System

Abstract

To provide a system capable of providing appropriate response relevant to the ill health of children, even when medical facilities are busy.SOLUTION: A system in an embodiment includes a reception unit, an analysis unit, a providing unit, and a guide unit. The reception unit allows a parent to input the symptoms of a child via text, an image, or a moving image. The analysis unit analyzes the information input by the reception unit and provides advice based on symptoms. The providing unit provides advice based on the analysis results obtained by the analysis unit. The guide unit provides night-time emergency care or switches to remote medical care if necessary.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] With conventional technology, it was difficult to provide appropriate treatment for a child's illness when medical facilities were closed.

[0005] The system according to the embodiment aims to provide an appropriate response to a child's poor physical condition even during times when medical institutions are closed. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a guidance unit. The reception unit allows a parent to input the child's symptoms in the form of text, images, or videos. The analysis unit analyzes the information input by the reception unit and provides advice according to the symptoms. The provision unit provides advice based on the analysis results obtained by the analysis unit. The guidance unit transitions to a night-time emergency guidance or remote medical treatment as necessary. Effect of the Invention

[0007] The system according to the embodiment can provide an appropriate response to a child's poor physical condition even during times when medical institutions are closed. [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) A medical support system according to an embodiment of the present invention is a system that supports parents in making appropriate decisions when their children fall ill on days when the clinic is closed or at night. In this medical support system, parents input their children's symptoms in text, images, or videos, and the generation AI analyzes the information and provides advice according to the symptoms. If necessary, the system can transition to nighttime emergency care or remote medical care. For example, a parent takes a picture of their child's fever or cough with a device and uploads the video to the system. This information is input into the generation AI. The generation AI understands the content of the text, image, or video and provides advice according to the symptoms. For example, if the fever continues, it provides advice on cooling methods and hydration. Furthermore, if necessary, the system can transition to nighttime emergency care or remote medical care. For example, if the generation AI determines that the symptoms are serious, it provides information on the nearest nighttime emergency care. In addition, if remote medical care is possible, the patient can receive medical care through a video call with a doctor. This allows parents to take appropriate measures for their children's health even on days when the clinic is closed or at night. For example, if a child develops a fever in the middle of the night, the parent can take appropriate measures by inputting symptoms on a device and receiving advice from the generation AI. In addition, by providing night-time emergency medical services and switching to remote medical care as necessary, the medical support system can provide rapid medical care, allowing parents to properly assess their child's symptoms and provide prompt treatment.

[0029] The medical support system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a guidance unit. The reception unit allows a parent to input the child's symptoms in text, image, or video. The child's symptoms include, but are not limited to, fever, cough, and abdominal pain. The reception unit allows a parent to take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. The parent can also input the child's symptoms in images. The analysis unit uses a generation AI to analyze the information input by the reception unit and provide advice according to the symptoms. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the input text information. The generation AI can also use an image generation AI to analyze the input image information. The generation AI can also use a video generation AI to analyze the input video information. For example, the generation AI analyzes a video of the child's fever input by the parent and provides advice on the cause of the fever and how to deal with it. The provision unit provides advice based on the analysis result obtained by the analysis unit. The providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI, for example. The providing unit can also provide the parent with advice to visit a medical institution based on the results of the analysis by the generating AI. The guidance unit transitions to night emergency guidance or remote medical treatment as necessary. For example, when the generating AI judges the symptoms to be serious, the guidance unit provides guidance to the nearest night emergency. Furthermore, when remote medical treatment is possible, the guidance unit allows the parent to receive medical treatment through a video call with a doctor. This allows the medical support system according to the embodiment to allow the parent to appropriately judge the child's symptoms and respond quickly. Some or all of the above-mentioned processing in the reception unit, analysis unit, provision unit, and guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs text, image, or video information input by the parent to the generating AI, and the generating AI performs analysis. The analysis unit passes the results of the analysis by the generating AI to the provision unit, and the provision unit provides advice. When the generating AI judges the symptoms to be serious, the guidance unit provides guidance to the nearest night emergency.

[0030] The reception unit allows a parent to input the child's symptoms in text, image, or video. The child's symptoms include, but are not limited to, fever, cough, abdominal pain, and the like. The reception unit allows, for example, a parent to take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. Furthermore, the parent can input the child's symptoms in images. The reception unit is designed to provide a user-friendly interface and allow the parent to easily input symptoms. For example, through a smartphone app or a web portal, the parent can input details of the symptoms using an intuitive screen. In the case of text input, the parent can describe in detail the time of onset, duration, and other related information of the symptoms. In the case of image input, the parent can upload a photo showing the location and condition of the symptoms. In the case of video input, the parent can upload a video showing the dynamic changes of the symptoms or the specific situation. This allows the reception unit to accept information in various formats and provide accurate data to the analysis unit. Furthermore, the reception unit automatically classifies the input information so that the analysis unit can efficiently process it. For example, text information is analyzed using natural language processing technology, and image and video information is converted into an appropriate format, allowing the reception unit to quickly and accurately pass on the information entered by the parent to the analysis unit.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit and provide advice according to the symptoms. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the input text information. The generation AI can also use an image generation AI to analyze the input image information. Furthermore, the generation AI can also use a video generation AI to analyze the input video information. For example, the generation AI analyzes a video of a child's fever input by a parent and advises the cause of the fever and how to deal with it. The generation AI uses natural language processing technology to extract details of the symptoms and related background information from the text information. For example, it analyzes the duration of the fever and the presence or absence of other symptoms, and generates appropriate advice. In the image analysis, image recognition technology is used to identify visual symptoms such as skin rashes and swelling, and provide information that can be used as a reference for diagnosis. In the video analysis, dynamic changes in symptoms and behavior patterns are analyzed to provide more detailed diagnostic information. The generation AI provides specific advice to the parent based on these analysis results. For example, in the case of a fever, it advises on cooling methods, methods of hydration, and the need to visit a medical institution. In addition, the generative AI can provide more accurate analysis results by utilizing past data and medical knowledge bases, allowing the analysis unit to quickly and accurately analyze the information entered by parents and provide appropriate advice.

[0032] The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI. The providing unit can also provide the parent with advice to recommend a visit to a medical institution based on the results of the analysis by the generating AI. The providing unit provides information in various formats such as text, images, and videos in order to provide advice in a format that is easy for the parent to understand. For example, for cooling methods, images and videos showing the actual steps can be provided along with text showing specific steps. For hydration methods, appropriate types, amounts, and frequency of drinks can be specifically shown. When recommending a visit to a medical institution, information on the nearest medical institution, items to bring when visiting, and points to note can be provided in detail. Furthermore, the providing unit can collect feedback after the parent performs the advice and provide the feedback to the analyzing unit, thereby improving the accuracy and reliability of the entire system. For example, the providing unit checks whether the child's symptoms have improved as a result of the parent performing the cooling method, and provides the information to the analyzing unit. This allows the providing unit to provide appropriate advice to the parent and support the improvement of the child's symptoms.

[0033] The information section transitions to night-time emergency guidance or remote medical treatment as necessary. For example, if the generation AI judges the symptoms to be severe, the information section provides information on the nearest night-time emergency. In addition, if remote medical treatment is possible, the information section allows the patient to receive medical treatment through a video call with a doctor. The information section provides detailed information so that the parent can receive appropriate medical services quickly. For example, the information on the nearest night-time emergency includes information such as the location, contact information, hours of operation, and access methods. In addition, the information on remote medical treatment includes video call procedures, necessary equipment, and the flow of medical treatment. In addition, the information section can provide advice on first aid required until the parent arrives at the medical institution. For example, if the fever continues, the information section reconfirms the cooling method and hydration method, and encourages the parent to perform appropriate first aid. The information section always provides the latest information and supports rapid and appropriate response so that the parent can receive medical services with peace of mind. In this way, the information section can support parents in properly assessing their child's symptoms and responding quickly, thereby protecting the child's health.

[0034] The reception unit can analyze the past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the parent frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice, text, and image) that the parent used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input in a specific time period from the parent's past input history. This improves input efficiency by suggesting the optimal input method based on the past history. 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 inputs the parent's past input history into the generation AI, which then suggests the optimal input method. Furthermore, the reception unit can accumulate the parent's input history data and analyze long-term input patterns. For example, the reception unit analyzes the parent's input history data in chronological order to identify input trends. This makes it possible to grasp the parent's input history in detail and suggest the optimal input method.

[0035] The reception unit can customize the input items based on the child's age and medical history when inputting symptoms. The reception unit automatically displays appropriate symptom input items according to, for example, the child's age. The reception unit can also preferentially display related symptom input items in consideration of the child's medical history. Furthermore, the reception unit can adjust the order of input items based on the child's age and medical history. This improves the accuracy of input by providing input items according to the child's age and medical history. 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 inputs data on the child's age and medical history to the generation AI, which customizes the input items. Furthermore, the reception unit can accumulate the child's age and medical history data and analyze the long-term health condition. For example, the reception unit analyzes the child's age and medical history data in chronological order to identify a change pattern in the health condition. This makes it possible to grasp the child's age and medical history in detail and customize the input items.

[0036] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting symptoms. For example, the reception unit preferentially displays input items related to symptoms and illnesses specific to the region based on the parent's current location. The reception unit can also suggest information related to the nearest medical institution as an input item in consideration of the geographical location information of the parent. Furthermore, the reception unit can display symptom input items related to the climate and environment of the region based on the geographical location information of the parent. In this way, symptoms and medical information specific to the region can be provided by taking the geographical location information into consideration. 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 inputs the geographical location information of the parent to the generation AI, and the generation AI preferentially inputs highly relevant information. Furthermore, the reception unit can accumulate the geographical location information data of the parent and analyze the long-term trends of symptoms and illnesses specific to the region. For example, the reception unit analyzes the geographical location information data of the parent in a chronological order to identify the change pattern of symptoms and illnesses specific to the region. In this way, the geographical location information of the parent can be grasped in detail and highly relevant information can be preferentially input.

[0037] The reception unit can analyze the parent's social media activity and input related information when inputting symptoms. For example, the reception unit suggests symptoms related to recent activities and events as input items from the parent's social media posts. The reception unit can also analyze posts from the parent's social media friends and followers and display input items related to prevalent diseases and symptoms. Furthermore, the reception unit can suggest symptom input items related to recently visited places based on the parent's social media location information. In this way, related symptoms and information can be provided by analyzing social media activities. 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 inputs the parent's social media activity data to the generation AI, and the generation AI inputs the related information. Furthermore, the reception unit can accumulate the parent's social media activity data and analyze long-term activity patterns. For example, the reception unit analyzes the parent's social media activity data in chronological order to identify change patterns in activity. In this way, the parent's social media activity can be grasped in detail and related information can be input.

[0038] The analysis unit can optimize the analysis algorithm by referring to past symptom data during analysis. For example, the analysis unit identifies cases with similar symptoms based on past symptom data and optimizes the analysis algorithm. The analysis unit can also extract a specific pattern from past symptom data and reflect it in the analysis algorithm. Furthermore, the analysis unit can analyze past symptom data and improve the accuracy of the analysis algorithm. As a result, the accuracy of the analysis algorithm is improved by referring to past data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit inputs past symptom data to a generation AI, and the generation AI optimizes the analysis algorithm. Furthermore, the analysis unit can accumulate past symptom data and analyze long-term trends of symptoms. For example, the analysis unit analyzes past symptom data in a chronological order and identifies a change pattern of symptoms. As a result, past symptom data can be grasped in detail and the analysis algorithm can be optimized.

[0039] The analysis unit can apply different analysis methods depending on the symptom category during analysis. For example, the analysis unit applies a standard analysis method to general symptoms such as fever and cough. The analysis unit can also apply an image analysis method to visual symptoms such as skin rash and swelling. Furthermore, the analysis unit can apply a video analysis method to symptoms inputted as a video and perform a detailed analysis. This improves the accuracy of the analysis by applying an analysis method according to the symptom category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs data according to the symptom category to the generation AI, and the generation AI applies a different analysis method. Furthermore, the analysis unit can accumulate symptom category data and analyze long-term symptom trends. For example, the analysis unit analyzes the symptom category data in a time series and identifies the change pattern of the symptom. This makes it possible to grasp the symptom category in detail and apply different analysis methods.

[0040] The analysis unit can determine the priority of the analysis based on the occurrence time of the symptoms during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring symptoms and provides prompt advice. The analysis unit can also perform a detailed analysis of symptoms that have continued for a long time and identify the root cause. Furthermore, the analysis unit can refer to past data and provide appropriate advice for seasonal symptoms. This allows for a prompt response by setting a priority based on the occurrence time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the occurrence time data of the symptoms to the generation AI, and the generation AI determines the analysis priority. Furthermore, the analysis unit can accumulate the occurrence time data of the symptoms and analyze the long-term trend of the symptoms. For example, the analysis unit analyzes the occurrence time data of the symptoms in a chronological order and identifies the change pattern of the symptoms. This allows the occurrence time of the symptoms to be grasped in detail and the analysis priority to be determined.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the literature related to the symptoms during the analysis. The analysis unit, for example, refers to the latest medical literature and reflects it in the analysis result. The analysis unit can also optimize the analysis algorithm by referring to past research papers related to the symptoms. Furthermore, the analysis unit can also perform a detailed analysis by referring to a specialized book on the symptoms. As a result, the accuracy of the analysis is improved by referring to the related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the literature data related to the symptoms to the generation AI, and the generation AI improves the accuracy of the analysis. Furthermore, the analysis unit can accumulate the literature data related to the symptoms and analyze the long-term trend of the symptoms. For example, the analysis unit analyzes the literature data related to the symptoms in a chronological order and identifies the change pattern of the symptoms. As a result, the literature related to the symptoms can be grasped in detail and the accuracy of the analysis can be improved.

[0042] The providing unit can adjust the level of detail of the advice based on the importance of the symptom when providing the advice. For example, the providing unit provides detailed advice for serious symptoms to encourage a quick response. The providing unit can also provide concise advice for mild symptoms and convey only necessary information. Furthermore, the providing unit can provide advice with a moderate level of detail for moderate symptoms to encourage an appropriate response. As a result, an appropriate response is possible by providing a level of detail according to the importance of the symptom. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs symptom importance data to the generating AI, and the generating AI adjusts the level of detail of the advice. Furthermore, the providing unit can accumulate symptom importance data and analyze long-term trends of the symptoms. For example, the providing unit analyzes symptom importance data in a chronological order to identify a change pattern of the symptoms. As a result, the importance of the symptoms can be grasped in detail and the level of detail of the advice can be adjusted.

[0043] The providing unit can apply different advice methods according to the symptom category when providing advice. For example, the providing unit provides standard advice for common symptoms such as fever and cough. In addition, the providing unit can also provide advice using images for visual symptoms such as skin rash and swelling. Furthermore, the providing unit can also provide advice using videos for symptoms inputted as videos. As a result, appropriate advice can be provided by providing advice methods according to the symptom category. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom category data to a generating AI, and the generating AI applies different advice methods. Furthermore, the providing unit can accumulate symptom category data and analyze long-term symptom trends. For example, the providing unit analyzes symptom category data in a time series and identifies a change pattern of the symptom. As a result, the symptom category can be grasped in detail and different advice methods can be applied.

[0044] The providing unit can adjust the order of advice based on the occurrence time of the symptom when providing advice. For example, the providing unit provides advice preferentially to a symptom that has recently occurred. In addition, the providing unit can provide detailed advice to a symptom that has continued for a long time and identify the root cause. Furthermore, the providing unit can provide appropriate advice to seasonal symptoms by referring to past data. This allows for a quick response by setting an order based on the occurrence time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom occurrence time data to the generating AI, and the generating AI adjusts the order of advice. Furthermore, the providing unit can accumulate symptom occurrence time data and analyze long-term symptom trends. For example, the providing unit analyzes symptom occurrence time data in chronological order to identify a change pattern of the symptom. This allows the occurrence time of the symptom to be grasped in detail and the order of advice to be adjusted.

[0045] The providing unit can improve the accuracy of advice by referring to symptom-related information when providing advice. For example, the providing unit refers to the latest medical information related to the symptom and reflects it in the advice. The providing unit can also improve the accuracy of advice by referring to past research papers related to the symptom. Furthermore, the providing unit can also provide detailed advice by referring to specialized books on the symptom. As a result, the accuracy of advice is improved by referring to the related information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom-related information data to a generating AI, which then improves the accuracy of advice. Furthermore, the providing unit can accumulate symptom-related information data and analyze long-term trends of symptoms. For example, the providing unit analyzes symptom-related information data in chronological order to identify a change pattern of the symptom. As a result, symptom-related information can be grasped in detail and the accuracy of advice can be improved.

[0046] The guidance unit can select the optimal guidance method by referring to the past guidance history when providing guidance. For example, the guidance unit proposes the optimal guidance method based on the guidance method used by the parent in the past. The guidance unit can also propose a guidance method that avoids congestion from the parent's past guidance history. Furthermore, the guidance unit can analyze the parent's past guidance history and propose the most efficient guidance method. As a result, the efficiency of guidance is improved by providing the optimal guidance method based on the past history. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the parent's past guidance history data into the generation AI, and the generation AI selects the optimal guidance method. Furthermore, the guidance unit can accumulate the parent's guidance history data and analyze long-term guidance patterns. For example, the guidance unit analyzes the parent's guidance history data in chronological order to identify the change pattern of guidance. As a result, the parent's guidance history can be grasped in detail and the optimal guidance method can be selected.

[0047] The guidance unit can adjust the level of detail of the guidance based on the severity of the symptoms when providing guidance. For example, the guidance unit provides detailed guidance for serious symptoms to encourage a quick response. The guidance unit can also provide concise guidance for mild symptoms and convey only necessary information. Furthermore, the guidance unit can provide guidance with a moderate level of detail for moderate symptoms to encourage an appropriate response. As a result, an appropriate response is possible by providing a level of detail according to the severity of the symptoms. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the severity data of the symptoms to the generation AI, and the generation AI adjusts the level of detail of the guidance. Furthermore, the guidance unit can accumulate the severity data of the symptoms and analyze the long-term trend of the symptoms. For example, the guidance unit analyzes the severity data of the symptoms in a time series and identifies the change pattern of the symptoms. As a result, the severity of the symptoms can be grasped in detail and the level of detail of the guidance can be adjusted.

[0048] The guidance unit can select the optimal guidance method by considering the geographical location information of the parent when providing guidance. For example, the guidance unit provides guidance to the nearest medical institution based on the parent's current location. The guidance unit can also provide medical information specific to the region by considering the geographical location information of the parent. Furthermore, the guidance unit can provide the optimal means of transportation based on the geographical location information of the parent. This makes it possible to provide medical information specific to the region by considering the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit inputs the geographical location information data of the parent to the generation AI, and the generation AI selects the optimal guidance method. Furthermore, the guidance unit can accumulate the geographical location information data of the parent and analyze the long-term trend of the medical information specific to the region. For example, the guidance unit analyzes the geographical location information data of the parent in a time series and identifies the change pattern of the medical information specific to the region. This makes it possible to grasp the geographical location information of the parent in detail and select the optimal guidance method.

[0049] The guidance unit can improve the accuracy of the guidance by referring to the symptom-related information when providing guidance. The guidance unit, for example, refers to the latest medical information related to the symptom and reflects it in the guidance. The guidance unit can also improve the accuracy of the guidance by referring to past research papers related to the symptom. Furthermore, the guidance unit can also provide detailed guidance by referring to specialized books on the symptom. As a result, the accuracy of the guidance is improved by referring to the related information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the symptom-related information data to the generation AI, and the generation AI improves the accuracy of the guidance. Furthermore, the guidance unit can accumulate the symptom-related information data and analyze the long-term trend of the symptom. For example, the guidance unit analyzes the symptom-related information data in a chronological order and identifies the change pattern of the symptom. As a result, the symptom-related information can be grasped in detail and the accuracy of the guidance can be improved.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception department can translate symptom information entered by parents in real time, providing multilingual support. For example, symptoms entered by parents in Japanese can be translated into English or Chinese and provided to medical institutions. The reception department can also translate symptoms entered by parents in a foreign language into Japanese and input them into the generation AI. Furthermore, the reception department can convert symptom information entered by parents into text using voice recognition technology and translate it. This makes it possible to provide multilingual support, making it easier for foreign parents to use.

[0052] The reception unit can analyze the past symptom input history and suggest preventive measures to the parents. For example, if fever symptoms have been frequently input in the past, advice on how to prevent fevers can be provided. Also, if there have been many coughing symptoms in the past, environmental improvement measures to prevent coughing can be suggested. Furthermore, from the past input history, it is possible to predict symptoms that are common in certain seasons and notify preventive measures in advance. This improves health management by suggesting preventive measures based on past history.

[0053] When inputting symptoms, the reception unit can customize the input items taking into account the lifestyle habit data of the parent. For example, if the parent is a smoker, symptom input items related to smoking are preferentially displayed. Also, if the parent is not getting enough exercise, symptom input items related to exercise can be suggested. Furthermore, if the parent has specific dietary restrictions, symptom input items related to the dietary restrictions can be displayed. This allows for more appropriate symptom input by taking into account lifestyle habit data.

[0054] When inputting symptoms, the reception unit can customize the input items taking into account the parent's health data. For example, if the parent has high blood pressure, symptom input items related to high blood pressure can be displayed preferentially. Also, if the parent has diabetes, symptom input items related to diabetes can be suggested. Furthermore, if the parent has allergies, symptom input items related to the allergy can be displayed. This allows for more appropriate symptom input by taking health data into account.

[0055] The reception unit can analyze the parent's social media activity and input related information when inputting symptoms. For example, the reception unit can suggest symptoms related to recent activities or events as input items from the parent's social media posts. The reception unit can also analyze posts from the parent's social media friends and followers to display input items related to prevalent diseases and symptoms. Furthermore, the reception unit can suggest symptom input items related to recently visited places based on the parent's social media location information. In this way, related symptoms and information can be provided by analyzing social media activity.

[0056] During analysis, the analysis unit can optimize the analysis algorithm by referring to past symptom data. For example, cases with similar symptoms are identified based on past symptom data, and the analysis algorithm is optimized. The analysis unit can also extract specific patterns from past symptom data and reflect them in the analysis algorithm. Furthermore, the analysis unit can analyze past symptom data and improve the accuracy of the analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to past data.

[0057] During analysis, the analysis unit can apply different analysis methods depending on the symptom category. For example, a standard analysis method is applied to general symptoms such as fever and cough. The analysis unit can also apply an image analysis method to visual symptoms such as skin rashes and swelling. Furthermore, the analysis unit can apply a video analysis method to symptoms inputted as a video to perform a detailed analysis. In this way, the accuracy of the analysis is improved by applying an analysis method according to the symptom category.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The reception unit receives a message from a parent about the child's symptoms, which may include, but are not limited to, fever, cough, abdominal pain, and the like. The parent can take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. The parent can also input the child's symptoms in images. Step 2: The analysis unit uses the generation AI to analyze the information input by the reception unit and provides advice according to the symptoms. For example, the generation AI analyzes the input text information using a text generation AI (e.g., LLM). The generation AI can also analyze the input image information using an image generation AI. Furthermore, the generation AI can also analyze the input video information using a video generation AI. For example, the generation AI analyzes a video of a child's fever input by a parent and provides advice on the cause of the fever and how to deal with it. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI. The providing unit can also provide the parent with advice to visit a medical institution based on the results of the analysis by the generating AI. Step 4: The information section transitions to night-time emergency care or remote medical care as necessary. For example, if the generation AI judges the symptoms to be severe, the information section provides information on the nearest night-time emergency care. In addition, if remote medical care is possible, the information section allows the patient to receive medical care through a video call with a doctor.

[0060] (Example 2) A medical support system according to an embodiment of the present invention is a system that supports parents in making appropriate decisions when their children fall ill on days when the clinic is closed or at night. In this medical support system, parents input their children's symptoms in text, images, or videos, and the generation AI analyzes the information and provides advice according to the symptoms. If necessary, the system can transition to nighttime emergency care or remote medical care. For example, a parent takes a picture of their child's fever or cough with a device and uploads the video to the system. This information is input into the generation AI. The generation AI understands the content of the text, image, or video and provides advice according to the symptoms. For example, if the fever continues, it provides advice on cooling methods and hydration. Furthermore, if necessary, the system can transition to nighttime emergency care or remote medical care. For example, if the generation AI determines that the symptoms are serious, it provides information on the nearest nighttime emergency care. In addition, if remote medical care is possible, the patient can receive medical care through a video call with a doctor. This allows parents to take appropriate measures for their children's health even on days when the clinic is closed or at night. For example, if a child develops a fever in the middle of the night, the parent can take appropriate measures by inputting symptoms on a device and receiving advice from the generation AI. In addition, by providing night-time emergency medical services and switching to remote medical care as necessary, the medical support system can provide rapid medical care, allowing parents to properly assess their child's symptoms and provide prompt treatment.

[0061] The medical support system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a guidance unit. The reception unit allows a parent to input the child's symptoms in text, image, or video. The child's symptoms include, but are not limited to, fever, cough, and abdominal pain. The reception unit allows a parent to take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. The parent can also input the child's symptoms in images. The analysis unit uses a generation AI to analyze the information input by the reception unit and provide advice according to the symptoms. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the input text information. The generation AI can also use an image generation AI to analyze the input image information. The generation AI can also use a video generation AI to analyze the input video information. For example, the generation AI analyzes a video of the child's fever input by the parent and provides advice on the cause of the fever and how to deal with it. The provision unit provides advice based on the analysis result obtained by the analysis unit. The providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI, for example. The providing unit can also provide the parent with advice to visit a medical institution based on the results of the analysis by the generating AI. The guidance unit transitions to night emergency guidance or remote medical treatment as necessary. For example, when the generating AI judges the symptoms to be serious, the guidance unit provides guidance to the nearest night emergency. Furthermore, when remote medical treatment is possible, the guidance unit allows the parent to receive medical treatment through a video call with a doctor. This allows the medical support system according to the embodiment to allow the parent to appropriately judge the child's symptoms and respond quickly. Some or all of the above-mentioned processing in the reception unit, analysis unit, provision unit, and guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs text, image, or video information input by the parent to the generating AI, and the generating AI performs analysis. The analysis unit passes the results of the analysis by the generating AI to the provision unit, and the provision unit provides advice. When the generating AI judges the symptoms to be serious, the guidance unit provides guidance to the nearest night emergency.

[0062] The reception unit allows a parent to input the child's symptoms in text, image, or video. The child's symptoms include, but are not limited to, fever, cough, abdominal pain, and the like. The reception unit allows, for example, a parent to take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. Furthermore, the parent can input the child's symptoms in images. The reception unit is designed to provide a user-friendly interface and allow the parent to easily input symptoms. For example, through a smartphone app or a web portal, the parent can input details of the symptoms using an intuitive screen. In the case of text input, the parent can describe in detail the time of onset, duration, and other related information of the symptoms. In the case of image input, the parent can upload a photo showing the location and condition of the symptoms. In the case of video input, the parent can upload a video showing the dynamic changes of the symptoms or the specific situation. This allows the reception unit to accept information in various formats and provide accurate data to the analysis unit. Furthermore, the reception unit automatically classifies the input information so that the analysis unit can efficiently process it. For example, text information is analyzed using natural language processing technology, and image and video information is converted into an appropriate format, allowing the reception unit to quickly and accurately pass on the information entered by the parent to the analysis unit.

[0063] The analysis unit uses the generation AI to analyze the information input by the reception unit and provide advice according to the symptoms. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the input text information. The generation AI can also use an image generation AI to analyze the input image information. Furthermore, the generation AI can also use a video generation AI to analyze the input video information. For example, the generation AI analyzes a video of a child's fever input by a parent and advises the cause of the fever and how to deal with it. The generation AI uses natural language processing technology to extract details of the symptoms and related background information from the text information. For example, it analyzes the duration of the fever and the presence or absence of other symptoms, and generates appropriate advice. In the image analysis, image recognition technology is used to identify visual symptoms such as skin rashes and swelling, and provide information that can be used as a reference for diagnosis. In the video analysis, dynamic changes in symptoms and behavior patterns are analyzed to provide more detailed diagnostic information. The generation AI provides specific advice to the parent based on these analysis results. For example, in the case of a fever, it advises on cooling methods, methods of hydration, and the need to visit a medical institution. In addition, the generative AI can provide more accurate analysis results by utilizing past data and medical knowledge bases, allowing the analysis unit to quickly and accurately analyze the information entered by parents and provide appropriate advice.

[0064] The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI. The providing unit can also provide the parent with advice to recommend a visit to a medical institution based on the results of the analysis by the generating AI. The providing unit provides information in various formats such as text, images, and videos in order to provide advice in a format that is easy for the parent to understand. For example, for cooling methods, images and videos showing the actual steps can be provided along with text showing specific steps. For hydration methods, appropriate types, amounts, and frequency of drinks can be specifically shown. When recommending a visit to a medical institution, information on the nearest medical institution, items to bring when visiting, and points to note can be provided in detail. Furthermore, the providing unit can collect feedback after the parent performs the advice and provide the feedback to the analyzing unit, thereby improving the accuracy and reliability of the entire system. For example, the providing unit checks whether the child's symptoms have improved as a result of the parent performing the cooling method, and provides the information to the analyzing unit. As a result, the providing unit can provide appropriate advice to the parent and support the improvement of the child's symptoms.

[0065] The information section transitions to night-time emergency guidance or remote medical treatment as necessary. For example, if the generation AI judges the symptoms to be severe, the information section provides information on the nearest night-time emergency. In addition, if remote medical treatment is possible, the information section allows the patient to receive medical treatment through a video call with a doctor. The information section provides detailed information so that the parent can receive appropriate medical services quickly. For example, the information on the nearest night-time emergency includes information such as the location, contact information, hours of operation, and access methods. In addition, the information on remote medical treatment includes video call procedures, necessary equipment, and the flow of medical treatment. In addition, the information section can provide advice on first aid required until the parent arrives at the medical institution. For example, if the fever continues, the information section reconfirms the cooling method and hydration method, and encourages the parent to perform appropriate first aid. The information section always provides the latest information and supports rapid and appropriate response so that the parent can receive medical services with peace of mind. In this way, the information section can support parents in properly assessing their child's symptoms and responding quickly, thereby protecting the child's health.

[0066] The reception unit can estimate the parent's emotion and adjust the symptom input interface based on the estimated parent's emotion. For example, when the parent is feeling anxious, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the parent is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the parent is in a hurry, the reception unit can prioritize voice input and enable the parent to quickly input symptoms. This improves ease of input by providing an interface according to the parent'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 captures the parent's facial expression with a camera and inputs it to the generation AI, which then estimates the parent's emotion. Furthermore, the reception unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the reception unit may analyze the parent's emotion data over time to identify patterns of change in emotion, thereby enabling the parent's emotions to be understood in detail and the interface to be adjusted.

[0067] The reception unit can analyze the past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the parent frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice, text, and image) that the parent used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input in a specific time period from the parent's past input history. This improves input efficiency by suggesting the optimal input method based on the past history. 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 inputs the parent's past input history into the generation AI, which then suggests the optimal input method. Furthermore, the reception unit can accumulate the parent's input history data and analyze long-term input patterns. For example, the reception unit analyzes the parent's input history data in chronological order to identify input trends. This makes it possible to grasp the parent's input history in detail and suggest the optimal input method.

[0068] The reception unit can customize the input items based on the child's age and medical history when inputting symptoms. The reception unit automatically displays appropriate symptom input items according to, for example, the child's age. The reception unit can also preferentially display related symptom input items in consideration of the child's medical history. Furthermore, the reception unit can adjust the order of input items based on the child's age and medical history. This improves the accuracy of input by providing input items according to the child's age and medical history. 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 inputs data on the child's age and medical history to the generation AI, which customizes the input items. Furthermore, the reception unit can accumulate the child's age and medical history data and analyze the long-term health condition. For example, the reception unit analyzes the child's age and medical history data in chronological order to identify a change pattern in the health condition. This makes it possible to grasp the child's age and medical history in detail and customize the input items.

[0069] The reception unit can estimate the parent's emotion and determine the priority of input based on the estimated parent's emotion. For example, when the parent is feeling anxious, the reception unit preferentially displays important symptom input items. In addition, when the parent is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the parent is in a hurry, the reception unit can display the most important input items first to enable quick input. In this way, important information can be quickly input by setting priorities according to the parent's emotion. The estimation of emotion 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 captures the parent's facial expression with a camera and inputs it to the generation AI, which then estimates the parent's emotion. Furthermore, the reception unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the reception unit may analyze the parent's emotion data in chronological order to identify patterns of change in emotion, thereby enabling the parent's emotions to be grasped in detail and the priority of input to be determined.

[0070] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting symptoms. For example, the reception unit preferentially displays input items related to symptoms and illnesses specific to the region based on the parent's current location. The reception unit can also suggest information related to the nearest medical institution as an input item in consideration of the geographical location information of the parent. Furthermore, the reception unit can display symptom input items related to the climate and environment of the region based on the geographical location information of the parent. In this way, symptoms and medical information specific to the region can be provided by taking the geographical location information into consideration. 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 inputs the geographical location information of the parent to the generation AI, and the generation AI preferentially inputs highly relevant information. Furthermore, the reception unit can accumulate the geographical location information data of the parent and analyze the long-term trends of symptoms and illnesses specific to the region. For example, the reception unit analyzes the geographical location information data of the parent in a chronological order to identify the change pattern of symptoms and illnesses specific to the region. In this way, the geographical location information of the parent can be grasped in detail and highly relevant information can be preferentially input.

[0071] The reception unit can analyze the parent's social media activity and input related information when inputting symptoms. For example, the reception unit suggests symptoms related to recent activities and events as input items from the parent's social media posts. The reception unit can also analyze posts from the parent's social media friends and followers and display input items related to prevalent diseases and symptoms. Furthermore, the reception unit can suggest symptom input items related to recently visited places based on the parent's social media location information. In this way, related symptoms and information can be provided by analyzing social media activities. 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 inputs the parent's social media activity data to the generation AI, and the generation AI inputs the related information. Furthermore, the reception unit can accumulate the parent's social media activity data and analyze long-term activity patterns. For example, the reception unit analyzes the parent's social media activity data in chronological order to identify change patterns in activity. In this way, the parent's social media activity can be grasped in detail and related information can be input.

[0072] The analysis unit can estimate the parent's emotion and adjust the accuracy of the analysis based on the estimated parent's emotion. For example, if the parent feels anxious, the analysis unit can increase the accuracy of the analysis and provide detailed advice. Also, if the parent is relaxed, the analysis unit can keep the analysis accuracy at a normal level and provide general advice. Furthermore, if the parent is in a hurry, the analysis unit can perform a quick analysis and provide concise advice. This makes it possible to provide appropriate advice by providing analysis accuracy according to the parent'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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit captures the parent's facial expression with a camera and inputs it into the generation AI, which then estimates the parent's emotion. Furthermore, the analysis unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the analysis unit may analyze the parent's emotion data over time to identify patterns of change in emotion, thereby enabling the parent's emotions to be understood in detail and the accuracy of the analysis to be adjusted.

[0073] The analysis unit can optimize the analysis algorithm by referring to past symptom data during analysis. For example, the analysis unit identifies cases with similar symptoms based on past symptom data and optimizes the analysis algorithm. The analysis unit can also extract a specific pattern from past symptom data and reflect it in the analysis algorithm. Furthermore, the analysis unit can analyze past symptom data and improve the accuracy of the analysis algorithm. As a result, the accuracy of the analysis algorithm is improved by referring to past data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit inputs past symptom data to a generation AI, and the generation AI optimizes the analysis algorithm. Furthermore, the analysis unit can accumulate past symptom data and analyze long-term trends of symptoms. For example, the analysis unit analyzes past symptom data in a chronological order and identifies a change pattern of symptoms. As a result, past symptom data can be grasped in detail and the analysis algorithm can be optimized.

[0074] The analysis unit can apply different analysis methods depending on the symptom category during analysis. For example, the analysis unit applies a standard analysis method to general symptoms such as fever and cough. The analysis unit can also apply an image analysis method to visual symptoms such as skin rash and swelling. Furthermore, the analysis unit can apply a video analysis method to symptoms inputted as a video and perform a detailed analysis. This improves the accuracy of the analysis by applying an analysis method according to the symptom category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs data according to the symptom category to the generation AI, and the generation AI applies a different analysis method. Furthermore, the analysis unit can accumulate symptom category data and analyze long-term symptom trends. For example, the analysis unit analyzes the symptom category data in a time series and identifies the change pattern of the symptom. This makes it possible to grasp the symptom category in detail and apply different analysis methods.

[0075] The analysis unit can estimate the parent's emotion and adjust the display method of the analysis result based on the estimated parent's emotion. For example, when the parent feels anxious, the analysis unit provides a detailed analysis result to give a sense of security. In addition, when the parent is relaxed, the analysis unit can provide a concise analysis result and display only necessary information. Furthermore, when the parent is in a hurry, the analysis unit can provide an analysis result with the main points and quickly convey information. This provides a display method according to the parent's emotion, thereby deepening understanding of the analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit captures the parent's facial expression with a camera and inputs it into the generation AI, which estimates the parent's emotion. Furthermore, the analysis unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the analysis unit may analyze the parent's emotion data over time to identify patterns of emotion change, thereby enabling the parent's emotions to be understood in detail and the method of displaying the analysis results to be adjusted.

[0076] The analysis unit can determine the priority of the analysis based on the occurrence time of the symptoms during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring symptoms and provides prompt advice. The analysis unit can also perform a detailed analysis of symptoms that have continued for a long time and identify the root cause. Furthermore, the analysis unit can refer to past data and provide appropriate advice for seasonal symptoms. This allows for a prompt response by setting a priority based on the occurrence time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the occurrence time data of the symptoms to the generation AI, and the generation AI determines the analysis priority. Furthermore, the analysis unit can accumulate the occurrence time data of the symptoms and analyze the long-term trend of the symptoms. For example, the analysis unit analyzes the occurrence time data of the symptoms in a chronological order and identifies the change pattern of the symptoms. This allows the occurrence time of the symptoms to be grasped in detail and the analysis priority to be determined.

[0077] The analysis unit can improve the accuracy of the analysis by referring to the literature related to the symptoms during the analysis. The analysis unit, for example, refers to the latest medical literature and reflects it in the analysis result. The analysis unit can also optimize the analysis algorithm by referring to past research papers related to the symptoms. Furthermore, the analysis unit can also perform a detailed analysis by referring to a specialized book on the symptoms. As a result, the accuracy of the analysis is improved by referring to the related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the literature data related to the symptoms to the generation AI, and the generation AI improves the accuracy of the analysis. Furthermore, the analysis unit can accumulate the literature data related to the symptoms and analyze the long-term trend of the symptoms. For example, the analysis unit analyzes the literature data related to the symptoms in a chronological order and identifies the change pattern of the symptoms. As a result, the literature related to the symptoms can be grasped in detail and the accuracy of the analysis can be improved.

[0078] The providing unit can estimate the parent's emotion and adjust the way of expressing the advice based on the estimated parent's emotion. For example, when the parent feels anxious, the providing unit provides the advice in gentle language. Also, when the parent is relaxed, the providing unit can provide the advice including detailed information. Furthermore, when the parent is in a hurry, the providing unit can provide concise and to the point advice. This improves the ease of accepting the advice by providing an expression method according to the parent's emotion. The estimation of the emotion 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit captures the parent's facial expression with a camera and inputs it into the generation AI, which estimates the parent's emotion. Furthermore, the providing unit can accumulate the parent's emotion data and analyze the long-term trend of emotions. For example, the providing unit may analyze the parent's emotion data over time to identify patterns of change in emotion, thereby enabling the parent's emotions to be understood in detail and the method of expressing advice to be adjusted.

[0079] The providing unit can adjust the level of detail of the advice based on the importance of the symptom when providing the advice. For example, the providing unit provides detailed advice for serious symptoms to encourage a quick response. The providing unit can also provide concise advice for mild symptoms and convey only necessary information. Furthermore, the providing unit can provide advice with a moderate level of detail for moderate symptoms to encourage an appropriate response. As a result, an appropriate response is possible by providing a level of detail according to the importance of the symptom. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs symptom importance data to the generating AI, and the generating AI adjusts the level of detail of the advice. Furthermore, the providing unit can accumulate symptom importance data and analyze long-term trends of the symptoms. For example, the providing unit analyzes symptom importance data in a chronological order to identify a change pattern of the symptoms. As a result, the importance of the symptoms can be grasped in detail and the level of detail of the advice can be adjusted.

[0080] The providing unit can apply different advice methods according to the symptom category when providing advice. For example, the providing unit provides standard advice for common symptoms such as fever and cough. In addition, the providing unit can also provide advice using images for visual symptoms such as skin rash and swelling. Furthermore, the providing unit can also provide advice using videos for symptoms inputted as videos. As a result, appropriate advice can be provided by providing advice methods according to the symptom category. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom category data to a generating AI, and the generating AI applies different advice methods. Furthermore, the providing unit can accumulate symptom category data and analyze long-term symptom trends. For example, the providing unit analyzes symptom category data in a time series and identifies a change pattern of the symptom. As a result, the symptom category can be grasped in detail and different advice methods can be applied.

[0081] The providing unit can estimate the parent's emotion and determine the priority of advice based on the estimated parent's emotion. For example, when the parent feels anxious, the providing unit can provide important advice with priority. Also, when the parent is relaxed, the providing unit can provide detailed advice and convey necessary information. Furthermore, when the parent is in a hurry, the providing unit can provide the most important advice first, so that the parent can respond quickly. In this way, important information can be provided quickly by setting a priority according to the parent's emotion. The estimation of emotion 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit captures the parent's facial expression with a camera and inputs it into the generation AI, which then estimates the parent's emotion. Furthermore, the providing unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the providing unit may analyze the emotion data of the parent over time to identify a pattern of change in emotion, thereby enabling the parent's emotions to be understood in detail and the priority of advice to be determined.

[0082] The providing unit can adjust the order of advice based on the occurrence time of the symptom when providing advice. For example, the providing unit provides advice preferentially to a symptom that has recently occurred. In addition, the providing unit can provide detailed advice to a symptom that has continued for a long time and identify the root cause. Furthermore, the providing unit can provide appropriate advice to seasonal symptoms by referring to past data. This allows for a quick response by setting an order based on the occurrence time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom occurrence time data to the generating AI, and the generating AI adjusts the order of advice. Furthermore, the providing unit can accumulate symptom occurrence time data and analyze long-term symptom trends. For example, the providing unit analyzes symptom occurrence time data in chronological order to identify a change pattern of the symptom. This allows the occurrence time of the symptom to be grasped in detail and the order of advice to be adjusted.

[0083] The providing unit can improve the accuracy of advice by referring to symptom-related information when providing advice. For example, the providing unit refers to the latest medical information related to the symptom and reflects it in the advice. The providing unit can also improve the accuracy of advice by referring to past research papers related to the symptom. Furthermore, the providing unit can also provide detailed advice by referring to specialized books on the symptom. As a result, the accuracy of advice is improved by referring to the related information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs symptom-related information data to a generating AI, which then improves the accuracy of advice. Furthermore, the providing unit can accumulate symptom-related information data and analyze long-term trends of symptoms. For example, the providing unit analyzes symptom-related information data in chronological order to identify a change pattern of the symptom. As a result, symptom-related information can be grasped in detail and the accuracy of advice can be improved.

[0084] The guidance unit can estimate the parent's emotion and adjust the display method of the guidance based on the estimated parent's emotion. For example, when the parent feels anxious, the guidance unit provides a simple and highly visible display method. Furthermore, when the parent is relaxed, the guidance unit can also provide a display method including detailed information. Furthermore, when the parent is in a hurry, the guidance unit can also provide a display method that focuses on the main points. This provides a display method according to the parent's emotion, thereby deepening understanding of the guidance. 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 guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit captures the parent's facial expression with a camera and inputs it into the generation AI, which estimates the parent's emotion. Furthermore, the guidance unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the guidance unit may analyze the emotion data of the parent over time to identify a pattern of change in emotion, thereby enabling the unit to grasp the parent's emotions in detail and adjust the method of displaying guidance.

[0085] The guidance unit can select the optimal guidance method by referring to the past guidance history when providing guidance. For example, the guidance unit proposes the optimal guidance method based on the guidance method used by the parent in the past. The guidance unit can also propose a guidance method that avoids congestion from the parent's past guidance history. Furthermore, the guidance unit can analyze the parent's past guidance history and propose the most efficient guidance method. As a result, the efficiency of guidance is improved by providing the optimal guidance method based on the past history. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the parent's past guidance history data into the generation AI, and the generation AI selects the optimal guidance method. Furthermore, the guidance unit can accumulate the parent's guidance history data and analyze long-term guidance patterns. For example, the guidance unit analyzes the parent's guidance history data in chronological order to identify the change pattern of guidance. As a result, the parent's guidance history can be grasped in detail and the optimal guidance method can be selected.

[0086] The guidance unit can adjust the level of detail of the guidance based on the severity of the symptoms when providing guidance. For example, the guidance unit provides detailed guidance for serious symptoms to encourage a quick response. The guidance unit can also provide concise guidance for mild symptoms and convey only necessary information. Furthermore, the guidance unit can provide guidance with a moderate level of detail for moderate symptoms to encourage an appropriate response. As a result, an appropriate response is possible by providing a level of detail according to the severity of the symptoms. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the severity data of the symptoms to the generation AI, and the generation AI adjusts the level of detail of the guidance. Furthermore, the guidance unit can accumulate the severity data of the symptoms and analyze the long-term trend of the symptoms. For example, the guidance unit analyzes the severity data of the symptoms in a time series and identifies the change pattern of the symptoms. As a result, the severity of the symptoms can be grasped in detail and the level of detail of the guidance can be adjusted.

[0087] The guidance unit can estimate the parent's emotion and determine the priority of the guidance based on the estimated parent's emotion. For example, when the parent feels anxious, the guidance unit can provide important guidance with priority. Also, when the parent is relaxed, the guidance unit can provide detailed guidance and convey necessary information. Furthermore, when the parent is in a hurry, the guidance unit can provide the most important guidance first so that the parent can respond quickly. In this way, important information can be provided quickly by setting a priority according to the parent's emotion. The estimation of emotion 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 guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit captures the parent's facial expression with a camera and inputs it into the generation AI, which estimates the parent's emotion. Furthermore, the guidance unit can accumulate the parent's emotion data and analyze long-term emotion trends. For example, the guidance unit may analyze the emotion data of the parent in chronological order to identify a pattern of change in emotion, thereby enabling the unit to grasp the parent's emotions in detail and determine the priority of guidance.

[0088] The guidance unit can select the optimal guidance method by considering the geographical location information of the parent when providing guidance. For example, the guidance unit provides guidance to the nearest medical institution based on the parent's current location. The guidance unit can also provide medical information specific to the region by considering the geographical location information of the parent. Furthermore, the guidance unit can provide the optimal means of transportation based on the geographical location information of the parent. This makes it possible to provide medical information specific to the region by considering the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit inputs the geographical location information data of the parent to the generation AI, and the generation AI selects the optimal guidance method. Furthermore, the guidance unit can accumulate the geographical location information data of the parent and analyze the long-term trend of the medical information specific to the region. For example, the guidance unit analyzes the geographical location information data of the parent in a time series and identifies the change pattern of the medical information specific to the region. This makes it possible to grasp the geographical location information of the parent in detail and select the optimal guidance method.

[0089] The guidance unit can improve the accuracy of the guidance by referring to the symptom-related information when providing guidance. The guidance unit, for example, refers to the latest medical information related to the symptom and reflects it in the guidance. The guidance unit can also improve the accuracy of the guidance by referring to past research papers related to the symptom. Furthermore, the guidance unit can also provide detailed guidance by referring to specialized books on the symptom. As a result, the accuracy of the guidance is improved by referring to the related information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit inputs the symptom-related information data to the generation AI, and the generation AI improves the accuracy of the guidance. Furthermore, the guidance unit can accumulate the symptom-related information data and analyze the long-term trend of the symptom. For example, the guidance unit analyzes the symptom-related information data in a chronological order and identifies the change pattern of the symptom. As a result, the symptom-related information can be grasped in detail and the accuracy of the guidance can be improved.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception department can translate symptom information entered by parents in real time, providing multilingual support. For example, symptoms entered by parents in Japanese can be translated into English or Chinese and provided to medical institutions. The reception department can also translate symptoms entered by parents in a foreign language into Japanese and input them into the generation AI. Furthermore, the reception department can convert symptom information entered by parents into text using voice recognition technology and translate it. This makes it possible to provide multilingual support, making it easier for foreign parents to use.

[0092] The reception unit can estimate the parent's emotions and provide a guide for symptom input based on the estimated parent's emotions. For example, if the parent feels anxious, the input procedure can be explained in gentle language. If the parent feels relaxed, detailed guidance can be provided to improve the accuracy of input. Furthermore, if the parent is in a hurry, concise guidance can be provided to enable quick input. In this way, ease of input is improved by providing a guide according to the parent's emotions.

[0093] The reception unit can analyze the past symptom input history and suggest preventive measures to the parents. For example, if fever symptoms have been frequently input in the past, advice on how to prevent fevers can be provided. Also, if there have been many coughing symptoms in the past, environmental improvement measures to prevent coughing can be suggested. Furthermore, from the past input history, it is possible to predict symptoms that are common in certain seasons and notify preventive measures in advance. This improves health management by suggesting preventive measures based on past history.

[0094] When inputting symptoms, the reception unit can customize the input items taking into account the lifestyle habit data of the parent. For example, if the parent is a smoker, symptom input items related to smoking are preferentially displayed. Also, if the parent is not getting enough exercise, symptom input items related to exercise can be suggested. Furthermore, if the parent has specific dietary restrictions, symptom input items related to the dietary restrictions can be displayed. This allows for more appropriate symptom input by taking into account lifestyle habit data.

[0095] The reception unit can estimate the parent's emotions and provide feedback on the input based on the estimated parent's emotions. For example, if the parent feels anxious, an encouraging message can be displayed in response to the input. Also, if the parent feels relaxed, detailed feedback can be provided in response to the input to improve the accuracy of the input. Furthermore, if the parent is in a hurry, brief feedback can be provided to allow the parent to proceed to the next step quickly. In this way, by providing feedback according to the parent's emotions, motivation for input is improved.

[0096] When inputting symptoms, the reception unit can customize the input items taking into account the parent's health data. For example, if the parent has high blood pressure, symptom input items related to high blood pressure can be displayed preferentially. Also, if the parent has diabetes, symptom input items related to diabetes can be suggested. Furthermore, if the parent has allergies, symptom input items related to the allergy can be displayed. This allows for more appropriate symptom input by taking health data into account.

[0097] The reception unit can analyze the parent's social media activity and input related information when inputting symptoms. For example, the reception unit can suggest symptoms related to recent activities or events as input items from the parent's social media posts. The reception unit can also analyze posts from the parent's social media friends and followers to display input items related to prevalent diseases and symptoms. Furthermore, the reception unit can suggest symptom input items related to recently visited places based on the parent's social media location information. In this way, related symptoms and information can be provided by analyzing social media activity.

[0098] The analysis unit can estimate the parent's emotions and determine the priority of analysis based on the estimated parent's emotions. For example, if the parent feels anxious, important symptoms are analyzed with priority. Also, if the parent feels relaxed, general symptoms can be analyzed with normal priority. Furthermore, if the parent is in a hurry, symptoms that require quick analysis can be analyzed first. In this way, important information can be provided quickly by setting priorities according to the parent's emotions.

[0099] During analysis, the analysis unit can optimize the analysis algorithm by referring to past symptom data. For example, cases with similar symptoms are identified based on past symptom data, and the analysis algorithm is optimized. The analysis unit can also extract specific patterns from past symptom data and reflect them in the analysis algorithm. Furthermore, the analysis unit can analyze past symptom data and improve the accuracy of the analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to past data.

[0100] During analysis, the analysis unit can apply different analysis methods depending on the symptom category. For example, a standard analysis method is applied to general symptoms such as fever and cough. The analysis unit can also apply an image analysis method to visual symptoms such as skin rashes and swelling. Furthermore, the analysis unit can apply a video analysis method to symptoms inputted as a video to perform a detailed analysis. In this way, the accuracy of the analysis is improved by applying an analysis method according to the symptom category.

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The reception unit receives a message from a parent about the child's symptoms, which may include, but are not limited to, fever, cough, abdominal pain, and the like. The parent can take a picture of the child's fever with a smartphone and upload the video to the system. The parent can also input the child's symptoms in text. The parent can also input the child's symptoms in images. Step 2: The analysis unit uses the generation AI to analyze the information input by the reception unit and provides advice according to the symptoms. For example, the generation AI analyzes the input text information using a text generation AI (e.g., LLM). The generation AI can also analyze the input image information using an image generation AI. Furthermore, the generation AI can also analyze the input video information using a video generation AI. For example, the generation AI analyzes a video of a child's fever input by a parent and provides advice on the cause of the fever and how to deal with it. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides the parent with advice on cooling methods and hydration based on the results of the analysis by the generating AI. The providing unit can also provide the parent with advice to visit a medical institution based on the results of the analysis by the generating AI. Step 4: The information section transitions to night-time emergency care or remote medical care as necessary. For example, if the generation AI judges the symptoms to be severe, the information section provides information on the nearest night-time emergency care. In addition, if remote medical care is possible, the information section allows the patient to receive medical care through a video call with a doctor.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements including the reception unit, analysis unit, provision unit, and guidance unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the parent can input the child's symptoms in text, image, or video. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis result. The guidance unit is realized, for example, by the control unit 46A of the smart device 14, and transitions to nighttime emergency guidance or remote medical treatment as necessary. The reception unit can estimate the parent's emotions and adjust the symptom input interface based on the estimated parent's emotions. For example, if the parent is feeling anxious, a simple and intuitive interface is provided to minimize the input procedure. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements including the reception unit, analysis unit, provision unit, and guidance unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the parent can input the child's symptoms in text, image, or video. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis result. The guidance unit is realized, for example, by the control unit 46A of the smart glasses 214, and transitions to nighttime emergency guidance or remote medical treatment as necessary. The reception unit can estimate the parent's emotions and adjust the symptom input interface based on the estimated parent's emotions. For example, if the parent is feeling anxious, a simple and intuitive interface is provided to minimize the input procedure. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and guidance unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the parent can input the child's symptoms in text, image, or video. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis result. The guidance unit is realized, for example, by the control unit 46A of the headset type terminal 314, and transitions to nighttime emergency guidance or remote medical treatment as necessary. The reception unit can estimate the parent's emotions and adjust the symptom input interface based on the estimated parent's emotions. For example, if the parent is feeling anxious, a simple and intuitive interface is provided to minimize the input procedure. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements including the reception unit, analysis unit, provision unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the parent can input the child's symptoms in text, image, or video. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis result. The guidance unit is realized, for example, by the control unit 46A of the robot 414, and transitions to nighttime emergency guidance or remote medical treatment as necessary. The reception unit can estimate the parent's emotions and adjust the symptom input interface based on the estimated parent's emotions. For example, if the parent is feeling anxious, a simple and intuitive interface is provided to minimize the input procedure. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] 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.

[0157] 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.

[0158] These emotions are distributed in the 3 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.

[0159] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Appendix 1) A reception area where parents can enter their child's symptoms using text, images, or videos; an analysis unit that analyzes the information input by the reception unit and provides advice according to symptoms; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A guidance section that provides guidance for night-time emergency care or transitions to remote medical care as necessary. A system characterized by: (Appendix 2) The reception unit is Estimating parental emotions and adjusting the symptom input interface based on the estimated parental emotions 2. The system of claim 1, (Appendix 3) The reception unit is Analyzes past symptom input history and suggests the most suitable input method 2. The system of claim 1, (Appendix 4) The reception unit is When entering symptoms, customize the input fields based on the child's age and medical history 2. The system of claim 1, (Appendix 5) The reception unit is Estimate parental emotions and prioritize inputs based on the estimated parental emotions 2. The system of claim 1, (Appendix 6) The reception unit is When entering symptoms, prioritize relevant information based on parent's geographic location 2. The system of claim 1, (Appendix 7) The reception unit is When symptoms are entered, the system analyzes the parent's social media activity and enters relevant information 2. The system of claim 1, (Appendix 8) The analysis unit is Estimate the parent's emotions and adjust the accuracy of the analysis based on the estimated parent's emotions. 2. The system of claim 1, (Appendix 9) The analysis unit is During analysis, past symptom data is referenced to optimize the analysis algorithm 2. The system of claim 1, (Appendix 10) The analysis unit is During analysis, different analysis methods are applied depending on the symptom category. 2. The system of claim 1, (Appendix 11) The analysis unit is Infer parental sentiment and adjust the way analysis results are displayed based on the inferred parental sentiment 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, prioritize analysis based on when symptoms first appeared 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, refer to literature related to symptoms to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 14) The providing unit is Infer parental emotions and adjust advice presentation based on the inferred parental emotions 2. The system of claim 1, (Appendix 15) The providing unit is When providing advice, adjust the level of detail based on the severity of the symptoms 2. The system of claim 1, (Appendix 16) The providing unit is When providing advice, apply different advice techniques depending on symptom categories 2. The system of claim 1, (Appendix 17) The providing unit is Estimate parental emotions and prioritize advice based on the estimated parental emotions 2. The system of claim 1, (Appendix 18) The providing unit is When providing advice, adjust the order of advice based on when symptoms occur 2. The system of claim 1, (Appendix 19) The providing unit is When providing advice, refer to relevant symptom information to improve the accuracy of the advice 2. The system of claim 1, (Appendix 20) The guide unit is Infer parental emotions and adjust how guidance is displayed based on the inferred parental emotions 2. The system of claim 1, (Appendix 21) The guide unit is When providing guidance, the most appropriate guidance method is selected by referring to past guidance history. 2. The system of claim 1, (Appendix 22) The guide unit is When providing guidance, adjust the level of detail based on the severity of the symptoms 2. The system of claim 1, (Appendix 23) The guide unit is Estimate the parent's emotions and determine the priority of notifications based on the estimated parent's emotions 2. The system of claim 1, (Appendix 24) The guide unit is When providing guidance, the optimal guidance method is selected taking into account the parent's geographic location information. 2. The system of claim 1, (Appendix 25) The guide unit is When providing guidance, refer to relevant information about symptoms to improve the accuracy of guidance 2. The system of claim 1, [Explanation of symbols]

[0175] 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 area where parents can enter their child's symptoms using text, images, or videos; an analysis unit that analyzes the information input by the reception unit and provides advice according to symptoms; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A guidance section that provides guidance for night-time emergency care or transitions to remote medical care as necessary. A system characterized by:

2. The reception unit is Estimating parental emotions and adjusting the symptom input interface based on the estimated parental emotions 2. The system of claim 1 .

3. The reception unit is Analyzes past symptom input history and suggests the most suitable input method 2. The system of claim 1 .

4. The reception unit is When entering symptoms, customize the input fields based on the child's age and medical history 2. The system of claim 1 .

5. The reception unit is Estimate parental emotions and prioritize inputs based on the estimated parental emotions 2. The system of claim 1 .

6. The reception unit is When entering symptoms, prioritize relevant information based on parent's geographic location 2. The system of claim 1 .

7. The reception unit is When symptoms are entered, the system analyzes the parent's social media activity and enters relevant information 2. The system of claim 1 .

8. The analysis unit is Estimate the parent's emotions and adjust the accuracy of the analysis based on the estimated parent's emotions.

2. The system of claim 1 .

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