the reception unit receives the geographical location information data of the user as an input and outputs highly relevant symptoms. As a result, symptoms related to diseases and health risks specific to the area can be preferentially input by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the preferential display of highly relevant symptoms.

[0083] The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms. The reception unit can suggest related symptoms based on, for example, health information shared by the user on social media. The reception unit can also analyze recent health trends from the user's social media activity and suggest related symptoms. The reception unit can also suggest related symptoms based on posts by health experts the user follows on social media. For example, the reception unit receives the user's social media activity data as input and outputs related symptoms. In this way, it is possible to suggest related symptoms to the user by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related symptoms.

[0084] The diagnostic unit can estimate the user's emotion and adjust the expression method of the diagnosis result based on the estimated user's emotion. For example, when the user feels anxious, the diagnostic unit uses an expression method that gives a sense of security. In addition, when the user is relaxed, the diagnostic unit can also use an expression method that includes detailed information. In addition, when the user is in a hurry, the diagnostic unit can use a concise expression method that focuses on the main points. For example, the diagnostic unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in the facial expression. In this way, a more appropriate diagnosis result can be provided by adjusting the expression method of the diagnosis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the symptoms at the time of diagnosis. For example, in the case of a serious symptom, the diagnostic unit provides detailed diagnostic information. In addition, in the case of a minor symptom, the diagnostic unit can also provide concise diagnostic information. Furthermore, the diagnostic unit can provide only necessary information according to the importance of the symptoms. For example, the diagnostic unit inputs the symptom data of the user and adjusts the level of detail of the diagnosis based on the importance of the symptoms. In this way, by adjusting the level of detail of the diagnosis based on the importance of the symptoms, it is possible to provide only necessary information. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the symptom data of the user to the generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0086] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category at the time of diagnosis. For example, in the case of a respiratory symptom, the diagnostic unit applies a specialized diagnostic algorithm. In addition, the diagnostic unit can also apply a different diagnostic algorithm in the case of a digestive symptom. Furthermore, the diagnostic unit can apply yet another diagnostic algorithm in the case of a nervous symptom. For example, the diagnostic unit inputs the user's symptom data and applies different diagnostic algorithms depending on the symptom category. This enables more accurate diagnosis by applying different diagnostic algorithms depending on the symptom category. Part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the user's symptom data to the generation AI and cause the generation AI to apply different diagnostic algorithms.

[0087] The diagnostic unit can estimate the user's emotion and adjust the length of the diagnostic result based on the estimated user's emotion. For example, when the user feels anxious, the diagnostic unit provides a concise diagnostic result that gives a sense of security. In addition, when the user is relaxed, the diagnostic unit can also provide a detailed diagnostic result. Furthermore, when the user is in a hurry, the diagnostic unit can also provide a short diagnostic result that covers the main points. For example, the diagnostic unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in the facial expression. In this way, by adjusting the length of the diagnostic result according to the user's emotion, a more appropriate diagnostic result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The diagnosis unit can determine the priority of diagnosis based on the occurrence time of the symptom at the time of diagnosis. For example, the diagnosis unit gives priority to diagnosing a symptom that has recently occurred. The diagnosis unit can also give priority to diagnosing a symptom that has continued for a long time. Furthermore, the diagnosis unit can adjust the priority of diagnosis according to the occurrence time of the symptom. For example, the diagnosis unit inputs the symptom data of the user and determines the priority of diagnosis based on the occurrence time of the symptom. As a result, by determining the priority of diagnosis based on the occurrence time of the symptom, more appropriate diagnosis is possible. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the symptom data of the user to the generation AI and cause the generation AI to execute the determination of the priority of diagnosis.

[0089] The diagnostic unit can adjust the order of diagnostic results based on the relevance of symptoms at the time of diagnosis. For example, the diagnostic unit preferentially displays highly relevant symptoms in the diagnostic results. The diagnostic unit can also postpone less relevant symptoms. Furthermore, the diagnostic unit can adjust the order of diagnostic results based on the relevance of symptoms. For example, the diagnostic unit inputs the symptom data of the user and adjusts the order of diagnostic results based on the relevance of symptoms. In this way, by adjusting the order of diagnostic results based on the relevance of symptoms, more relevant information can be provided. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI or may be performed without using AI. For example, the diagnostic unit inputs the symptom data of the user to the generation AI and causes the generation AI to adjust the order of diagnostic results.

[0090] The recommendation unit can estimate the user's emotion and adjust the method of recommending an expert based on the estimated user's emotion. For example, when the user feels anxious, the recommendation unit preferentially recommends a highly reliable expert. In addition, when the user is relaxed, the recommendation unit can also recommend multiple experts and provide options. Furthermore, when the user is in a hurry, the recommendation unit can recommend an expert who can respond quickly. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in the facial expression. In this way, a more appropriate expert can be recommended by adjusting the method of recommending an expert according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the expert at the time of recommendation. For example, in the case of an important expert, the recommendation unit provides detailed information. In addition, in the case of a general expert, the recommendation unit can also provide concise information. Furthermore, the recommendation unit can adjust the level of detail of the recommendation according to the importance of the expert. For example, the recommendation unit inputs evaluation data of the expert and adjusts the level of detail of the recommendation based on the importance of the expert. In this way, by adjusting the level of detail of the recommendation based on the importance of the expert, it is possible to provide only necessary information. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input evaluation data of the expert to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0092] The recommendation unit can apply different recommendation algorithms depending on the category of the expert when making a recommendation. For example, in the case of a medical expert, the recommendation unit applies a specialized recommendation algorithm. In addition, in the case of a health coach, the recommendation unit can also apply a different recommendation algorithm. Furthermore, in the case of a nutritionist, the recommendation unit can apply yet another recommendation algorithm. For example, the recommendation unit inputs category data of the expert and applies different recommendation algorithms depending on the category. In this way, by applying different recommendation algorithms depending on the category of the expert, a more appropriate expert can be recommended. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input category data of the expert to the generation AI and cause the generation AI to apply different recommendation algorithms.

[0093] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user's emotion. For example, when the user feels anxious, the recommendation unit provides a concise and reassuring recommendation. In addition, when the user is relaxed, the recommendation unit can also provide a detailed recommendation. In addition, when the user is in a hurry, the recommendation unit can also provide a short recommendation that covers the main points. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression. This allows for more appropriate recommendations by adjusting the length of the recommendation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0094] The recommendation unit can determine the priority of recommendations based on the evaluation time of the experts at the time of recommendation. For example, the recommendation unit preferentially recommends experts who have been evaluated recently. The recommendation unit can also postpone experts who have not been evaluated for a long time. Furthermore, the recommendation unit can adjust the priority of recommendations based on the evaluation time of the experts. For example, the recommendation unit inputs evaluation time data of the experts and determines the priority of recommendations based on the evaluation time. In this way, by determining the priority of recommendations based on the evaluation time of the experts, more appropriate experts can be recommended. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input evaluation time data of the experts to the generation AI and cause the generation AI to determine the priority of recommendations.

[0095] The recommendation unit can adjust the order of the recommendation results based on the relevance of the experts at the time of recommendation. For example, the recommendation unit preferentially displays highly relevant experts in the recommendation results. The recommendation unit can also postpone less relevant experts. Furthermore, the recommendation unit can adjust the order of the recommendation results based on the relevance of the experts. For example, the recommendation unit inputs relevance data of the experts and adjusts the order of the recommendation results based on the relevance. As a result, by adjusting the order of the recommendation results based on the relevance of the experts, it is possible to recommend more relevant experts. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input relevance data of the experts to the generation AI and cause the generation AI to adjust the order of the recommendation results.

[0096] The setting unit can estimate the user's emotion and adjust the reminder setting method based on the estimated user's emotion. For example, when the user is stressed, the setting unit provides a simple and intuitive reminder setting method. In addition, when the user is relaxed, the setting unit can also provide detailed reminder setting options. In addition, when the user is in a hurry, the setting unit can prioritize voice input and enable the user to quickly set a reminder. For example, the setting unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression. This enables more appropriate reminder setting by adjusting the reminder setting method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0097] The setting unit can select an appropriate setting method by referring to the user's past behavior history when setting a reminder. For example, the setting unit analyzes the pattern of reminders set by the user in the past and proposes an optimal setting method. The setting unit can also set a reminder for a specific time period from the user's past behavior history. Furthermore, the setting unit can optimize the frequency of reminders based on the user's past behavior history. For example, the setting unit inputs the user's past behavior history data and outputs an optimal reminder setting method. In this way, by referring to the past behavior history, the optimal reminder setting method can be provided to the user. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's past behavior history data to the generation AI and cause the generation AI to select the optimal reminder setting method.

[0098] The setting unit can customize the timing of the reminder based on the user's current living situation when setting the reminder. For example, when the user is busy, the setting unit adjusts the timing of the reminder to reduce notifications. In addition, the setting unit can provide detailed reminders when the user is relaxed. Furthermore, the setting unit can optimize the timing of the reminder according to the user's living situation. For example, the setting unit inputs the user's living situation data and customizes the timing of the reminder. This enables more effective reminder setting by customizing the timing of the reminder based on the current living situation. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's living situation data to the generation AI and cause the generation AI to customize the timing of the reminder.

[0099] The setting unit can estimate the user's emotion and determine the priority of reminders based on the estimated user's emotion. For example, when the user feels anxious, the setting unit prioritizes important reminders. In addition, when the user is relaxed, the setting unit can also provide detailed reminders. Furthermore, when the user is in a hurry, the setting unit can also notify only major reminders. For example, the setting unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. This enables more appropriate reminder notification by determining the priority of reminders according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the setting unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0100] The setting unit can set an appropriate reminder in consideration of the geographical location information of the user when setting a reminder. For example, when the user is in a specific location, the setting unit sets a reminder related to the location. In addition, when the user is traveling, the setting unit can also set a reminder related to health risks at the travel destination. Furthermore, when the user is at home, the setting unit can set a reminder based on local health information. For example, the setting unit takes the geographical location information data of the user as an input and outputs an appropriate reminder. In this way, by considering the geographical location information, a reminder related to health risks specific to the region can be set. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the geographical location information data of the user to the generation AI and cause the generation AI to set an appropriate reminder.

[0101] When setting a reminder, the setting unit can analyze the user's social media activity and suggest the contents of the reminder. For example, the setting unit can suggest a related reminder based on health information shared by the user on social media. The setting unit can also analyze recent health trends from the user's social media activity and suggest a related reminder. Furthermore, the setting unit can suggest a related reminder based on posts by health experts the user follows on social media. For example, the setting unit inputs the user's social media activity data and outputs a related reminder. In this way, by analyzing the social media activity, a reminder related to the user can be suggested. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of the contents of the reminder.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] An AI assistant system for health checkups can be equipped with a function to analyze a user's meal photos and evaluate the nutritional balance. For example, a user takes a photo of their meal and uploads it to the system. The analysis unit uses image recognition technology to identify ingredients and calculate the nutrient content. It can then evaluate the nutritional balance and suggest improvements to the user. It can also compare the results with the user's past meal history to analyze long-term trends in nutritional balance. This allows users to review their eating habits and strive to eat healthier meals.

[0104] An AI assistant system for health checkups can collect users' exercise data and support them in improving their exercise habits. For example, a user records exercise data using a smartwatch or fitness tracker. The system obtains data from these devices and analyzes the frequency and intensity of exercise. It can then suggest an exercise plan based on the user's goals and monitor progress. In addition, it can integrate the user's exercise data with other health data to evaluate the overall health status. This allows the user to develop effective exercise habits and maintain health.

[0105] The AI ​​assistant system for health checkups can be equipped with the function of analyzing a user's sleep data and providing advice to improve the quality of sleep. For example, a user records sleep data using a smartwatch or sleep tracker. The system obtains data from these devices and analyzes sleep patterns and quality. It can then suggest improvements based on the user's sleep environment and lifestyle habits. It can also compare the user's past sleep data and analyze long-term sleep trends. This allows the user to ensure high-quality sleep and maintain health.

[0106] The AI ​​assistant system for health checkups can be equipped with the function of monitoring the user's stress level and providing advice for stress reduction. For example, the user records stress data using a smartwatch or stress monitoring device. The system obtains data from these devices and analyzes the stress level. Then, based on the user's stress level, it can suggest relaxation methods and activities for stress reduction. In addition, it can compare with the user's past stress data and analyze long-term stress trends. This allows the user to effectively manage stress and maintain physical and mental health.

[0107] An AI assistant system for health checkups can have the ability to estimate a user's emotions and customize health advice based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxation methods and activities to reduce stress. Also, if the user is relaxed, the system can provide advice on maintaining a healthy lifestyle. Furthermore, if the user is feeling anxious, the system can provide information and support that gives a sense of security. This makes it possible to provide appropriate health advice according to the user's emotions and support the user's physical and mental health.

[0108] An AI assistant system for health checkups can have a function of estimating the user's emotions and adjusting the way in which diagnosis results are presented based on the estimated emotions. For example, if the user feels anxious, the system can use an expression that gives a sense of security. If the user feels relaxed, the system can use an expression that includes detailed information. If the user is in a hurry, the system can use a concise and to-the-point expression. This allows the system to provide more appropriate diagnosis results by adjusting the way in which diagnosis results are presented according to the user's emotions.

[0109] An AI assistant system for health checkups can have a function to estimate a user's emotions and adjust the reminder setting method based on the estimated emotions. For example, if the user is stressed, the system can provide a simple and intuitive reminder setting method. If the user is relaxed, the system can provide detailed reminder setting options. Furthermore, if the user is in a hurry, the system can prioritize voice input and enable quick reminder setting. This allows for more appropriate reminder setting by adjusting the reminder setting method according to the user's emotions.

[0110] An AI assistant system for health checkups can be equipped with a function to estimate the user's emotions and adjust the method of recommending experts based on the estimated emotions. For example, if the user feels anxious, the system will preferentially recommend highly reliable experts. Also, if the user feels relaxed, the system can recommend multiple experts and provide a choice. Furthermore, if the user is in a hurry, the system can recommend an expert who can respond quickly. This allows the system to recommend more appropriate experts by adjusting the method of recommending experts according to the user's emotions.

[0111] An AI assistant system for health checkups can have a function of estimating the user's emotions and adjusting the length of the diagnosis result based on the estimated emotions. For example, if the user feels anxious, the system can provide a concise and reassuring diagnosis result. If the user feels relaxed, the system can provide a detailed diagnosis result. Furthermore, if the user is in a hurry, the system can provide a short diagnosis result that focuses on the main points. This allows the system to provide a more appropriate diagnosis result by adjusting the length of the diagnosis result according to the user's emotions.

[0112] An AI assistant system for health checkups can analyze users' social media activities and predict health risks. For example, it can analyze the health information and posts shared by users on social media to identify health risks. It can then suggest preventive measures and areas for improvement to the user. In addition, it can analyze health trends from users' social media activities and provide relevant information. This allows users to understand their own health risks early and take appropriate measures.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The reception unit receives an input of symptoms from a user. Examples of symptoms input from a user include, but are not limited to, headache, stomachache, and fever. For example, the reception unit receives the symptoms input by the user in a text format. The reception unit can also receive the user's symptoms using voice input. For example, the reception unit converts the user's voice into text using voice recognition technology and receives the text as a symptom. Step 2: The diagnosis unit performs a preliminary diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits. The diagnosis unit considers the user's health history, such as past diagnosis results, treatment history, and medical history. The diagnosis unit also considers the user's lifestyle habits, such as eating habits, exercise habits, and sleep patterns. The diagnosis unit uses a machine learning algorithm to analyze the user's symptoms and improve the accuracy of the diagnosis. For example, the diagnosis unit uses a machine learning algorithm such as a neural network or a support vector machine to analyze the user's symptoms. Step 3: The recommendation unit recommends consulting an expert based on the diagnosis result obtained by the diagnosis unit. For example, the recommendation unit recommends consulting an expert when symptoms are severe or when self-diagnosis is difficult to handle. The recommendation unit also provides a method for accessing an expert. For example, the recommendation unit provides a means for accessing an expert by telephone, email, video call, or other methods. Step 4: The setting unit sets regular health checks and reminders. The setting unit optimizes the timing of reminders based on the user's behavioral history, for example. For example, the setting unit analyzes the user's past behavioral history and suggests optimal reminder timing. The setting unit also customizes the timing of reminders based on the user's current life situation. For example, the setting unit reduces notifications when the user is busy and provides detailed reminders when the user is relaxed.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives the user's symptoms in text or voice. The diagnosis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs a basic diagnosis taking into account the user's past health history and lifestyle habits. The recommendation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and recommends consultation with a specialist based on the diagnosis result. The setting unit is realized by, for example, the control unit 46A of the smart device 14, and sets regular health checks and reminders. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's symptoms by voice. The diagnosis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and performs a basic diagnosis taking into account the user's past health history and lifestyle habits. The recommendation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and recommends consultation with a specialist based on the diagnosis result. The setting unit is realized by, for example, the control unit 46A of the smart glasses 214 and sets regular health checks and reminders. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives the user's symptoms by voice. The diagnosis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs a basic diagnosis taking into account the user's past health history and lifestyle habits. The recommendation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and recommends consultation with a specialist based on the diagnosis result. The setting unit is realized by, for example, the control unit 46A of the headset type terminal 314, and performs periodic health checks and sets reminders. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] For example, the reception unit is realized by the microphone 238 of the robot 414, and receives the user's symptoms by voice. The diagnosis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and performs a basic diagnosis taking into account the user's past health history and lifestyle habits. The recommendation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and recommends consultation with a specialist based on the diagnosis result. The setting unit is realized by, for example, the control unit 46A of the robot 414, and sets regular health checks and reminders. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] In addition, 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 upon request from the data processing device 12.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) A reception unit that receives an input of a symptom from a user; a diagnosis unit that performs a diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits; a recommendation unit that recommends consultation with a specialist based on the diagnosis result obtained by the diagnosis unit; A setting unit for setting health checks and reminders. A system characterized by: (Appendix 2) The diagnosis unit includes: Have a database based on past health history or lifestyle habits 2. The system of claim 1, (Appendix 3) The recommendation unit, Providing access to experts 2. The system of claim 1, (Appendix 4) The diagnosis unit includes: Using machine learning algorithms to analyze the user's symptoms and improve diagnostic accuracy 2. The system of claim 1, (Appendix 5) The setting unit is The function of adjusting the timing of reminders based on the user's behavior history is provided. 2. The system of claim 1, (Appendix 6) The diagnosis unit includes: The geographic location information of the user is further used to diagnose area-specific diseases and health risks. 2. The system of claim 1, (Appendix 7) The recommendation unit, Recommend experts based on their reviews and ratings 2. The system of claim 1, (Appendix 8) The diagnosis unit includes: The user's medication history is further used to make the diagnosis. 2. The system of claim 1, (Appendix 9) The reception unit is To estimate a user's emotion and adjust a symptom input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The reception unit is Analyze the user's past symptom input history and select the appropriate input method 2. The system of claim 1, (Appendix 11) The reception unit is When entering symptoms, filter the input based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 12) The reception unit is Estimate the user's emotions and prioritize the symptoms to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The reception unit is When entering symptoms, the system takes into account the user's geographic location to prioritize the most relevant symptoms. 2. The system of claim 1, (Appendix 14) The reception unit is When a symptom is entered, the system analyzes the user's social media activity and enters related symptoms. 2. The system of claim 1, (Appendix 15) The diagnosis unit includes: Estimating a user's emotions and adjusting the way diagnostic results are presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 16) The diagnosis unit includes: During diagnosis, adjust diagnostic detail based on symptom severity 2. The system of claim 1, (Appendix 17) The diagnosis unit includes: At diagnosis time, different diagnostic algorithms are applied depending on the symptom category. 2. The system of claim 1, (Appendix 18) The diagnosis unit includes: To estimate a user's emotion and adjust the length of a diagnosis result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The diagnosis unit includes: At diagnosis, prioritize diagnosis based on when symptoms first appear 2. The system of claim 1, (Appendix 20) The diagnosis unit includes: At diagnosis time, adjust the order of diagnostic results based on symptom relevance 2. The system of claim 1, (Appendix 21) The recommendation unit, To estimate a user's emotion and adjust an expert recommendation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The recommendation unit, When making recommendations, adjust the detail of the recommendation based on the importance of the expert 2. The system of claim 1, (Appendix 23) The recommendation unit, When making recommendations, different recommendation algorithms are applied depending on the expert category. 2. The system of claim 1, (Appendix 24) The recommendation unit, Estimate user sentiment and adjust the length of recommendations based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The recommendation unit, When making recommendations, we will prioritize recommendations based on the time of the expert evaluation. 2. The system of claim 1, (Appendix 26) The recommendation unit, When making recommendations, adjust the order of recommendation results based on the relevance of the experts 2. The system of claim 1, (Appendix 27) The setting unit is Infer user emotions and adjust how reminders are set based on the inferred user emotions 2. The system of claim 1, (Appendix 28) The setting unit is When setting a reminder, select the appropriate setting method by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 29) The setting unit is When setting a reminder, customize the reminder timing based on the user's current life situation 2. The system of claim 1, (Appendix 30) The setting unit is Infer user emotions and prioritize reminders based on the inferred user emotions 2. The system of claim 1, (Appendix 31) The setting unit is When setting reminders, the geographic location of the user is taken into consideration to set appropriate reminders. 2. The system of claim 1, (Appendix 32) The setting unit is When setting a reminder, the app will analyze the user's social media activity to suggest the content of the reminder. 2. The system of claim 1, [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an input of a symptom from a user; a diagnosis unit that performs a diagnosis based on the information received by the reception unit and the user's past health history and lifestyle habits; a recommendation unit that recommends consultation with a specialist based on the diagnosis result obtained by the diagnosis unit; A setting unit for setting health checks and reminders. A system characterized by:

2. The diagnosis unit includes: Have a database based on past health history or lifestyle habits 2. The system of claim 1 .

3. The recommendation unit, Providing access to experts 2. The system of claim 1 .

4. The diagnosis unit includes: Using machine learning algorithms to analyze the user's symptoms and improve diagnostic accuracy 2. The system of claim 1 .

5. The setting unit is The function of adjusting the timing of reminders based on the user's behavior history is provided.

2. The system of claim 1 .

6. The diagnosis unit includes: The geographic location information of the user is further used to diagnose area-specific diseases and health risks.

2. The system of claim 1 .

7. The recommendation unit, Recommend experts based on their reviews and ratings 2. The system of claim 1 .

8. The diagnosis unit includes: The user's medication history is further used to make the diagnosis.

2. The system of claim 1 .

9. The reception unit is Estimating an emotion of the user and adjusting a symptom input interface based on the estimated emotion of the user.

2. The system of claim 1 .

10. The reception unit is Analyze the user's past symptom input history and select an appropriate input method.

2. The system of claim 1 .

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