System

Abstract

To provide a system for giving support such that a parent can rapidly and appropriately cope with a change in a child's physical condition.SOLUTION: A system includes a reception section, an analysis section, a proposal section, and a guidance section. The reception section allows a parent to input a change in a child's physical condition. The analysis section analyzes information inputted by the reception section. The proposal section proposes a countermeasure on the basis of information analyzed by the analysis section. The guidance section performs guidance to hospitals and emergency service on the basis of a countermeasure proposed by the proposal section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technology makes it difficult for parents to respond quickly and appropriately to changes in their child's physical condition, which can lead to confusion and anxiety in an emergency.

[0005] The system according to the embodiment aims to support parents in responding promptly and appropriately to changes in their children's physical condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a guidance unit. The reception unit allows a parent to input information about changes in the child's physical condition. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes a response method based on the information analyzed by the analysis unit. The guidance unit guides the parent to a hospital or emergency service based on the response method proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can support parents in quickly and appropriately responding to changes in their child's physical condition. [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 the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical support system according to the embodiment of the present invention is a system in which a parent of a newborn baby inputs changes in the child's physical condition, and an AI analyzes the information and proposes an appropriate response method. In this medical support system, a parent inputs changes in the child's physical condition, and an AI analyzes the information and proposes an appropriate response method, thereby guiding the child to a hospital or emergency services. For example, when a parent inputs a child's fever into the system, the AI ​​analyzes the information and proposes home measures such as "have the child drink a lot of fluids" and "cool the body." In addition, if the fever continues or other symptoms appear, the AI ​​issues instructions such as "take the child to the hospital" and "call emergency services." This service allows parents to respond appropriately in an emergency without feeling confused or anxious. Furthermore, even if a parent is feeling anxious about raising a child for the first time, they can use this service to respond with peace of mind. As a result, the medical support system allows a parent to input changes in the child's physical condition, and an AI analyzes the information, proposes an appropriate response method, and guides the child to a hospital or emergency services.

[0029] The medical support system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit is used by a parent to input changes in the child's physical condition. The changes in physical condition input by the parent include, but are not limited to, fever, cough, stomachache, and the like. The reception unit is used by a parent to input changes in the child's physical condition in text format. The reception unit can also convert the contents dictated by the parent into text using voice input. For example, the reception unit converts the contents dictated by the parent into text data using voice recognition technology. The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed, for example, by referring to a database, but is not limited to the example. For example, the analysis unit refers to a database regarding changes in the child's physical condition and suggests an appropriate response method. The suggestion unit uses AI to suggest an appropriate response method based on the information analyzed by the analysis unit. The suggestion is, for example, used at home to suggest measures to be taken, but is not limited to the example. For example, in the case of a fever, the suggestion unit suggests measures to be taken at home, such as "have the child drink a lot of fluids" and "cool the body." The guidance unit guides the child to a hospital or emergency services based on the response method proposed by the suggestion unit. The guidance is performed, for example, when the child has a persistent fever or when other symptoms appear, but is not limited to such examples. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services." In this way, the medical support system according to the embodiment allows parents to input changes in the child's physical condition, and the AI ​​analyzes the information, suggests appropriate response methods, and guides the child to a hospital or emergency services.

[0030] The reception unit allows a parent to input changes in the child's physical condition. Examples of changes in physical condition input by a parent include, but are not limited to, fever, cough, stomachache, and the like. The reception unit allows a parent to input changes in the child's physical condition in text format. The reception unit can also convert the contents dictated by the parent into text using voice input. For example, the reception unit converts the contents dictated by the parent into text data using voice recognition technology. Specifically, the reception unit allows a parent to input changes in physical condition through a dedicated application or web interface using a device such as a smartphone or tablet. In the case of text input, the parent can describe symptoms in detail, and can provide specific information such as, for example, "I have had a fever of 38 degrees since yesterday, and my cough has gotten worse." In the case of voice input, the parent simply speaks into the device, and the voice recognition technology automatically converts the contents into text. The voice recognition technology uses natural language processing (NLP) to accurately understand what the parent is saying and converts it into appropriate text data. For example, if a parent says, "My child has been saying that he has had a stomachache since yesterday," the contents are recorded as text data. Furthermore, the reception unit also has a function for centrally managing the input data and checking the consistency and completeness of the data before sending it to the analysis unit. This allows the reception unit to provide an environment where parents can easily and quickly input changes in their child's physical condition, thereby improving the accuracy of subsequent analysis and suggestions.

[0031] The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed, for example, by referring to a database, but is not limited to such an example. For example, the analysis unit refers to a database regarding changes in the child's physical condition and proposes an appropriate response method. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the input text data and identify patterns and associations of symptoms. For example, if "fever" and "cough" are reported at the same time, the analysis unit searches the database to find out what disease or condition these symptoms are related to. Furthermore, the analysis unit uses machine learning algorithms to predict how the current symptoms are likely to progress based on past data and cases. For example, it analyzes what kind of treatment children with similar symptoms have received in the past and what kind of progress they have made, and applies it to the current case. Based on this information, the analysis unit identifies the most appropriate response method and transmits it to the suggestion unit. In addition, the analysis unit can perform analysis based on the latest knowledge by referring to medical information and guidelines that are updated in real time. This allows the analysis unit to quickly and accurately analyze the information input by the parent and provide a foundation for suggesting appropriate response methods.

[0032] The suggestion unit uses AI to suggest an appropriate response method based on the information analyzed by the analysis unit. The suggestion may be, for example, a response method at home, but is not limited to such an example. For example, in the case of a fever, the suggestion unit suggests a response method at home, such as "have the child drink a lot of fluids" or "cool the body." Specifically, the suggestion unit provides the parent with a specific response method according to the symptoms based on the information provided by the analysis unit. For example, in the case of a fever, the suggestion unit provides specific advice such as "cool the forehead with a cold towel," "maintain moderate hydration," and "wear light clothing." In addition, in the case of a severe cough, the suggestion unit suggests a response method such as "use a humidifier to maintain humidity" or "have the child drink hot drinks." The suggestion unit can use text messages, audio guides, and even videos and illustrations to convey this information to the parent in an easy-to-understand manner. For example, a video of the response method can be played within the application so that the parent can visually understand the specific steps. In addition, the suggestion unit can monitor the effectiveness of the suggestions and provide additional advice or corrections as necessary. For example, if the fever continues or new symptoms appear, the suggestion unit will send that information back to the analysis unit and make new suggestions based on the latest analysis results. This allows the suggestion unit to support parents in taking appropriate measures at home and effectively manage their children's health.

[0033] The guidance unit guides the child to a hospital or emergency services based on the response method proposed by the suggestion unit. The guidance is performed, for example, when the fever continues or when other symptoms appear, but is not limited to such examples. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services." Specifically, the guidance unit provides specific action instructions to the parent based on the information provided by the suggestion unit. For example, if the fever continues, specific instructions such as "contact the nearest pediatrician and have the child examined." In addition, if the symptoms suddenly worsen, emergency instructions such as "call emergency services immediately and receive emergency treatment" are provided. The guidance unit can use the notification function of the smartphone, voice alerts, and even SMS and email to quickly and reliably convey these instructions to the parent. For example, a pop-up notification can be displayed within the application to enable the parent to take action immediately. The guidance unit also has a function of linking with hospitals and emergency services, and can make reservations or contact on behalf of the parent as necessary. For example, the application can be used to make reservations at the nearest hospital or to contact emergency services. This allows the guidance section to support parents in taking prompt and appropriate action, providing optimal responses to protect the health of their children.

[0034] The medical support system includes an information detailing unit that details the type of information input by the parent. The information detailing unit details the type of information input by the parent. The information input by the parent includes, for example, details of symptoms, occurrence time, frequency, etc., but is not limited to such examples. For example, the information detailing unit allows the parent to input details of symptoms when inputting changes in the child's physical condition. The information detailing unit can also allow the parent to input the occurrence time and frequency. For example, the information detailing unit allows the parent to input the occurrence time and frequency when inputting changes in the child's physical condition. By this, by detailing the type of information input by the parent, a more accurate response method can be proposed. A part or all of the above-mentioned processing in the information detailing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information detailing unit can input the type of information input by the parent to AI and allow AI to execute the information detailing.

[0035] The medical support system includes a database reference unit that causes the AI ​​to refer to a database. The database reference unit causes the AI ​​to refer to a database. The database to be referred to includes, for example, a medical database, a case database, and the like, but is not limited to such examples. For example, the database reference unit causes the AI ​​to refer to the medical database and propose an appropriate response method. The database reference unit can also refer to a case database. For example, the database reference unit causes the AI ​​to refer to the case database and propose an appropriate response method. As a result, the AI ​​can suggest an appropriate response method by referring to the database. A part or all of the above-mentioned processing in the database reference unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the database reference unit can cause the AI ​​to execute the database reference method when the AI ​​refers to the database.

[0036] The medical support system includes a supervision unit that is supervised by a medical expert. The supervision unit is supervised by a medical expert. The supervising medical expert includes, for example, a doctor, a nurse, a medical consultant, and the like, but is not limited to such examples. For example, the supervision unit is supervised by a doctor. The supervision unit can also be supervised by a nurse. For example, the supervision unit is supervised by a nurse. Furthermore, the supervision unit can also be supervised by a medical consultant. For example, the supervision unit is supervised by a medical consultant. As a result, by receiving supervision from a medical expert, a highly reliable response method can be proposed. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the supervision content of the medical expert to AI and cause AI to execute detailed processing of the supervision content.

[0037] The analysis unit can refer to a database related to changes in the child's physical condition and propose an appropriate response method. The analysis unit, for example, refers to a database related to changes in the child's physical condition. The database to be referred to includes, for example, a medical database, a case database, and the like, but is not limited to such examples. For example, the analysis unit refers to a medical database and proposes an appropriate response method. The analysis unit can also refer to a case database. For example, the analysis unit refers to a case database and proposes an appropriate response method. In this way, by referring to the database related to changes in the child's physical condition, an appropriate response method can be proposed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute the method of referring to the database.

[0038] The suggestion unit can suggest home remedies such as "have the child drink fluids" and "cool the body" in the case of fever. For example, in the case of fever, the suggestion unit suggests home remedies such as "have the child drink fluids" and "cool the body". Home remedies include, for example, a method of rehydrating and a method of cooling the body, but are not limited to such examples. For example, the suggestion unit suggests having the child drink cold drinks as a method of rehydrating. The suggestion unit can also suggest using a cold towel as a method of cooling the body. For example, the suggestion unit suggests using a cold towel to cool the body. This allows the parent to respond appropriately by suggesting home remedies in the case of fever. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input home remedies to AI and cause the AI ​​to execute the proposed remedies.

[0039] The guidance unit can issue instructions to "take the child to the hospital" or "call emergency services" if the fever continues or other symptoms appear. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services" if the fever continues or other symptoms appear. The instructions include, for example, the timing of taking the child to the hospital and the criteria for calling emergency services, but are not limited to such examples. For example, the guidance unit instructs the child to be taken to the hospital if the fever continues. The guidance unit can also instruct the child to call emergency services if other symptoms appear. For example, the guidance unit instructs the child to call emergency services if breathing difficulties appear. This allows the parent to respond quickly by issuing appropriate instructions when the fever continues or other symptoms appear. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the instruction content to AI and have the AI ​​execute the output of the instruction.

[0040] The reception unit can analyze the parent's past input history and suggest the optimal input method. The reception unit can analyze, for example, the parent's past input history and suggest the optimal input method. The input method includes, for example, text input, voice input, and the like, but is not limited to such examples. For example, the reception unit automatically displays physical condition changes that the parent frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the parent used in the past. For example, if the parent used voice input in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest physical condition changes that will be input in a specific time period from the parent's past input history. For example, the reception unit predicts physical condition changes that will be input at night and suggests an input method suitable for nighttime. In this way, the parent's past input history can be analyzed to suggest the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's past input history to AI and have the AI ​​execute the suggestion of the optimal input method.

[0041] The reception unit can automatically complete the input contents based on the parent's current situation and environment when inputting the child's physical condition change. For example, the reception unit automatically completes the input contents based on the parent's current situation and environment when inputting the child's physical condition change. The automatic completion includes, for example, predictive input, template input, and other methods, but is not limited to such examples. For example, when the parent is out, the reception unit proposes a simple input method and automatically completes the input contents. In addition, when the parent is at home, the reception unit can provide detailed input options and automatically complete the input contents. For example, when the parent is at home, the reception unit provides detailed input options and automatically completes the input contents. Furthermore, when the parent enters at night, the reception unit can automatically complete the input contents by referring to the past nighttime input history. For example, the reception unit predicts physical condition changes to be entered at night and automatically completes the input contents. As a result, the input work is made more efficient by automatically completing the input contents based on the parent's current situation and environment. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the parent's current situation and environmental data into the AI ​​and have the AI ​​automatically complete the input content.

[0042] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting a change in the child's physical condition. For example, the reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting a change in the child's physical condition. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the reception unit can preferentially input information related to the hospital. In addition, when the parent is at home, the reception unit can also preferentially input information related to measures at home. For example, when the parent is at home, the reception unit can also preferentially input information related to measures at home. Furthermore, when the parent is traveling, the reception unit can also preferentially input information related to a medical institution at the travel destination. For example, when the parent is traveling, the reception unit can preferentially input information related to a medical institution at the travel destination. In this way, highly relevant information can be preferentially input by considering the geographical location information of the parent. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographic location information of the parents to the AI ​​and cause the AI ​​to prioritize input of highly relevant information.

[0043] The reception unit can analyze the social media activity of the parent when inputting the change in the child's physical condition, and input related information. The reception unit can analyze the social media activity of the parent when inputting the change in the child's physical condition, and input related information. The social media activity includes, for example, the content of posts and the frequency of activities, but is not limited to such examples. For example, the reception unit automatically completes the input content based on the information shared by the parent on social media. The reception unit can also suggest the input content with reference to the information of medical experts that the parent follows on social media. For example, the reception unit can suggest the input content with reference to the information of medical experts that the parent follows on social media. Furthermore, the reception unit can automatically complete the input content based on the information of groups that the parent participates in on social media. For example, the reception unit automatically completes the input content based on the information of groups that the parent participates in on social media. In this way, the parent's social media activity can be analyzed to efficiently input related information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to AI and cause the AI ​​to execute the input of related information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's physical condition change during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the child's physical condition change during the analysis. The evaluation of the importance includes, for example, the severity of symptoms, the frequency of occurrence, and the like, but is not limited to such examples. For example, the analysis unit provides a concise analysis result when the child's physical condition change is mild. In addition, the analysis unit can also provide a detailed analysis result when the child's physical condition change is moderate. Furthermore, the analysis unit can also provide a very detailed analysis result when the child's physical condition change is severe. Thereby, by adjusting the level of detail of the analysis based on the importance of the child's physical condition change, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the child's physical condition change to AI and cause AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms according to the category of the child's physical condition change during analysis. The analysis unit, for example, applies different analysis algorithms according to the category of the child's physical condition change during analysis. The category classification includes, for example, the type of symptoms, the cause of occurrence, and the like, but is not limited to such examples. For example, in the case of fever, the analysis unit applies an analysis algorithm based on body temperature data. In addition, in the case of coughing, the analysis unit can also apply an analysis algorithm based on breathing data. Furthermore, in the case of a rash, the analysis unit can also apply an analysis algorithm based on skin data. In this way, by applying different analysis algorithms according to the category of the child's physical condition change, appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the child's physical condition change to AI and cause AI to apply the analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the time of occurrence of the child's physical condition change during the analysis. The analysis unit, for example, determines the priority of the analysis based on the time of occurrence of the child's physical condition change during the analysis. The evaluation of the time of occurrence includes, for example, the date and time of occurrence, the frequency of occurrence, etc., but is not limited to such examples. For example, the analysis unit prioritizes the analysis of recent physical condition changes. The analysis unit can also determine the priority of the analysis by referring to past physical condition changes. Furthermore, the analysis unit can also prioritize the analysis of physical condition changes that occurred in a specific time period. As a result, important information can be analyzed preferentially by determining the priority of the analysis based on the time of occurrence of the child's physical condition change. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the child's physical condition change to AI and cause AI to execute the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the child's physical condition changes during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the child's physical condition changes during analysis. The evaluation of the relevance includes, for example, a co-occurrence relationship of symptoms, a causal relationship, and the like, but is not limited to such examples. For example, the analysis unit prioritizes analysis of highly related physical condition changes. The analysis unit can also postpone the analysis of less related physical condition changes. Furthermore, when multiple physical condition changes are related, the analysis unit can also analyze them collectively. As a result, by adjusting the order of analysis based on the relevance of the child's physical condition changes, related information can be analyzed efficiently. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the child's physical condition changes to AI and cause AI to adjust the order of analysis.

[0048] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the response method at the time of suggestion. The evaluation of the importance includes, for example, the severity of the symptoms and the urgency of the response, but is not limited to such examples. For example, the suggestion unit provides a concise proposal in the case of a mild change in physical condition. The suggestion unit can also provide a detailed proposal in the case of a moderate change in physical condition. Furthermore, the suggestion unit can also provide a very detailed proposal in the case of a severe change in physical condition. Thereby, by adjusting the level of detail of the proposal based on the importance of the response method, an appropriate proposal can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the response method to AI and cause AI to adjust the level of detail of the proposal.

[0049] The suggestion unit can apply different proposal algorithms according to the category of the response method at the time of suggestion. For example, the suggestion unit applies different proposal algorithms according to the category of the response method at the time of suggestion. The classification of categories includes, for example, the type of symptoms and the type of response, but is not limited to such examples. For example, the suggestion unit provides a proposal regarding hydration and body temperature management in the case of fever. In addition, the suggestion unit can also provide a proposal regarding breathing management and humidity control in the case of cough. Furthermore, the suggestion unit can also provide a proposal regarding skin care and allergy measures in the case of a rash. In this way, by applying different proposal algorithms according to the category of the response method, appropriate proposals can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input category data of the response method to AI and cause AI to apply the proposal algorithm.

[0050] The suggestion unit can determine the priority of the proposal based on the occurrence time of the response method at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the occurrence time of the response method at the time of proposal. The evaluation of the occurrence time includes, for example, the occurrence date and time, the occurrence frequency, etc., but is not limited to such examples. For example, the suggestion unit provides a proposal for a recent physical condition change with priority. The suggestion unit can also determine the priority of the proposal with reference to past physical condition changes. Furthermore, the suggestion unit can also provide a proposal for a physical condition change that occurred in a specific time period with priority. As a result, by determining the priority of the proposal based on the occurrence time of the response method, important proposals can be provided with priority. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the occurrence time data of the response method to AI and cause AI to execute the determination of the priority of the proposal.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods at the time of suggestion. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the response methods at the time of suggestion. The evaluation of the relevance includes, for example, a co-occurrence relationship of symptoms, a causal relationship, and the like, but is not limited to such examples. For example, the suggestion unit preferentially suggests highly relevant response methods. The suggestion unit can also postpone less relevant response methods. Furthermore, when multiple response methods are related, the suggestion unit can also propose them collectively. As a result, by adjusting the order of suggestions based on the relevance of the response methods, related information can be efficiently suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data of the response methods to AI and cause AI to adjust the order of suggestions.

[0052] The guiding unit can select the optimal guiding method by analyzing the parent's past behavior history at the time of guiding. For example, the guiding unit selects the optimal guiding method by analyzing the parent's past behavior history at the time of guiding. The analysis of the behavior history includes, for example, past response methods, behavior patterns, etc., but is not limited to such examples. For example, the guiding unit selects the optimal guiding method based on medical institutions used by the parent in the past. In addition, the guiding unit can also select the most efficient guiding method from the parent's past behavior history. Furthermore, the guiding unit can also suggest an appropriate guiding method by referring to the parent's past behavior history. In this way, the optimal guiding method can be selected by analyzing the parent's past behavior history. A part or all of the above-mentioned processing in the guiding unit may be performed using, for example, AI, or may be performed without using AI. For example, the guiding unit can input the parent's past behavior history data into AI and cause the AI ​​to select the optimal guiding method.

[0053] The guidance unit can customize the guidance means based on the parent's current situation when guiding. The guidance unit, for example, customizes the guidance means based on the parent's current situation when guiding. The evaluation of the current situation includes, for example, the current physical condition, the environmental situation, and the like, but is not limited to such examples. For example, when the parent is driving a car, the guidance unit prioritizes voice guidance to guide the parent. In addition, when the parent is using public transportation, the guidance unit can also guide the parent by text message. Furthermore, when the parent is at home, the guidance unit can also provide guidance including detailed procedures. In this way, by customizing the guidance means based on the parent's current situation, guidance that is easy for the parent to understand can be provided. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the parent's current situation data into AI and have the AI ​​execute customization of the guidance means.

[0054] The guidance unit can select the optimal guidance method in consideration of the geographical location information of the parent at the time of guidance. For example, the guidance unit selects the optimal guidance method in consideration of the geographical location information of the parent at the time of guidance. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the guidance unit guides the shortest route to the hospital. In addition, when the parent is at home, the guidance unit can also guide the parent to a home remedy with priority. Furthermore, when the parent is traveling, the guidance unit can also guide the parent to a medical institution at the travel destination. In this way, the optimal guidance method can be selected by considering the geographical location information of the parent. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the geographical location information data of the parent to AI and cause the AI ​​to select the optimal guidance method.

[0055] The guidance unit can analyze the social media activity of the parent at the time of guidance and suggest a means of guidance. For example, the guidance unit can analyze the social media activity of the parent at the time of guidance and suggest a means of guidance. Social media activity includes, for example, the content of posts and the frequency of activity, but is not limited to such examples. For example, the guidance unit can suggest an optimal guidance method based on information shared by the parent on social media. The guidance unit can also perform guidance by referring to information on medical professionals followed by the parent on social media. Furthermore, the guidance unit can also suggest an appropriate guidance method based on information on groups in which the parent participates on social media. In this way, the optimal guidance means can be suggested by analyzing the social media activity of the parent. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the social media activity data of the parent into AI and cause the AI ​​to execute the suggestion of a means of guidance.

[0056] The information detail unit can provide optimal information details by referring to the parent's past input history. The information detail unit can provide optimal information details by referring to the parent's past input history, for example. The input history reference includes, for example, past input contents, input frequency, etc., but is not limited to such examples. For example, the information detail unit provides optimal information details based on information previously input by the parent. The information detail unit can also provide highly relevant information preferentially from the parent's past input history. Furthermore, the information detail unit can analyze the parent's past input history and provide the most efficient information details. As a result, optimal information details can be provided by referring to the parent's past input history. A part or all of the above-mentioned processing in the information detail unit can be performed, for example, using AI, or can be performed without using AI. For example, the information detail unit can input the parent's past input history data into AI and cause AI to provide optimal information details.

[0057] The information detail unit can provide optimal information details by considering the geographical location information of the parent. The information detail unit provides optimal information details by considering, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, the information detail unit provides detailed information about the hospital when the parent is near the hospital. In addition, the information detail unit can also provide detailed information about how to deal with the problem at home when the parent is at home. Furthermore, the information detail unit can also provide detailed information about medical institutions at the travel destination when the parent is traveling. This allows optimal information details to be provided by considering the geographical location information of the parent. A part or all of the above-mentioned processing in the information detail unit may be performed, for example, using AI, or may be performed without using AI. For example, the information detail unit can input the geographical location information data of the parent to AI and cause the AI ​​to provide optimal information details.

[0058] The database reference unit can provide an optimal reference method by analyzing the past reference history. The database reference unit can provide an optimal reference method by analyzing the past reference history, for example. The analysis of the reference history includes, for example, reference contents and reference frequency, but is not limited to such examples. For example, the database reference unit provides an optimal reference method based on data previously referenced by the parent. The database reference unit can also provide highly relevant data preferentially from the parent's past reference history. Furthermore, the database reference unit can also analyze the parent's past reference history and provide the most efficient reference method. In this way, the optimal reference method can be provided by analyzing the past reference history. A part or all of the above-mentioned processing in the database reference unit can be performed, for example, using AI, or can be performed without using AI. For example, the database reference unit can input the parent's past reference history data into AI and cause AI to provide the optimal reference method.

[0059] The database reference unit can provide an optimal database reference method in consideration of the geographical location information of the parent. The database reference unit provides an optimal database reference method in consideration of, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the database reference unit preferentially refers to data related to the hospital. In addition, when the parent is at home, the database reference unit can also preferentially refer to data related to home remedies. Furthermore, when the parent is traveling, the database reference unit can also preferentially refer to data related to medical institutions at the travel destination. In this way, by considering the geographical location information of the parent, an optimal database reference method can be provided. Part or all of the above-mentioned processing in the database reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the database reference unit can input the geographical location information data of the parent to AI and cause the AI ​​to execute the provision of the optimal database reference method.

[0060] The supervision unit can provide optimal supervision content by referring to the past supervision history. The supervision unit, for example, provides optimal supervision content by referring to the past supervision history. The reference to the supervision history includes, for example, supervision content, supervision frequency, etc., but is not limited to such examples. For example, the supervision unit provides optimal supervision content based on the supervision content referred to by the parent in the past. The supervision unit can also provide highly relevant supervision content preferentially from the parent's past supervision history. Furthermore, the supervision unit can analyze the parent's past supervision history and provide the most efficient supervision content. As a result, optimal supervision content can be provided by referring to the past supervision history. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the parent's past supervision history data into AI and cause AI to provide optimal supervision content.

[0061] The supervision unit can provide optimal supervision content by considering the geographical location information of the parent. The supervision unit provides optimal supervision content by considering, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the supervision unit can preferentially provide supervision content related to the hospital. In addition, when the parent is at home, the supervision unit can also preferentially provide supervision content related to how to deal with the problem at home. Furthermore, when the parent is traveling, the supervision unit can also preferentially provide supervision content related to medical institutions at the travel destination. In this way, optimal supervision content can be provided by considering the geographical location information of the parent. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the geographical location information data of the parent to AI and cause the AI ​​to provide optimal supervision content.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The medical support system can further include a lifestyle analysis unit that takes into account the lifestyle of the parent. The lifestyle analysis unit collects data on the parent's diet, exercise, sleep patterns, etc., and proposes an appropriate response method to changes in the child's physical condition based on this information. For example, if the parent works the night shift, a response method for the night can be proposed. Also, if the parent has specific dietary restrictions, a response method based on those restrictions can be proposed. Furthermore, if the parent exercises frequently, a response method for changes in physical condition after exercise can be proposed. In this way, by taking the parent's lifestyle into account, a more individualized response method can be provided.

[0064] The medical support system may further include a medical history reference unit that references the parent's past medical history. The medical history reference unit collects data such as the parent's past medical history, allergy information, and medications being taken, and suggests an appropriate response method for changes in the child's physical condition based on this information. For example, if the parent has a specific allergy, it can suggest a response method based on the allergy. Also, if the parent is taking a specific medication, it can suggest a response method that takes into account the effects of the medication. Furthermore, it can also suggest a response method when a similar symptom appears, referring to the parent's past medical history. In this way, a more appropriate response method can be provided by taking into account the parent's past medical history.

[0065] The medical support system can further include a social background analysis unit that takes into account the social background of the parents. The social background analysis unit collects data on the parents' occupation, family environment, economic situation, etc., and suggests appropriate ways of responding to changes in the child's physical condition based on this information. For example, if the parent is a single mother, it can provide information on support services. Also, if the parent has a low income, it can suggest medical services to reduce the economic burden. Furthermore, if the parent has a busy job, it can suggest ways of responding to use time efficiently. In this way, by taking the social background of the parents into account, it is possible to provide more appropriate ways of responding.

[0066] The medical support system can further include a cultural background analysis unit that takes into account the cultural background of the parents. The cultural background analysis unit collects data such as the parents' religion, customs, and language, and suggests an appropriate way of responding to changes in the child's physical condition based on this information. For example, if the parents believe in a particular religion, it can suggest a way of responding based on that religion. Also, if the parents have particular cultural customs, it can suggest a way of responding based on that custom. Furthermore, if the parents speak a different language, it can suggest a way of responding in that language. In this way, a more appropriate way of responding can be provided by taking the parents' cultural background into account.

[0067] The medical support system can further include a communication analysis unit that takes into account the parent's communication style. The communication analysis unit collects data such as the parent's speaking style, writing style, and expression style, and suggests an appropriate response method for a change in the child's physical condition based on this information. For example, if the parent prefers short and concise expressions, the system can suggest a concise response method. Also, if the parent prefers detailed explanations, the system can also suggest a detailed response method. Furthermore, if the parent prefers visual information, the system can also suggest a response method using diagrams and graphs. In this way, a more appropriate response method can be provided by taking the parent's communication style into consideration.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The reception unit receives a parent's input of changes in the child's physical condition. Examples of changes in physical condition that the parent may input include fever, cough, stomachache, and the like. The reception unit allows the parent to input the changes in the child's physical condition in text format, and can also convert what the parent dictates into text using voice input. The reception unit converts the parent's dictation into text data using voice recognition technology. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is performed, for example, by referring to a database. The analysis unit refers to a database regarding changes in the child's physical condition and proposes an appropriate response method. Step 3: The suggestion unit uses AI to suggest appropriate measures based on the information analyzed by the analysis unit. For example, the suggestion unit suggests measures to be taken at home. In the case of a fever, the suggestion unit suggests measures to be taken at home, such as "have the patient drink lots of fluids" and "cool the body." Step 4: The guidance unit guides the user to a hospital or emergency services based on the response method proposed by the suggestion unit. Guidance is performed, for example, if the fever continues or other symptoms appear. The guidance unit issues instructions such as "take the user to a hospital" or "call emergency services."

[0070] (Example 2) The medical support system according to the embodiment of the present invention is a system in which a parent of a newborn baby inputs changes in the child's physical condition, and an AI analyzes the information and proposes an appropriate response method. In this medical support system, a parent inputs changes in the child's physical condition, and an AI analyzes the information and proposes an appropriate response method, thereby guiding the child to a hospital or emergency services. For example, when a parent inputs a child's fever into the system, the AI ​​analyzes the information and proposes home measures such as "have the child drink a lot of fluids" and "cool the body." In addition, if the fever continues or other symptoms appear, the AI ​​issues instructions such as "take the child to the hospital" and "call emergency services." This service allows parents to respond appropriately in an emergency without feeling confused or anxious. Furthermore, even if a parent is feeling anxious about raising a child for the first time, they can use this service to respond with peace of mind. As a result, the medical support system allows a parent to input changes in the child's physical condition, and an AI analyzes the information, proposes an appropriate response method, and guides the child to a hospital or emergency services.

[0071] The medical support system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit is used by a parent to input changes in the child's physical condition. The changes in physical condition input by the parent include, but are not limited to, fever, cough, stomachache, and the like. The reception unit is used by a parent to input changes in the child's physical condition in text format. The reception unit can also convert the contents dictated by the parent into text using voice input. For example, the reception unit converts the contents dictated by the parent into text data using voice recognition technology. The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed, for example, by referring to a database, but is not limited to the example. For example, the analysis unit refers to a database regarding changes in the child's physical condition and suggests an appropriate response method. The suggestion unit uses AI to suggest an appropriate response method based on the information analyzed by the analysis unit. The suggestion is, for example, used at home to suggest measures to be taken, but is not limited to the example. For example, in the case of a fever, the suggestion unit suggests measures to be taken at home, such as "have the child drink a lot of fluids" and "cool the body." The guidance unit guides the child to a hospital or emergency services based on the response method proposed by the suggestion unit. The guidance is performed, for example, when the child has a persistent fever or when other symptoms appear, but is not limited to such examples. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services." In this way, the medical support system according to the embodiment allows parents to input changes in the child's physical condition, and the AI ​​analyzes the information, suggests appropriate response methods, and guides the child to a hospital or emergency services.

[0072] The reception unit allows a parent to input changes in the child's physical condition. Examples of changes in physical condition input by a parent include, but are not limited to, fever, cough, stomachache, and the like. The reception unit allows a parent to input changes in the child's physical condition in text format. The reception unit can also convert the contents dictated by the parent into text using voice input. For example, the reception unit converts the contents dictated by the parent into text data using voice recognition technology. Specifically, the reception unit allows a parent to input changes in physical condition through a dedicated application or web interface using a device such as a smartphone or tablet. In the case of text input, the parent can describe symptoms in detail, and can provide specific information such as, for example, "I have had a fever of 38 degrees since yesterday, and my cough has gotten worse." In the case of voice input, the parent simply speaks into the device, and the voice recognition technology automatically converts the contents into text. The voice recognition technology uses natural language processing (NLP) to accurately understand what the parent is saying and converts it into appropriate text data. For example, if a parent says, "My child has been saying that he has had a stomachache since yesterday," the contents are recorded as text data. Furthermore, the reception unit also has a function for centrally managing the input data and checking the consistency and completeness of the data before sending it to the analysis unit. This allows the reception unit to provide an environment where parents can easily and quickly input changes in their child's physical condition, thereby improving the accuracy of subsequent analysis and suggestions.

[0073] The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed, for example, by referring to a database, but is not limited to such an example. For example, the analysis unit refers to a database regarding changes in the child's physical condition and proposes an appropriate response method. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the input text data and identify patterns and associations of symptoms. For example, if "fever" and "cough" are reported at the same time, the analysis unit searches the database to find out what disease or condition these symptoms are related to. Furthermore, the analysis unit uses machine learning algorithms to predict how the current symptoms are likely to progress based on past data and cases. For example, it analyzes what kind of treatment children with similar symptoms have received in the past and what kind of progress they have made, and applies it to the current case. Based on this information, the analysis unit identifies the most appropriate response method and transmits it to the suggestion unit. In addition, the analysis unit can perform analysis based on the latest knowledge by referring to medical information and guidelines that are updated in real time. This allows the analysis unit to quickly and accurately analyze the information input by the parent and provide a foundation for suggesting appropriate response methods.

[0074] The suggestion unit uses AI to suggest an appropriate response method based on the information analyzed by the analysis unit. The suggestion may be, for example, a response method at home, but is not limited to such an example. For example, in the case of a fever, the suggestion unit suggests a response method at home, such as "have the child drink a lot of fluids" or "cool the body." Specifically, the suggestion unit provides the parent with a specific response method according to the symptoms based on the information provided by the analysis unit. For example, in the case of a fever, the suggestion unit provides specific advice such as "cool the forehead with a cold towel," "maintain moderate hydration," and "wear light clothing." In addition, in the case of a severe cough, the suggestion unit suggests a response method such as "use a humidifier to maintain humidity" or "have the child drink hot drinks." The suggestion unit can use text messages, audio guides, and even videos and illustrations to convey this information to the parent in an easy-to-understand manner. For example, a video of the response method can be played within the application so that the parent can visually understand the specific steps. In addition, the suggestion unit can monitor the effectiveness of the suggestions and provide additional advice or corrections as necessary. For example, if the fever continues or new symptoms appear, the suggestion unit will send that information back to the analysis unit and make new suggestions based on the latest analysis results. This allows the suggestion unit to support parents in taking appropriate measures at home and effectively manage their children's health.

[0075] The guidance unit guides the child to a hospital or emergency services based on the response method proposed by the suggestion unit. The guidance is performed, for example, when the fever continues or when other symptoms appear, but is not limited to such examples. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services." Specifically, the guidance unit provides specific action instructions to the parent based on the information provided by the suggestion unit. For example, if the fever continues, specific instructions such as "contact the nearest pediatrician and have the child examined." In addition, if the symptoms suddenly worsen, emergency instructions such as "call emergency services immediately and receive emergency treatment" are provided. The guidance unit can use the notification function of the smartphone, voice alerts, and even SMS and email to quickly and reliably convey these instructions to the parent. For example, a pop-up notification can be displayed within the application to enable the parent to take action immediately. The guidance unit also has a function of linking with hospitals and emergency services, and can make reservations or contact on behalf of the parent as necessary. For example, the application can be used to make reservations at the nearest hospital or to contact emergency services. This allows the guidance section to support parents in taking prompt and appropriate action, providing optimal responses to protect the health of their children.

[0076] The medical support system includes an information detailing unit that details the type of information input by the parent. The information detailing unit details the type of information input by the parent. The information input by the parent includes, for example, details of symptoms, occurrence time, frequency, etc., but is not limited to such examples. For example, the information detailing unit allows the parent to input details of symptoms when inputting changes in the child's physical condition. The information detailing unit can also allow the parent to input the occurrence time and frequency. For example, the information detailing unit allows the parent to input the occurrence time and frequency when inputting changes in the child's physical condition. By this, by detailing the type of information input by the parent, a more accurate response method can be proposed. A part or all of the above-mentioned processing in the information detailing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information detailing unit can input the type of information input by the parent to AI and allow AI to execute the information detailing.

[0077] The medical support system includes a database reference unit that causes the AI ​​to refer to a database. The database reference unit causes the AI ​​to refer to a database. The database to be referred to includes, for example, a medical database, a case database, and the like, but is not limited to such examples. For example, the database reference unit causes the AI ​​to refer to the medical database and propose an appropriate response method. The database reference unit can also refer to a case database. For example, the database reference unit causes the AI ​​to refer to the case database and propose an appropriate response method. As a result, the AI ​​can suggest an appropriate response method by referring to the database. A part or all of the above-mentioned processing in the database reference unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the database reference unit can cause the AI ​​to execute the database reference method when the AI ​​refers to the database.

[0078] The medical support system includes a supervision unit that is supervised by a medical expert. The supervision unit is supervised by a medical expert. The supervising medical expert includes, for example, a doctor, a nurse, a medical consultant, and the like, but is not limited to such examples. For example, the supervision unit is supervised by a doctor. The supervision unit can also be supervised by a nurse. For example, the supervision unit is supervised by a nurse. Furthermore, the supervision unit can also be supervised by a medical consultant. For example, the supervision unit is supervised by a medical consultant. As a result, by receiving supervision from a medical expert, a highly reliable response method can be proposed. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the supervision content of the medical expert to AI and cause AI to execute detailed processing of the supervision content.

[0079] The analysis unit can refer to a database related to changes in the child's physical condition and propose an appropriate response method. The analysis unit, for example, refers to a database related to changes in the child's physical condition. The database to be referred to includes, for example, a medical database, a case database, and the like, but is not limited to such examples. For example, the analysis unit refers to a medical database and proposes an appropriate response method. The analysis unit can also refer to a case database. For example, the analysis unit refers to a case database and proposes an appropriate response method. In this way, by referring to the database related to changes in the child's physical condition, an appropriate response method can be proposed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute the method of referring to the database.

[0080] The suggestion unit can suggest home remedies such as "have the child drink fluids" and "cool the body" in the case of fever. For example, in the case of fever, the suggestion unit suggests home remedies such as "have the child drink fluids" and "cool the body". Home remedies include, for example, a method of rehydrating and a method of cooling the body, but are not limited to such examples. For example, the suggestion unit suggests having the child drink cold drinks as a method of rehydrating. The suggestion unit can also suggest using a cold towel as a method of cooling the body. For example, the suggestion unit suggests using a cold towel to cool the body. This allows the parent to respond appropriately by suggesting home remedies in the case of fever. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input home remedies to AI and cause the AI ​​to execute the proposed remedies.

[0081] The guidance unit can issue instructions to "take the child to the hospital" or "call emergency services" if the fever continues or other symptoms appear. For example, the guidance unit issues instructions such as "take the child to the hospital" or "call emergency services" if the fever continues or other symptoms appear. The instructions include, for example, the timing of taking the child to the hospital and the criteria for calling emergency services, but are not limited to such examples. For example, the guidance unit instructs the child to be taken to the hospital if the fever continues. The guidance unit can also instruct the child to call emergency services if other symptoms appear. For example, the guidance unit instructs the child to call emergency services if breathing difficulties appear. This allows the parent to respond quickly by issuing appropriate instructions when the fever continues or other symptoms appear. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the instruction content to AI and have the AI ​​execute the output of the instruction.

[0082] The reception unit can estimate the parent's emotion and adjust the design of the input interface based on the estimated parent's emotion. The reception unit, for example, estimates the parent's emotion and adjusts the design of the input interface based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the reception unit estimates the parent's emotion using facial expression recognition technology. The reception unit can also estimate the parent's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the reception unit adjusts the design of the input interface based on the estimated parent's emotion. For example, when the parent is nervous, an interface with calm colors is provided to reduce visual stress. Also, when the parent is relaxed, an interface with bright colors is provided to make input work fun. Furthermore, when the parent is tired, a simple and highly visible interface is provided to facilitate input work. As a result, the design of the input interface is adjusted according to the parent's emotion, making it easier for the parent to input. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit may input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0083] The reception unit can analyze the parent's past input history and suggest the optimal input method. The reception unit can analyze, for example, the parent's past input history and suggest the optimal input method. The input method includes, for example, text input, voice input, and the like, but is not limited to such examples. For example, the reception unit automatically displays physical condition changes that the parent frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the parent used in the past. For example, if the parent used voice input in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest physical condition changes that will be input in a specific time period from the parent's past input history. For example, the reception unit predicts physical condition changes that will be input at night and suggests an input method suitable for nighttime. In this way, the parent's past input history can be analyzed to suggest the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's past input history to AI and have the AI ​​execute the suggestion of the optimal input method.

[0084] The reception unit can automatically complete the input contents based on the parent's current situation and environment when inputting the child's physical condition change. For example, the reception unit automatically completes the input contents based on the parent's current situation and environment when inputting the child's physical condition change. The automatic completion includes, for example, predictive input, template input, and other methods, but is not limited to such examples. For example, when the parent is out, the reception unit proposes a simple input method and automatically completes the input contents. In addition, when the parent is at home, the reception unit can provide detailed input options and automatically complete the input contents. For example, when the parent is at home, the reception unit provides detailed input options and automatically completes the input contents. Furthermore, when the parent enters at night, the reception unit can automatically complete the input contents by referring to the past nighttime input history. For example, the reception unit predicts physical condition changes to be entered at night and automatically completes the input contents. As a result, the input work is made more efficient by automatically completing the input contents based on the parent's current situation and environment. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the parent's current situation and environmental data into the AI ​​and have the AI ​​automatically complete the input content.

[0085] The reception unit can estimate the parent's emotion and determine the priority of input based on the estimated parent's emotion. The reception unit, for example, estimates the parent's emotion and determines the priority of input based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the reception unit estimates the parent's emotion using facial expression recognition technology. The reception unit can also estimate the parent's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the reception unit determines the priority of input based on the estimated parent's emotion. For example, if the parent is nervous, important input items can be displayed with priority. Also, if the parent is relaxed, detailed input items can be displayed sequentially. Furthermore, if the parent is in a hurry, the most important input item can be displayed first. As a result, important information can be input with priority by determining the priority of input based on 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 may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit may input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0086] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting a change in the child's physical condition. For example, the reception unit can preferentially input highly relevant information in consideration of the geographical location information of the parent when inputting a change in the child's physical condition. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the reception unit can preferentially input information related to the hospital. In addition, when the parent is at home, the reception unit can also preferentially input information related to measures at home. For example, when the parent is at home, the reception unit can also preferentially input information related to measures at home. Furthermore, when the parent is traveling, the reception unit can also preferentially input information related to a medical institution at the travel destination. For example, when the parent is traveling, the reception unit can preferentially input information related to a medical institution at the travel destination. In this way, highly relevant information can be preferentially input by considering the geographical location information of the parent. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographic location information of the parents to the AI ​​and cause the AI ​​to prioritize input of highly relevant information.

[0087] The reception unit can analyze the social media activity of the parent when inputting the change in the child's physical condition, and input related information. The reception unit can analyze the social media activity of the parent when inputting the change in the child's physical condition, and input related information. The social media activity includes, for example, the content of posts and the frequency of activities, but is not limited to such examples. For example, the reception unit automatically completes the input content based on the information shared by the parent on social media. The reception unit can also suggest the input content with reference to the information of medical experts that the parent follows on social media. For example, the reception unit can suggest the input content with reference to the information of medical experts that the parent follows on social media. Furthermore, the reception unit can automatically complete the input content based on the information of groups that the parent participates in on social media. For example, the reception unit automatically completes the input content based on the information of groups that the parent participates in on social media. In this way, the parent's social media activity can be analyzed to efficiently input related information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data to AI and cause the AI ​​to execute the input of related information.

[0088] The analysis unit can estimate the parent's emotion and adjust the expression method of the analysis result based on the estimated parent's emotion. The analysis unit, for example, estimates the parent's emotion and adjusts the expression method of the analysis result based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the analysis unit estimates the parent's emotion using facial expression recognition technology. The analysis unit can also estimate the parent's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the analysis unit adjusts the expression method of the analysis result based on the estimated parent's emotion. For example, if the parent is nervous, a simple and highly visible analysis result can be provided. Also, if the parent is relaxed, a detailed analysis result can be provided. Furthermore, if the parent is in a hurry, an analysis result that covers the main points can be provided. As a result, by adjusting the expression method of the analysis result based on the parent's emotion, an analysis result that is easy for the parent to understand can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's physical condition change during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the child's physical condition change during the analysis. The evaluation of the importance includes, for example, the severity of symptoms, the frequency of occurrence, and the like, but is not limited to such examples. For example, the analysis unit provides a concise analysis result when the child's physical condition change is mild. In addition, the analysis unit can also provide a detailed analysis result when the child's physical condition change is moderate. Furthermore, the analysis unit can also provide a very detailed analysis result when the child's physical condition change is severe. Thereby, by adjusting the level of detail of the analysis based on the importance of the child's physical condition change, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the child's physical condition change to AI and cause AI to adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms according to the category of the child's physical condition change during analysis. The analysis unit, for example, applies different analysis algorithms according to the category of the child's physical condition change during analysis. The category classification includes, for example, the type of symptoms, the cause of occurrence, and the like, but is not limited to such examples. For example, in the case of fever, the analysis unit applies an analysis algorithm based on body temperature data. In addition, in the case of coughing, the analysis unit can also apply an analysis algorithm based on breathing data. Furthermore, in the case of a rash, the analysis unit can also apply an analysis algorithm based on skin data. In this way, by applying different analysis algorithms according to the category of the child's physical condition change, appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the child's physical condition change to AI and cause AI to apply the analysis algorithm.

[0091] The analysis unit can estimate the parent's emotion and adjust the display order of the analysis results based on the estimated parent's emotion. The analysis unit, for example, estimates the parent's emotion and adjusts the display order of the analysis results based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the analysis unit estimates the parent's emotion using facial expression recognition technology. The analysis unit can also estimate the parent's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the analysis unit adjusts the display order of the analysis results based on the estimated parent's emotion. For example, if the parent is nervous, the most important analysis result is displayed first. Also, if the parent is relaxed, detailed analysis results can be displayed sequentially. Furthermore, if the parent is in a hurry, analysis results with the main points can be displayed first. In this way, by adjusting the display order of the analysis results based on the parent's emotion, the parent can prioritize checking important information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0092] The analysis unit can determine the priority of the analysis based on the time of occurrence of the child's physical condition change during the analysis. The analysis unit, for example, determines the priority of the analysis based on the time of occurrence of the child's physical condition change during the analysis. The evaluation of the time of occurrence includes, for example, the date and time of occurrence, the frequency of occurrence, etc., but is not limited to such examples. For example, the analysis unit prioritizes the analysis of recent physical condition changes. The analysis unit can also determine the priority of the analysis by referring to past physical condition changes. Furthermore, the analysis unit can also prioritize the analysis of physical condition changes that occurred in a specific time period. As a result, important information can be analyzed preferentially by determining the priority of the analysis based on the time of occurrence of the child's physical condition change. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the child's physical condition change to AI and cause AI to execute the determination of the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the child's physical condition changes during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the child's physical condition changes during analysis. The evaluation of the relevance includes, for example, a co-occurrence relationship of symptoms, a causal relationship, and the like, but is not limited to such examples. For example, the analysis unit prioritizes analysis of highly related physical condition changes. The analysis unit can also postpone the analysis of less related physical condition changes. Furthermore, when multiple physical condition changes are related, the analysis unit can also analyze them collectively. As a result, by adjusting the order of analysis based on the relevance of the child's physical condition changes, related information can be analyzed efficiently. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the child's physical condition changes to AI and cause AI to adjust the order of analysis.

[0094] The suggestion unit can estimate the parent's emotion and adjust the way of expressing the suggestion based on the estimated parent's emotion. The suggestion unit, for example, estimates the parent's emotion and adjusts the way of expressing the suggestion based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the suggestion unit estimates the parent's emotion using facial expression recognition technology. The suggestion unit can also estimate the parent's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the suggestion unit adjusts the way of expressing the suggestion based on the estimated parent's emotion. For example, if the parent is nervous, a simple and highly visible suggestion can be provided. Also, if the parent is relaxed, a detailed suggestion can be provided. Furthermore, if the parent is in a hurry, a suggestion that covers the main points can be provided. As a result, by adjusting the way of expressing the suggestion based on the parent's emotion, a suggestion that is easy for the parent to understand can be provided. 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 may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit may input emotion data of a parent to the generation AI and cause the generation AI to estimate the emotion.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the response method at the time of suggestion. The evaluation of the importance includes, for example, the severity of the symptoms and the urgency of the response, but is not limited to such examples. For example, the suggestion unit provides a concise proposal in the case of a mild change in physical condition. The suggestion unit can also provide a detailed proposal in the case of a moderate change in physical condition. Furthermore, the suggestion unit can also provide a very detailed proposal in the case of a severe change in physical condition. Thereby, by adjusting the level of detail of the proposal based on the importance of the response method, an appropriate proposal can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the response method to AI and cause AI to adjust the level of detail of the proposal.

[0096] The suggestion unit can apply different proposal algorithms according to the category of the response method at the time of suggestion. For example, the suggestion unit applies different proposal algorithms according to the category of the response method at the time of suggestion. The classification of categories includes, for example, the type of symptoms and the type of response, but is not limited to such examples. For example, the suggestion unit provides a proposal regarding hydration and body temperature management in the case of fever. In addition, the suggestion unit can also provide a proposal regarding breathing management and humidity control in the case of cough. Furthermore, the suggestion unit can also provide a proposal regarding skin care and allergy measures in the case of a rash. In this way, by applying different proposal algorithms according to the category of the response method, appropriate proposals can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input category data of the response method to AI and cause AI to apply the proposal algorithm.

[0097] The suggestion unit can estimate the parent's emotion and adjust the length of the suggestion based on the estimated parent's emotion. The suggestion unit, for example, estimates the parent's emotion and adjusts the length of the suggestion based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the examples are not limited to these. For example, the suggestion unit estimates the parent's emotion using facial expression recognition technology. The suggestion unit can also estimate the parent's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the suggestion unit adjusts the length of the suggestion based on the estimated parent's emotion. For example, if the parent is nervous, a short and to the point suggestion can be provided. Also, if the parent is relaxed, a longer suggestion including detailed explanations can be provided. Furthermore, if the parent is in a hurry, a short suggestion that can be quickly implemented can be provided. In this way, by adjusting the length of the suggestion based on the parent's emotion, a suggestion that is easy for the parent to understand can be provided. 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 may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit may input emotion data of a parent to the generation AI and cause the generation AI to estimate the emotion.

[0098] The suggestion unit can determine the priority of the proposal based on the occurrence time of the response method at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the occurrence time of the response method at the time of proposal. The evaluation of the occurrence time includes, for example, the occurrence date and time, the occurrence frequency, etc., but is not limited to such examples. For example, the suggestion unit provides a proposal for a recent physical condition change with priority. The suggestion unit can also determine the priority of the proposal with reference to past physical condition changes. Furthermore, the suggestion unit can also provide a proposal for a physical condition change that occurred in a specific time period with priority. As a result, by determining the priority of the proposal based on the occurrence time of the response method, important proposals can be provided with priority. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the occurrence time data of the response method to AI and cause AI to execute the determination of the priority of the proposal.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods at the time of suggestion. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the response methods at the time of suggestion. The evaluation of the relevance includes, for example, a co-occurrence relationship of symptoms, a causal relationship, and the like, but is not limited to such examples. For example, the suggestion unit preferentially suggests highly relevant response methods. The suggestion unit can also postpone less relevant response methods. Furthermore, when multiple response methods are related, the suggestion unit can also propose them collectively. As a result, by adjusting the order of suggestions based on the relevance of the response methods, related information can be efficiently suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data of the response methods to AI and cause AI to adjust the order of suggestions.

[0100] The induction unit can estimate the parent's emotion and adjust the induction method based on the estimated parent's emotion. The induction unit, for example, estimates the parent's emotion and adjusts the induction method based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the induction unit estimates the parent's emotion using facial expression recognition technology. The induction unit can also estimate the parent's emotion using voice analysis technology. For example, the induction unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the induction unit adjusts the induction method based on the estimated parent's emotion. For example, if the parent is nervous, the induction is performed in a calm voice. Also, if the parent is relaxed, the induction can be performed in a bright voice. Furthermore, if the parent is in a hurry, the induction can be performed quickly and concisely. As a result, by adjusting the induction method based on the parent's emotion, it is possible to provide an induction that is easy for the parent to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guiding unit may be performed, for example, using AI, or may be performed without using AI. For example, the guiding unit may input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0101] The guiding unit can select the optimal guiding method by analyzing the parent's past behavior history at the time of guiding. For example, the guiding unit selects the optimal guiding method by analyzing the parent's past behavior history at the time of guiding. The analysis of the behavior history includes, for example, past response methods, behavior patterns, etc., but is not limited to such examples. For example, the guiding unit selects the optimal guiding method based on medical institutions used by the parent in the past. In addition, the guiding unit can also select the most efficient guiding method from the parent's past behavior history. Furthermore, the guiding unit can also suggest an appropriate guiding method by referring to the parent's past behavior history. In this way, the optimal guiding method can be selected by analyzing the parent's past behavior history. A part or all of the above-mentioned processing in the guiding unit may be performed using, for example, AI, or may be performed without using AI. For example, the guiding unit can input the parent's past behavior history data into AI and cause the AI ​​to select the optimal guiding method.

[0102] The guidance unit can customize the guidance means based on the parent's current situation when guiding. The guidance unit, for example, customizes the guidance means based on the parent's current situation when guiding. The evaluation of the current situation includes, for example, the current physical condition, the environmental situation, and the like, but is not limited to such examples. For example, when the parent is driving a car, the guidance unit prioritizes voice guidance to guide the parent. In addition, when the parent is using public transportation, the guidance unit can also guide the parent by text message. Furthermore, when the parent is at home, the guidance unit can also provide guidance including detailed procedures. In this way, by customizing the guidance means based on the parent's current situation, guidance that is easy for the parent to understand can be provided. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the parent's current situation data into AI and have the AI ​​execute customization of the guidance means.

[0103] The induction unit can estimate the parent's emotion and determine the priority of the induction based on the estimated parent's emotion. The induction unit can estimate the parent's emotion, for example, and determine the priority of the induction based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the invention is not limited to such examples. For example, the induction unit can estimate the parent's emotion using facial expression recognition technology. The induction unit can also estimate the parent's emotion using voice analysis technology. For example, the induction unit can analyze the tone and speed of the parent's voice to estimate the emotion. Furthermore, the induction unit determines the priority of the induction based on the estimated parent's emotion. For example, if the parent is nervous, the most important induction is given priority. Also, if the parent is relaxed, detailed induction can be given sequentially. Furthermore, if the parent is in a hurry, induction that covers the main points can be given first. In this way, by determining the priority of the induction based on the parent's emotion, important induction can be given priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guiding unit may be performed, for example, using AI, or may be performed without using AI. For example, the guiding unit may input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0104] The guidance unit can select the optimal guidance method in consideration of the geographical location information of the parent at the time of guidance. For example, the guidance unit selects the optimal guidance method in consideration of the geographical location information of the parent at the time of guidance. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the guidance unit guides the shortest route to the hospital. In addition, when the parent is at home, the guidance unit can also guide the parent to a home remedy with priority. Furthermore, when the parent is traveling, the guidance unit can also guide the parent to a medical institution at the travel destination. In this way, the optimal guidance method can be selected by considering the geographical location information of the parent. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the geographical location information data of the parent to AI and cause the AI ​​to select the optimal guidance method.

[0105] The guidance unit can analyze the social media activity of the parent at the time of guidance and suggest a means of guidance. For example, the guidance unit can analyze the social media activity of the parent at the time of guidance and suggest a means of guidance. Social media activity includes, for example, the content of posts and the frequency of activity, but is not limited to such examples. For example, the guidance unit can suggest an optimal guidance method based on information shared by the parent on social media. The guidance unit can also perform guidance by referring to information on medical professionals followed by the parent on social media. Furthermore, the guidance unit can also suggest an appropriate guidance method based on information on groups in which the parent participates on social media. In this way, the optimal guidance means can be suggested by analyzing the social media activity of the parent. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the social media activity data of the parent into AI and cause the AI ​​to execute the suggestion of a means of guidance.

[0106] The information detailing unit can estimate the parent's emotion and adjust the level of detail of the input information based on the estimated parent's emotion. The information detailing unit, for example, estimates the parent's emotion and adjusts the level of detail of the input information based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the information detailing unit estimates the parent's emotion using facial expression recognition technology. The information detailing unit can also estimate the parent's emotion using voice analysis technology. For example, the information detailing unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the information detailing unit adjusts the level of detail of the input information based on the estimated parent's emotion. For example, if the parent is nervous, the information detailing unit encourages the parent to input concise information. Also, if the parent is relaxed, the information detailing unit can encourage the parent to input detailed information. Furthermore, if the parent is in a hurry, the information detailing unit can prioritize the most important information input. This allows the information that is easy for the parent to input to be provided by adjusting the level of detail of the input information based on 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 may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information detailing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the information detailing unit may input parental emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0107] The information detail unit can provide optimal information details by referring to the parent's past input history. The information detail unit can provide optimal information details by referring to the parent's past input history, for example. The input history reference includes, for example, past input contents, input frequency, etc., but is not limited to such examples. For example, the information detail unit provides optimal information details based on information previously input by the parent. The information detail unit can also provide highly relevant information preferentially from the parent's past input history. Furthermore, the information detail unit can analyze the parent's past input history and provide the most efficient information details. As a result, optimal information details can be provided by referring to the parent's past input history. A part or all of the above-mentioned processing in the information detail unit can be performed, for example, using AI, or can be performed without using AI. For example, the information detail unit can input the parent's past input history data into AI and cause AI to provide optimal information details.

[0108] The information detailing unit can estimate the parent's emotion and determine the priority of the input information based on the estimated parent's emotion. The information detailing unit can estimate the parent's emotion and determine the priority of the input information based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the invention is not limited to such examples. For example, the information detailing unit can estimate the parent's emotion using facial expression recognition technology. The information detailing unit can also estimate the parent's emotion using voice analysis technology. For example, the information detailing unit can analyze the tone and speed of the parent's voice to estimate the emotion. Furthermore, the information detailing unit determines the priority of the input information based on the estimated parent's emotion. For example, if the parent is nervous, the most important information can be input preferentially. Also, if the parent is relaxed, detailed information can be input sequentially. Furthermore, if the parent is in a hurry, information with the main points can be input first. In this way, by determining the priority of the input information based on the parent's emotion, important information can be input preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information detailing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the information detailing unit may input parental emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0109] The information detail unit can provide optimal information details by considering the geographical location information of the parent. The information detail unit provides optimal information details by considering, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, the information detail unit provides detailed information about the hospital when the parent is near the hospital. In addition, the information detail unit can also provide detailed information about how to deal with the problem at home when the parent is at home. Furthermore, the information detail unit can also provide detailed information about medical institutions at the travel destination when the parent is traveling. This allows optimal information details to be provided by considering the geographical location information of the parent. A part or all of the above-mentioned processing in the information detail unit may be performed, for example, using AI, or may be performed without using AI. For example, the information detail unit can input the geographical location information data of the parent to AI and cause the AI ​​to provide optimal information details.

[0110] The database reference unit can estimate the parent's emotion and adjust the database reference method based on the estimated parent's emotion. The database reference unit can estimate the parent's emotion, for example, and adjust the database reference method based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the database reference unit can estimate the parent's emotion using facial expression recognition technology. The database reference unit can also estimate the parent's emotion using voice analysis technology. For example, the database reference unit can analyze the tone and speed of the parent's voice to estimate the emotion. Furthermore, the database reference unit adjusts the database reference method based on the estimated parent's emotion. For example, if the parent is nervous, a concise and highly visible database reference method can be provided. Also, if the parent is relaxed, a detailed database reference method can be provided. Furthermore, if the parent is in a hurry, a database reference method that focuses on the main points can be provided. This allows the parent to be provided with data that is easy to understand by adjusting the database reference method based on the parent's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the database reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the database reference unit may input parent emotion data to the generation AI and cause the generation AI to estimate emotions.

[0111] The database reference unit can provide an optimal reference method by analyzing the past reference history. The database reference unit can provide an optimal reference method by analyzing the past reference history, for example. The analysis of the reference history includes, for example, reference contents and reference frequency, but is not limited to such examples. For example, the database reference unit provides an optimal reference method based on data previously referenced by the parent. The database reference unit can also provide highly relevant data preferentially from the parent's past reference history. Furthermore, the database reference unit can also analyze the parent's past reference history and provide the most efficient reference method. In this way, the optimal reference method can be provided by analyzing the past reference history. A part or all of the above-mentioned processing in the database reference unit can be performed, for example, using AI, or can be performed without using AI. For example, the database reference unit can input the parent's past reference history data into AI and cause AI to provide the optimal reference method.

[0112] The database reference unit can estimate the parent's emotion and determine the database reference priority based on the estimated parent's emotion. The database reference unit can estimate the parent's emotion, for example, and determine the database reference priority based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the invention is not limited to such examples. For example, the database reference unit can estimate the parent's emotion using facial expression recognition technology. The database reference unit can also estimate the parent's emotion using voice analysis technology. For example, the database reference unit can analyze the tone and speed of the parent's voice to estimate the emotion. Furthermore, the database reference unit determines the database reference priority based on the estimated parent's emotion. For example, if the parent is nervous, the most important data is referenced preferentially. Also, if the parent is relaxed, detailed data can be referenced sequentially. Furthermore, if the parent is in a hurry, data with the main points can be referenced first. In this way, by determining the database reference priority based on the parent's emotion, important data can be referenced preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the database reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the database reference unit may input parent emotion data to the generation AI and cause the generation AI to estimate emotions.

[0113] The database reference unit can provide an optimal database reference method in consideration of the geographical location information of the parent. The database reference unit provides an optimal database reference method in consideration of, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the database reference unit preferentially refers to data related to the hospital. In addition, when the parent is at home, the database reference unit can also preferentially refer to data related to home remedies. Furthermore, when the parent is traveling, the database reference unit can also preferentially refer to data related to medical institutions at the travel destination. In this way, by considering the geographical location information of the parent, an optimal database reference method can be provided. Part or all of the above-mentioned processing in the database reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the database reference unit can input the geographical location information data of the parent to AI and cause the AI ​​to execute the provision of the optimal database reference method.

[0114] The supervision unit can estimate the parent's emotion and adjust the level of detail of the supervision content based on the estimated parent's emotion. The supervision unit, for example, estimates the parent's emotion and adjusts the level of detail of the supervision content based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the supervision unit estimates the parent's emotion using facial expression recognition technology. The supervision unit can also estimate the parent's emotion using voice analysis technology. For example, the supervision unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the supervision unit adjusts the level of detail of the supervision content based on the estimated parent's emotion. For example, when the parent is nervous, the supervision unit provides concise and highly visible supervision content. Furthermore, when the parent is relaxed, the supervision unit can provide detailed supervision content. Furthermore, when the parent is in a hurry, the supervision unit can provide supervision content that covers the main points. As a result, the level of detail of the supervision content can be adjusted based on the parent's emotion, thereby providing supervision content that is easy for the parent to understand. The emotion estimation is realized by 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the supervision unit can input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0115] The supervision unit can provide optimal supervision content by referring to the past supervision history. The supervision unit, for example, provides optimal supervision content by referring to the past supervision history. The reference to the supervision history includes, for example, supervision content, supervision frequency, etc., but is not limited to such examples. For example, the supervision unit provides optimal supervision content based on the supervision content referred to by the parent in the past. The supervision unit can also provide highly relevant supervision content preferentially from the parent's past supervision history. Furthermore, the supervision unit can analyze the parent's past supervision history and provide the most efficient supervision content. As a result, optimal supervision content can be provided by referring to the past supervision history. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the parent's past supervision history data into AI and cause AI to provide optimal supervision content.

[0116] The supervision unit can estimate the parent's emotion and determine the priority order of the supervision contents based on the estimated parent's emotion. The supervision unit, for example, estimates the parent's emotion and determines the priority order of the supervision contents based on the estimated parent's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, the supervision unit estimates the parent's emotion using facial expression recognition technology. The supervision unit can also estimate the parent's emotion using voice analysis technology. For example, the supervision unit analyzes the tone and speed of the parent's voice to estimate the emotion. Furthermore, the supervision unit determines the priority order of the supervision contents based on the estimated parent's emotion. For example, if the parent is nervous, the most important supervision content can be provided preferentially. Also, if the parent is relaxed, detailed supervision content can be provided sequentially. Furthermore, if the parent is in a hurry, supervision content with the main points can be provided first. In this way, by determining the priority order of the supervision contents based on the parent's emotion, important supervision content can be provided preferentially. The emotion estimation is realized by 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the supervision unit can input the parent's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0117] The supervision unit can provide optimal supervision content by considering the geographical location information of the parent. The supervision unit provides optimal supervision content by considering, for example, the geographical location information of the parent. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the parent is near a hospital, the supervision unit can preferentially provide supervision content related to the hospital. In addition, when the parent is at home, the supervision unit can also preferentially provide supervision content related to how to deal with the problem at home. Furthermore, when the parent is traveling, the supervision unit can also preferentially provide supervision content related to medical institutions at the travel destination. In this way, optimal supervision content can be provided by considering the geographical location information of the parent. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the geographical location information data of the parent to AI and cause the AI ​​to provide optimal supervision content.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The medical support system can further include a lifestyle analysis unit that takes into account the lifestyle of the parent. The lifestyle analysis unit collects data on the parent's diet, exercise, sleep patterns, etc., and proposes an appropriate response method to changes in the child's physical condition based on this information. For example, if the parent works the night shift, a response method for the night can be proposed. Also, if the parent has specific dietary restrictions, a response method based on those restrictions can be proposed. Furthermore, if the parent exercises frequently, a response method for changes in physical condition after exercise can be proposed. In this way, by taking the parent's lifestyle into account, a more individualized response method can be provided.

[0120] The medical support system may further include a stress measurement unit that measures the parent's stress level. The stress measurement unit collects physiological data such as the parent's heart rate, blood pressure, and respiratory rate, and evaluates the parent's stress level based on this data. For example, if the parent's heart rate is high, it may determine that the parent is stressed and provide advice to relax. If the parent's blood pressure is high, measures to reduce stress may be suggested. Furthermore, if the parent's respiratory rate is increasing, relaxation methods such as deep breathing and meditation may be suggested. In this way, a more appropriate response method may be provided by taking the parent's stress level into consideration.

[0121] The medical support system may further include a medical history reference unit that references the parent's past medical history. The medical history reference unit collects data such as the parent's past medical history, allergy information, and medications being taken, and suggests an appropriate response method for changes in the child's physical condition based on this information. For example, if the parent has a specific allergy, it can suggest a response method based on the allergy. Also, if the parent is taking a specific medication, it can suggest a response method that takes into account the effects of the medication. Furthermore, it can also suggest a response method when a similar symptom appears, referring to the parent's past medical history. In this way, a more appropriate response method can be provided by taking into account the parent's past medical history.

[0122] The medical support system can further estimate the parent's emotions and adjust communication with the medical professional based on the estimated parent's emotions. For example, if the parent is feeling anxious, the medical professional can speak words that reassure the parent. Also, if the parent is feeling angry, the medical professional can calm the parent's emotions by responding calmly. Furthermore, if the parent is feeling sad, the medical professional can speak words of encouragement to the parent. In this way, communication according to the parent's emotions allows the parent to receive medical support with peace of mind.

[0123] The medical support system can further include a social background analysis unit that takes into account the social background of the parents. The social background analysis unit collects data on the parents' occupation, family environment, economic situation, etc., and suggests appropriate ways of responding to changes in the child's physical condition based on this information. For example, if the parent is a single mother, it can provide information on support services. Also, if the parent has a low income, it can suggest medical services to reduce the economic burden. Furthermore, if the parent has a busy job, it can suggest ways of responding to use time efficiently. In this way, by taking the social background of the parents into account, it is possible to provide more appropriate ways of responding.

[0124] The medical support system can further estimate the parent's emotions and adjust the system's response speed based on the estimated parent's emotions. For example, if the parent is in a hurry due to an emergency, the system can respond quickly and provide the necessary information immediately. If the parent is relaxed, the system can provide detailed information slowly. Furthermore, if the parent is tired, the system can provide concise and easy-to-understand information. In this way, by adjusting the response speed according to the parent's emotions, the parent can use the system without feeling stressed.

[0125] The medical support system can further include a cultural background analysis unit that takes into account the cultural background of the parents. The cultural background analysis unit collects data such as the parents' religion, customs, and language, and suggests an appropriate way of responding to changes in the child's physical condition based on this information. For example, if the parents believe in a particular religion, it can suggest a way of responding based on that religion. Also, if the parents have particular cultural customs, it can suggest a way of responding based on that custom. Furthermore, if the parents speak a different language, it can suggest a way of responding in that language. In this way, a more appropriate way of responding can be provided by taking the parents' cultural background into account.

[0126] The medical support system can further estimate the parent's emotions and customize the system interface based on the estimated parent's emotions. For example, if the parent feels anxious, the system can provide an interface with calm colors to reduce the parent's anxiety. Also, if the parent feels relaxed, the system can provide an interface with bright colors to further improve the parent's mood. Furthermore, if the parent is tired, the system can provide a simple and highly visible interface. In this way, by providing an interface according to the parent's emotions, the parent can comfortably use the system.

[0127] The medical support system can further include a communication analysis unit that takes into account the parent's communication style. The communication analysis unit collects data such as the parent's speaking style, writing style, and expression style, and suggests an appropriate response method for a change in the child's physical condition based on this information. For example, if the parent prefers short and concise expressions, the system can suggest a concise response method. Also, if the parent prefers detailed explanations, the system can also suggest a detailed response method. Furthermore, if the parent prefers visual information, the system can also suggest a response method using diagrams and graphs. In this way, a more appropriate response method can be provided by taking the parent's communication style into consideration.

[0128] The medical support system can further estimate the parent's emotions and adjust the notification method of the system based on the estimated parent's emotions. For example, if the parent is nervous, the system can notify the parent with a gentle sound to relieve the parent's tension. Also, if the parent is relaxed, the system can notify the parent with a bright sound to further improve the parent's mood. Furthermore, if the parent is in a hurry, the system can notify the parent with a quick and clear sound. In this way, by providing a notification method according to the parent's emotions, the parent can use the system without feeling stressed.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The reception unit receives a parent's input of changes in the child's physical condition. Examples of changes in physical condition that the parent may input include fever, cough, stomachache, and the like. The reception unit allows the parent to input the changes in the child's physical condition in text format, and can also convert what the parent dictates into text using voice input. The reception unit converts the parent's dictation into text data using voice recognition technology. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is performed, for example, by referring to a database. The analysis unit refers to a database regarding changes in the child's physical condition and proposes an appropriate response method. Step 3: The suggestion unit uses AI to suggest appropriate measures based on the information analyzed by the analysis unit. For example, the suggestion unit suggests measures to be taken at home. In the case of a fever, the suggestion unit suggests measures to be taken at home, such as "have the patient drink lots of fluids" and "cool the body." Step 4: The guidance unit guides the user to a hospital or emergency services based on the response method proposed by the suggestion unit. Guidance is performed, for example, if the fever continues or other symptoms appear. The guidance unit issues instructions such as "take the user to a hospital" or "call emergency services."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, guidance unit, information detail unit, database reference unit, and supervision unit 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 a parent inputs changes in the child's physical condition. 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 AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate response method based on the analyzed information. The guidance unit is realized, for example, by the control unit 46A of the smart device 14, and guides the parent to a hospital or emergency service based on the proposed response method. The information detail unit is realized, for example, by the control unit 46A of the smart device 14, and details the type of information input by the parent. The database reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI refers to the database. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The 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.

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

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

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

[0142] Fig. 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.

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

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

[0145] In the 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.

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

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0149] The data processing system 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.

[0150] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, guidance unit, information detail unit, database reference unit, and supervision unit 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 a parent inputs a change in the child's physical condition. 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 AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate response method based on the analyzed information. The guidance unit is realized, for example, by the control unit 46A of the smart glasses 214, and guides the parent to a hospital or emergency service based on the proposed response method. The information detail unit is realized, for example, by the control unit 46A of the smart glasses 214, and details the type of information input by the parent. The database reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI refers to the database. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The 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.

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

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (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).

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, guidance unit, information detail unit, database reference unit, and supervision 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 a parent inputs changes in the child's physical condition. 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 AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate response method based on the analyzed information. The guidance unit is realized, for example, by the control unit 46A of the headset type terminal 314, and guides the parent to a hospital or emergency service based on the proposed response method. The information detail unit is realized, for example, by the control unit 46A of the headset type terminal 314, and details the type of information input by the parent. The database reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI refers to the database. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, guidance unit, information detail unit, database reference unit, and supervision unit 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 a parent inputs changes in the child's physical condition. 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 AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an appropriate response method based on the analyzed information. The guidance unit is realized, for example, by the control unit 46A of the robot 414, and guides the parent to a hospital or emergency service based on the proposed response method. The information detail unit is realized, for example, by the control unit 46A of the robot 414, and details the type of information input by the parent. The database reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI refers to the database. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Appendix 1) A reception section where parents can input changes in their child's physical condition; an analysis unit that analyzes the information input by the reception unit; a suggestion unit that proposes a response method based on the information analyzed by the analysis unit; A guidance unit that guides the user to a hospital or emergency service based on the response method proposed by the suggestion unit. A system characterized by: (Appendix 2) Provides an information detail section that details the type of information the parent will enter 2. The system of claim 1, (Appendix 3) Equipped with a database reference section that allows AI to reference the database 2. The system of claim 1, (Appendix 4) Equipping a supervision department that is supervised by medical experts 2. The system of claim 1, (Appendix 5) The analysis unit is Refer to a database of changes in a child's physical condition and suggest appropriate measures to take 2. The system of claim 1, (Appendix 6) The suggestion unit, In the case of fever, suggest measures to be taken at home such as "encouraging fluid intake" and "cooling the body" 2. The system of claim 1, (Appendix 7) The induction portion is If the fever persists or other symptoms appear, instruct them to "take the child to hospital" or "call emergency services." 2. The system of claim 1, (Appendix 8) The reception unit is Estimate parental emotions and adjust the design of the input interface based on the estimated parental emotions 2. The system of claim 1, (Appendix 9) The reception unit is Analyzes parent's past input history and suggests input methods 2. The system of claim 1, (Appendix 10) The reception unit is When entering changes in a child's health condition, the system automatically completes the input based on the parent's current situation and environment. 2. The system of claim 1, (Appendix 11) The reception unit is Estimate parental emotions and prioritize inputs based on the estimated parental emotions 2. The system of claim 1, (Appendix 12) The reception unit is When entering changes in a child's health condition, the system takes into account the parent's geographic location information and prioritizes entering the most relevant information. 2. The system of claim 1, (Appendix 13) The reception unit is When parents enter changes in their child's health condition, the system analyzes their social media activity and prompts them to enter related information. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer the parent's emotions and adjust the way the analysis results are presented based on the inferred parent's emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of changes in the child's physical condition 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of changes in the child's physical condition. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer parental emotions and adjust the display order of analysis results based on the inferred parental emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the analysis priority is determined based on the timing of changes in the child's physical condition. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on the relevance of changes in the child's physical condition 2. The system of claim 1, (Appendix 20) The suggestion unit, Infer parental sentiment and adjust suggestion presentation based on the inferred parental sentiment 2. The system of claim 1, (Appendix 21) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the action 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the response method. 2. The system of claim 1, (Appendix 23) The suggestion unit, Estimate parental sentiment and adjust the length of the suggestion based on the estimated parental sentiment 2. The system of claim 1, (Appendix 24) The suggestion unit, When making proposals, prioritize proposals based on when the response methods occur 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the response 2. The system of claim 1, (Appendix 26) The induction portion is Infer parental emotions and adjust guidance methods based on the inferred parental emotions 2. The system of claim 1, (Appendix 27) The induction portion is When guiding, the parent's past behavioral history is analyzed to select the optimal guidance method. 2. The system of claim 1, (Appendix 28) The induction portion is During navigation, customize the navigation based on the parent's current situation 2. The system of claim 1, (Appendix 29) The induction portion is Estimate the parent's emotions and determine the priority of guidance based on the estimated parent's emotions 2. The system of claim 1, (Appendix 30) The induction portion is When guiding, the optimal guidance method is selected taking into account the geographic location information of the parent. 2. The system of claim 1, (Appendix 31) The induction portion is During guidance, analyze parents' social media activity to suggest guidance methods 2. The system of claim 1, (Appendix 32) The information details section includes: Estimate the parent's emotions and adjust the level of detail of the input information based on the estimated parent's emotions 3. The system of claim 2, (Appendix 33) The information details section includes: Refer to parent's past input history to provide optimal information details 3. The system of claim 2, (Appendix 34) The information details section includes: Estimate the parent's emotions and prioritize input information based on the estimated parent's emotions 3. The system of claim 2, (Appendix 35) The information details section includes: Provide optimal details based on parent's geographic location 3. The system of claim 2, (Appendix 36) The database reference unit includes: Infer the parent's emotions and adjust the database reference method based on the estimated parent's emotions. 4. The system of claim 3. (Appendix 37) The database reference unit includes: Analyze past browsing history to provide optimal browsing methods 4. The system of claim 3. (Appendix 38) The database reference unit includes: The parent's sentiment is estimated, and a database reference priority is determined based on the estimated parent's sentiment. 4. The system of claim 3. (Appendix 39) The database reference unit includes: Provides optimal database lookup methods that take into account parent geographic location information 4. The system of claim 3. (Appendix 40) The supervisory department: Inferring parental emotions and adjusting the level of detail of supervision based on the inferred parental emotions 5. The system of claim 4. (Appendix 41) The supervisory department: Refer to past supervision history to provide optimal supervision content 5. The system of claim 4. (Appendix 42) The supervisory department: Estimate parental feelings and prioritize supervision content based on the estimated parental feelings 5. The system of claim 4. (Appendix 43) The supervisory department: Provide optimal supervision taking into account parents' geographic location 5. The system of claim 4. [Explanation of symbols]

[0203] 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 section where parents can input changes in their child's physical condition; an analysis unit that analyzes the information input by the reception unit; a suggestion unit that proposes a response method based on the information analyzed by the analysis unit; A guidance unit that guides the user to a hospital or emergency service based on the response method proposed by the suggestion unit. A system characterized by:

2. Provides an information detail section that details the type of information the parent will enter 2. The system of claim 1 .

3. Equipping a supervision department that is supervised by medical experts 2. The system of claim 1 .

4. The reception unit is Estimate parental emotions and adjust the design of the input interface based on the estimated parental emotions 2. The system of claim 1 .

5. The reception unit is Analyzes parent's past input history and suggests input methods 2. The system of claim 1 .

6. The reception unit is When entering changes in a child's health condition, the system automatically completes the input based on the parent's current situation and environment.

2. The system of claim 1 .

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