system

Abstract

The system according to this embodiment aims to analyze information on a child's symptoms and injuries and provide information to help parents take appropriate action. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a sharing unit, and a notification unit. The reception unit inputs images of the child's symptoms or injuries. The analysis unit analyzes the information input by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. The sharing unit shares the information obtained by the analysis unit with other parents. The notification unit sends a push notification to check on the progress based on the information shared by the sharing unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for parents to obtain appropriate information when a child is in poor health, and it is difficult to grasp the necessity of going to the hospital and the expected number of days until recovery.

[0005] The system according to the embodiment aims to analyze the symptoms and injury information of a child and provide information for parents to take appropriate actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a sharing unit, and a notification unit. The reception unit receives images of the child's symptoms or injuries. The analysis unit analyzes the information entered by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. The sharing unit shares the information obtained by the analysis unit with other parents. The notification unit sends push notifications to check on the progress based on the information shared by the sharing unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze information about a child's symptoms and injuries and provide information to help parents take appropriate action. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The child illness support system according to an embodiment of the present invention is a system in which images of a child's symptoms or injuries are input, and a generating AI analyzes them to present possible diagnoses, whether a hospital visit is necessary, and the estimated number of days until full recovery, along with gentle advice. This system provides a platform in which parents can share experiences and advice and receive support from other parents. It also has a function to send a push notification three days later to check on the child's progress (including the hospital diagnosis results). This encourages active information exchange among users, and the system's accuracy is improved by accumulating and sharing information uploaded along with images. Furthermore, by linking with HELPO, medical consultations with doctors and other professionals, as well as online consultations, can be selected as needed. As a result, the child illness support system reduces parental anxiety and ensures that responses to a child's illness are carried out quickly and appropriately. It also encourages active information exchange among users and improves the system's accuracy. For example, as images and information uploaded by parents are accumulated, the accuracy of the generating AI's analysis improves, enabling more accurate diagnoses.

[0029] The child illness support system according to this embodiment comprises a reception unit, an analysis unit, a sharing unit, and a notification unit. The reception unit inputs images of the child's symptoms or injuries. The reception unit can, for example, take pictures of the child's symptoms or injuries using a smartphone camera and upload them to the app. The reception unit can also select and upload existing images. Furthermore, the reception unit can also input information in video format. For example, it can record the child's movements and facial expressions in video and upload it to the app. The analysis unit uses a generation AI to analyze the information input by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. The analysis unit, for example, uses a generation AI to analyze the input images and videos and estimate the diagnoses. The analysis unit can also evaluate the severity and urgency of the symptoms and determine whether hospital visits are necessary. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. The sharing unit provides a platform for parents to share the information obtained by the analysis unit. The sharing section allows, for example, parents to post experiences and advice, and other parents to comment and ask questions. The sharing section also allows information to be shared along with images and videos. Furthermore, the sharing section has a function to award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. The notification section sends push notifications to check on progress based on the information shared by the sharing section. For example, the notification section sends a push notification to check on the progress after three days. The notification section can also notify progress information, including hospital diagnosis results. Furthermore, the notification section can send customized notifications based on the frequency and content of notifications set by the user. As a result, the child's illness support system according to the embodiment can support appropriate responses to the child's symptoms and injuries and reduce parental anxiety.

[0030] The reception desk inputs images of the child's symptoms and injuries. For example, the reception desk can take pictures of the child's symptoms or injuries using a smartphone camera and upload them to the app. Specifically, parents can use their smartphone camera to take pictures of their child's rashes or injuries and upload the images directly to the app. The reception desk can also select and upload existing images. For example, it is possible to select and upload images taken in the past or images provided by a doctor. Furthermore, the reception desk can also input information in video format. For example, it is possible to record the child's movements and facial expressions in video and upload it to the app. This allows medical professionals and AI to perform analysis based on more detailed information. The video upload function is useful for recording things like how a child walks, how they cough, and changes in their facial expressions. This provides dynamic information that cannot be captured by still images alone, enabling more accurate diagnosis and response. The reception desk centrally manages this information and provides an interface for sending it to the analysis department. Furthermore, the reception desk has a user-friendly interface and is designed to allow parents to easily upload images and videos. For example, it includes guided upload procedures and features that automatically check the quality of images and videos. This helps the reception desk to help parents quickly and accurately enter information about their child's symptoms or injuries.

[0031] The analysis unit uses a generation AI to analyze information entered by the reception unit and presents possible diagnoses, whether a hospital visit is necessary, and the estimated number of days until complete recovery. For example, the generation AI analyzes images and videos entered by the analysis unit to estimate the diagnose. Specifically, the generation AI uses image recognition technology to analyze the shape and color of rashes, the depth and extent of injuries, etc., and compares them with past databases to estimate the diagnose. The analysis unit can also evaluate the severity and urgency of symptoms and determine whether a hospital visit is necessary. For example, the generation AI analyzes the progression of symptoms and their relationship to other symptoms, and generates a notification recommending immediate hospital visit if the situation is highly urgent. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. For example, the generation AI analyzes the progression pattern of symptoms based on past data of similar cases and predicts the duration until complete recovery. This allows parents to predict how long it will take for their child's symptoms to improve and provides them with reference information to take appropriate action. The analysis unit is equipped with an interface to present these analysis results to parents in an easy-to-understand manner. For example, the analysis results can be visually displayed in graphs and charts, allowing users to understand the diagnosis, whether hospital visits are necessary, and the estimated number of days until full recovery at a glance. The analysis unit can also provide specific advice and recommended actions based on the analysis results. This enables the analysis unit to quickly and accurately provide parents with the information necessary to take appropriate action regarding their child's symptoms.

[0032] The sharing section provides a platform for parents to share information obtained by the analysis section. For example, parents can post experiences and advice, and other parents can comment and ask questions. Specifically, parents can post experiences and coping strategies regarding their children's symptoms or injuries, and other parents can comment or ask questions. The sharing section also allows for the sharing of information along with images and videos. For example, parents can post images or videos of their children's symptoms, and other parents can offer advice. Furthermore, the sharing section includes a point system for information providers. For example, parents who provide useful information will be awarded points. These points can function as incentives, such as discounts on specific services or products. The sharing section also includes features to promote communication among parents and facilitate information sharing and mutual support. For example, it provides a tagging function to make it easier for parents to search for information on specific symptoms or injuries, and a function to display popular posts in a ranking format. The sharing section also includes privacy protection features, allowing parents to post information anonymously. This allows parents to share information with peace of mind and refer to advice and experiences from other parents. The shared area allows parents to share information with each other, supporting them in dealing with their child's illness and reducing their anxiety.

[0033] The notification unit sends push notifications to check on progress based on information shared by the sharing unit. For example, the notification unit sends a push notification to check on the progress after three days. Specifically, after a parent posts information about their child's symptoms or injury to the sharing unit, the notification unit automatically sends a push notification to check on progress after a certain period of time has passed. The notification unit can also notify about progress information, including hospital diagnosis results. For example, if a parent posts the results of a hospital diagnosis to the sharing unit, the notification unit will notify about progress based on that information. Furthermore, the notification unit can send customized notifications based on the frequency and content of notifications set by the user. For example, if a parent sets the notification frequency to once a week, the notification unit will send a progress check notification once a week. The content of the notifications can also be customized according to the parent's needs. For example, notifications can be sent that include information about specific symptoms or the latest advice from medical institutions. Through these notifications, the notification unit helps parents to continuously monitor the progress of their child's symptoms or injury and take appropriate action. Furthermore, the notification unit also has a function to provide feedback to parents after they receive a notification. For example, after receiving a notification, parents can report on the improvement of their child's symptoms or the occurrence of new symptoms. This allows the notification system to provide continuous support to parents so that they can take appropriate action when their child is unwell, and to reduce parental anxiety.

[0034] The reception desk can input images of a child's symptoms or injuries. For example, the reception desk can take pictures of a child's symptoms or injuries using a smartphone camera and upload them to the app. The reception desk can also select and upload existing images. Furthermore, the reception desk can also input information in video format. For example, it can record a child's movements and facial expressions in video and upload it to the app. This allows for accurate input of images of a child's symptoms or injuries. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can use AI to support image and video input and improve input accuracy.

[0035] The analysis unit can use a generation AI to present possible disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery. For example, the analysis unit can use a generation AI to analyze input images or videos and estimate disease names. The analysis unit can also evaluate the severity and urgency of symptoms and determine whether hospital visits are necessary. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. In this way, by using a generation AI, it is possible to accurately present disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit presents disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery based on the results of analysis by the generation AI.

[0036] The sharing section can provide a platform for parents to share experiences and advice with each other. For example, parents can post experiences and advice, and other parents can comment and ask questions. The sharing section can also share information along with images and videos. Furthermore, the sharing section has a feature to award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. This allows parents to share experiences and advice with each other, enabling quicker and more appropriate responses to children's symptoms and injuries. Some or all of the above processes in the sharing section may be performed using AI, or not. For example, the sharing section may use AI to analyze posts and automatically display relevant information.

[0037] The notification unit can send push notifications to check on the progress. For example, it can send a push notification three days later to check on the progress. The notification unit can also send progress information, including hospital diagnosis results. Furthermore, the notification unit can send customized notifications based on the frequency and content of notifications set by the user. This allows users to track the progress of their child's symptoms or injuries by sending push notifications to check on their progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze progress information and send push notifications at the appropriate time.

[0038] The sharing section can store and share information uploaded along with images. For example, the sharing section can store images and information uploaded by a parent and make them accessible to other parents. The sharing section can also use the stored information to improve the analysis accuracy of the generating AI. As a result, the accuracy of the app is improved by storing and sharing information uploaded along with images. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section can use AI to automatically classify uploaded information and display related information.

[0039] The sharing section can award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. The sharing section can also offer benefits and rewards based on the accumulation of points. By awarding points to information providers, this increases the users' motivation to provide information. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section may use AI to evaluate the usefulness of the information provided and automatically award points.

[0040] The analysis unit can provide medical consultations and online medical consultations in conjunction with HELPO. For example, when a user enters information about their symptoms or injuries, the analysis unit can provide the option to consult with a doctor through HELPO. The analysis unit can also select online medical consultations if the symptoms worsen or if additional medical consultation is needed. In this way, the analysis unit can provide medical consultations and online medical consultations as needed in conjunction with HELPO. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can use generative AI to analyze the user's symptoms and present appropriate medical consultation or online medical consultation options.

[0041] The reception desk can refer to the child's past symptom history and evaluate the reliability of the entered information. For example, if the information matches the past symptom history, the reception desk will rate the reliability of the entered information highly. If the information differs from the past symptom history, the reception desk may also display a message prompting further confirmation. Furthermore, the reception desk can score the reliability of the entered information based on the past symptom history and present it to the user. This allows the reliability of the entered information to be evaluated by referring to the past symptom history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the past symptom history and evaluate the reliability of the entered information.

[0042] The reception desk can display guides to make it easier for users to input images of symptoms or injuries. For example, the reception desk can display guides on how to take images and the angles to ensure that users input appropriate images. The reception desk can also display step-by-step instructions for uploading images to ensure that users can input images without getting lost. Furthermore, the reception desk can perform image quality checks and display messages prompting users to retake images if inappropriate images are entered. This improves input accuracy by displaying guides to ensure that users input appropriate images. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze image quality and display appropriate guides.

[0043] The reception system can prioritize retrieving highly relevant information by considering the user's geographical location when they input images of symptoms or injuries. For example, the reception system can prioritize retrieving information on region-specific diseases and injuries based on the user's current location. It can also prioritize retrieving information on nearby medical facilities based on the user's geographical location. Furthermore, the reception system can prioritize retrieving information related to the local climate and environment by considering the user's geographical location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can use AI to analyze geographical location information and automatically retrieve highly relevant information.

[0044] The reception desk can analyze the user's social media activity and obtain relevant information when the user inputs images of symptoms or injuries. For example, the reception desk can obtain information about past symptoms or injuries from the user's social media posts. The reception desk can also obtain relevant medical information based on the user's social media activity. Furthermore, the reception desk can obtain relevant information from posts by the user's friends and followers on social media. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze social media activity and automatically obtain relevant information.

[0045] The analysis unit can adjust the accuracy of its analysis based on the level of detail in the images of symptoms or injuries during the analysis. For example, if high-resolution images are provided, the analysis unit will perform a detailed analysis. Conversely, if low-resolution images are provided, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail in the images. This allows for the provision of more accurate analysis results by adjusting the accuracy of the analysis according to the level of detail in the images. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the level of detail in the images and determine the appropriate analysis accuracy.

[0046] The analysis unit can apply different analysis algorithms depending on the category of symptoms or injuries during analysis. For example, the analysis unit can apply a specialized skin analysis algorithm to skin symptoms. It can also apply a specialized injury analysis algorithm to injuries such as fractures and bruises. Furthermore, it can apply a specialized respiratory analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the category of symptoms or injuries, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the category of symptoms or injuries and apply an appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the timing of the onset of symptoms or injuries. For example, the analysis unit may prioritize the analysis of recently occurring symptoms or injuries. The analysis unit can also analyze current symptoms or injuries while referring to symptoms or injuries that have occurred in the past. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the timing of onset. This allows for the priority of analysis of symptoms or injuries that require rapid attention, by determining the priority of analysis according to the timing of onset. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the timing of onset and determine appropriate priorities.

[0048] The analysis unit can adjust the order of analysis results based on the relationships between symptoms and injuries during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant symptoms and injuries. It can also postpone the analysis of less relevant symptoms and injuries. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to the relationships between symptoms and injuries. This allows for the priority provision of highly relevant information by adjusting the order of analysis results based on the relationships between symptoms and injuries. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the relationships between symptoms and injuries and determine the appropriate order.

[0049] The sharing unit can select the optimal sharing method by referring to past sharing information when sharing. For example, the sharing unit can prioritize the selection of sharing methods that have received high ratings in the past. The sharing unit can also propose the optimal sharing method based on past sharing information. Furthermore, the sharing unit can analyze past sharing history and select the most effective sharing method. In this way, the optimal sharing method can be selected by referring to past sharing information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can use AI to analyze past sharing information and determine the optimal sharing method.

[0050] The sharing function can customize shared information by considering the user's attribute information at the time of sharing. For example, the sharing function can customize shared information according to the user's age and gender. It can also provide optimal shared information based on the user's past sharing history. Furthermore, the sharing function can prioritize displaying highly relevant shared information by considering the user's attribute information. This allows for the provision of highly relevant shared information by considering the user's attribute information. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze the user's attribute information and determine appropriate shared information.

[0051] The sharing function can select the optimal sharing method when sharing information, taking into account the user's geographical location. For example, the sharing function can prioritize sharing region-specific information based on the user's current location. It can also prioritize sharing information about nearby medical facilities based on the user's geographical location. Furthermore, the sharing function can prioritize sharing information related to the local climate and environment, taking into account the user's geographical location. This allows for the priority sharing of region-specific information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze geographical location information and determine the optimal sharing method.

[0052] The sharing function can analyze the user's social media activity and suggest information to share when sharing. For example, the sharing function can prioritize sharing relevant information from the user's social media posts. It can also prioritize sharing relevant medical information based on the user's social media activity. Furthermore, the sharing function can prioritize sharing relevant information from the posts of the user's friends and followers on social media. In this way, relevant information can be shared preferentially by analyzing the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze social media activity and suggest appropriate information to share.

[0053] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit may prioritize notification methods that have received high ratings in the past. The notification unit can also suggest the optimal notification method based on past notification history. Furthermore, the notification unit can analyze past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze past notification history and determine the optimal notification method.

[0054] The notification unit can customize notification content by considering the user's attribute information when sending notifications. For example, the notification unit can customize notification content according to the user's age and gender. Furthermore, the notification unit can provide optimal notification content based on the user's past notification history. In addition, the notification unit can prioritize displaying highly relevant notification content by considering the user's attribute information. This allows for the provision of highly relevant notification content by considering the user's attribute information. Some or all of the above processing in the notification unit may be performed using AI, or without AI. For example, the notification unit can use AI to analyze the user's attribute information and determine appropriate notification content.

[0055] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can prioritize notifying users of region-specific information based on the user's current location. It can also prioritize notifying users of information about nearby medical facilities based on the user's geographical location information. Furthermore, the notification unit can prioritize notifying users of information related to the local climate and environment, taking into account the user's geographical location information. In this way, region-specific information can be prioritized when the user's geographical location information is considered. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit may use AI to analyze geographical location information and determine the optimal notification method.

[0056] The notification unit can analyze the user's social media activity and suggest notification content when sending a notification. For example, the notification unit can prioritize notifying users of relevant information from the user's social media posts. It can also prioritize notifying users of relevant medical information based on the user's social media activity. Furthermore, the notification unit can prioritize notifying users of relevant information from posts by the user's friends and followers on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize notifying users of relevant information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze social media activity and suggest appropriate notification content.

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

[0058] The reception desk can refer to the child's past symptom history and evaluate the reliability of the entered information. For example, if the information matches the past symptom history, the reception desk will rate the reliability of the entered information highly. If the information differs from the past symptom history, the reception desk may also display a message prompting further confirmation. Furthermore, the reception desk can score the reliability of the entered information based on the past symptom history and present it to the user. This allows the reliability of the entered information to be evaluated by referring to the past symptom history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the past symptom history and evaluate the reliability of the entered information.

[0059] The analysis unit can adjust the accuracy of its analysis based on the level of detail in the images of symptoms or injuries during the analysis. For example, if high-resolution images are provided, the analysis unit will perform a detailed analysis. Conversely, if low-resolution images are provided, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail in the images. This allows for the provision of more accurate analysis results by adjusting the accuracy of the analysis according to the level of detail in the images. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the level of detail in the images and determine the appropriate analysis accuracy.

[0060] The analysis unit can apply different analysis algorithms depending on the category of symptoms or injuries during analysis. For example, the analysis unit can apply a specialized skin analysis algorithm to skin symptoms. It can also apply a specialized injury analysis algorithm to injuries such as fractures and bruises. Furthermore, it can apply a specialized respiratory analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the category of symptoms or injuries, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the category of symptoms or injuries and apply an appropriate analysis algorithm.

[0061] The sharing unit can select the optimal sharing method by referring to past sharing information when sharing. For example, the sharing unit can prioritize the selection of sharing methods that have received high ratings in the past. The sharing unit can also propose the optimal sharing method based on past sharing information. Furthermore, the sharing unit can analyze past sharing history and select the most effective sharing method. In this way, the optimal sharing method can be selected by referring to past sharing information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can use AI to analyze past sharing information and determine the optimal sharing method.

[0062] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit may prioritize notification methods that have received high ratings in the past. The notification unit can also suggest the optimal notification method based on past notification history. Furthermore, the notification unit can analyze past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze past notification history and determine the optimal notification method.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs images of the child's symptoms or injuries. For example, images of the child's symptoms or injuries can be taken using a smartphone camera and uploaded to the app. It is also possible to select and upload existing images, and video information can also be entered. For example, the child's movements and facial expressions can be recorded as a video and uploaded to the app. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. For example, the generation AI analyzes the entered images and videos to estimate the diagnoses. It can also evaluate the severity and urgency of symptoms to determine whether hospital visits are necessary. Furthermore, it can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. Step 3: The sharing section provides a platform for parents to share information obtained by the analysis section. For example, parents can post experiences and advice, and other parents can comment or ask questions. Information can also be shared along with images and videos. Furthermore, it has a function to award points to information providers; points are awarded for providing useful information. Step 4: The notification unit sends push notifications to check on progress based on the information shared by the sharing unit. For example, it can send a push notification to check on the progress after 3 days. It can also notify progress information including hospital diagnosis results. Furthermore, it can send customized notifications based on the frequency and content of notifications set by the user.

[0065] (Example of form 2) The child illness support system according to an embodiment of the present invention is a system in which images of a child's symptoms or injuries are input, and a generating AI analyzes them to present possible diagnoses, whether a hospital visit is necessary, and the estimated number of days until full recovery, along with gentle advice. This system provides a platform in which parents can share experiences and advice and receive support from other parents. It also has a function to send a push notification three days later to check on the child's progress (including the hospital diagnosis results). This encourages active information exchange among users, and the system's accuracy is improved by accumulating and sharing information uploaded along with images. Furthermore, by linking with HELPO, medical consultations with doctors and other professionals, as well as online consultations, can be selected as needed. As a result, the child illness support system reduces parental anxiety and ensures that responses to a child's illness are carried out quickly and appropriately. It also encourages active information exchange among users and improves the system's accuracy. For example, as images and information uploaded by parents are accumulated, the accuracy of the generating AI's analysis improves, enabling more accurate diagnoses.

[0066] The child illness support system according to this embodiment comprises a reception unit, an analysis unit, a sharing unit, and a notification unit. The reception unit inputs images of the child's symptoms or injuries. The reception unit can, for example, take pictures of the child's symptoms or injuries using a smartphone camera and upload them to the app. The reception unit can also select and upload existing images. Furthermore, the reception unit can also input information in video format. For example, it can record the child's movements and facial expressions in video and upload it to the app. The analysis unit uses a generation AI to analyze the information input by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. The analysis unit, for example, uses a generation AI to analyze the input images and videos and estimate the diagnoses. The analysis unit can also evaluate the severity and urgency of the symptoms and determine whether hospital visits are necessary. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. The sharing unit provides a platform for parents to share the information obtained by the analysis unit. The sharing section allows, for example, parents to post experiences and advice, and other parents to comment and ask questions. The sharing section also allows information to be shared along with images and videos. Furthermore, the sharing section has a function to award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. The notification section sends push notifications to check on progress based on the information shared by the sharing section. For example, the notification section sends a push notification to check on the progress after three days. The notification section can also notify progress information, including hospital diagnosis results. Furthermore, the notification section can send customized notifications based on the frequency and content of notifications set by the user. As a result, the child's illness support system according to the embodiment can support appropriate responses to the child's symptoms and injuries and reduce parental anxiety.

[0067] The reception desk inputs images of the child's symptoms and injuries. For example, the reception desk can take pictures of the child's symptoms or injuries using a smartphone camera and upload them to the app. Specifically, parents can use their smartphone camera to take pictures of their child's rashes or injuries and upload the images directly to the app. The reception desk can also select and upload existing images. For example, it is possible to select and upload images taken in the past or images provided by a doctor. Furthermore, the reception desk can also input information in video format. For example, it is possible to record the child's movements and facial expressions in video and upload it to the app. This allows medical professionals and AI to perform analysis based on more detailed information. The video upload function is useful for recording things like how a child walks, how they cough, and changes in their facial expressions. This provides dynamic information that cannot be captured by still images alone, enabling more accurate diagnosis and response. The reception desk centrally manages this information and provides an interface for sending it to the analysis department. Furthermore, the reception desk has a user-friendly interface and is designed to allow parents to easily upload images and videos. For example, it includes guided upload procedures and features that automatically check the quality of images and videos. This helps the reception desk to help parents quickly and accurately enter information about their child's symptoms or injuries.

[0068] The analysis unit uses a generation AI to analyze information entered by the reception unit and presents possible diagnoses, whether a hospital visit is necessary, and the estimated number of days until complete recovery. For example, the generation AI analyzes images and videos entered by the analysis unit to estimate the diagnose. Specifically, the generation AI uses image recognition technology to analyze the shape and color of rashes, the depth and extent of injuries, etc., and compares them with past databases to estimate the diagnose. The analysis unit can also evaluate the severity and urgency of symptoms and determine whether a hospital visit is necessary. For example, the generation AI analyzes the progression of symptoms and their relationship to other symptoms, and generates a notification recommending immediate hospital visit if the situation is highly urgent. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. For example, the generation AI analyzes the progression pattern of symptoms based on past data of similar cases and predicts the duration until complete recovery. This allows parents to predict how long it will take for their child's symptoms to improve and provides them with reference information to take appropriate action. The analysis unit is equipped with an interface to present these analysis results to parents in an easy-to-understand manner. For example, the analysis results can be visually displayed in graphs and charts, allowing users to understand the diagnosis, whether hospital visits are necessary, and the estimated number of days until full recovery at a glance. The analysis unit can also provide specific advice and recommended actions based on the analysis results. This enables the analysis unit to quickly and accurately provide parents with the information necessary to take appropriate action regarding their child's symptoms.

[0069] The sharing section provides a platform for parents to share information obtained by the analysis section. For example, parents can post experiences and advice, and other parents can comment and ask questions. Specifically, parents can post experiences and coping strategies regarding their children's symptoms or injuries, and other parents can comment or ask questions. The sharing section also allows for the sharing of information along with images and videos. For example, parents can post images or videos of their children's symptoms, and other parents can offer advice. Furthermore, the sharing section includes a point system for information providers. For example, parents who provide useful information will be awarded points. These points can function as incentives, such as discounts on specific services or products. The sharing section also includes features to promote communication among parents and facilitate information sharing and mutual support. For example, it provides a tagging function to make it easier for parents to search for information on specific symptoms or injuries, and a function to display popular posts in a ranking format. The sharing section also includes privacy protection features, allowing parents to post information anonymously. This allows parents to share information with peace of mind and refer to advice and experiences from other parents. The shared area allows parents to share information with each other, supporting them in dealing with their child's illness and reducing their anxiety.

[0070] The notification unit sends push notifications to check on progress based on information shared by the sharing unit. For example, the notification unit sends a push notification to check on the progress after three days. Specifically, after a parent posts information about their child's symptoms or injury to the sharing unit, the notification unit automatically sends a push notification to check on progress after a certain period of time has passed. The notification unit can also notify about progress information, including hospital diagnosis results. For example, if a parent posts the results of a hospital diagnosis to the sharing unit, the notification unit will notify about progress based on that information. Furthermore, the notification unit can send customized notifications based on the frequency and content of notifications set by the user. For example, if a parent sets the notification frequency to once a week, the notification unit will send a progress check notification once a week. The content of the notifications can also be customized according to the parent's needs. For example, notifications can be sent that include information about specific symptoms or the latest advice from medical institutions. Through these notifications, the notification unit helps parents to continuously monitor the progress of their child's symptoms or injury and take appropriate action. Furthermore, the notification unit also has a function to provide feedback to parents after they receive a notification. For example, after receiving a notification, parents can report on the improvement of their child's symptoms or the occurrence of new symptoms. This allows the notification system to provide continuous support to parents so that they can take appropriate action when their child is unwell, and to reduce parental anxiety.

[0071] The reception desk can input images of a child's symptoms or injuries. For example, the reception desk can take pictures of a child's symptoms or injuries using a smartphone camera and upload them to the app. The reception desk can also select and upload existing images. Furthermore, the reception desk can also input information in video format. For example, it can record a child's movements and facial expressions in video and upload it to the app. This allows for accurate input of images of a child's symptoms or injuries. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can use AI to support image and video input and improve input accuracy.

[0072] The analysis unit can use a generation AI to present possible disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery. For example, the analysis unit can use a generation AI to analyze input images or videos and estimate disease names. The analysis unit can also evaluate the severity and urgency of symptoms and determine whether hospital visits are necessary. Furthermore, the analysis unit can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. In this way, by using a generation AI, it is possible to accurately present disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit presents disease names, whether hospital visits are necessary, and the estimated number of days until complete recovery based on the results of analysis by the generation AI.

[0073] The sharing section can provide a platform for parents to share experiences and advice with each other. For example, parents can post experiences and advice, and other parents can comment and ask questions. The sharing section can also share information along with images and videos. Furthermore, the sharing section has a feature to award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. This allows parents to share experiences and advice with each other, enabling quicker and more appropriate responses to children's symptoms and injuries. Some or all of the above processes in the sharing section may be performed using AI, or not. For example, the sharing section may use AI to analyze posts and automatically display relevant information.

[0074] The notification unit can send push notifications to check on the progress. For example, it can send a push notification three days later to check on the progress. The notification unit can also send progress information, including hospital diagnosis results. Furthermore, the notification unit can send customized notifications based on the frequency and content of notifications set by the user. This allows users to track the progress of their child's symptoms or injuries by sending push notifications to check on their progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze progress information and send push notifications at the appropriate time.

[0075] The sharing section can store and share information uploaded along with images. For example, the sharing section can store images and information uploaded by a parent and make them accessible to other parents. The sharing section can also use the stored information to improve the analysis accuracy of the generating AI. As a result, the accuracy of the app is improved by storing and sharing information uploaded along with images. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section can use AI to automatically classify uploaded information and display related information.

[0076] The sharing section can award points to information providers. For example, if a parent provides useful information, points will be awarded for that information. The sharing section can also offer benefits and rewards based on the accumulation of points. By awarding points to information providers, this increases the users' motivation to provide information. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section may use AI to evaluate the usefulness of the information provided and automatically award points.

[0077] The analysis unit can provide medical consultations and online medical consultations in conjunction with HELPO. For example, when a user enters information about their symptoms or injuries, the analysis unit can provide the option to consult with a doctor through HELPO. The analysis unit can also select online medical consultations if the symptoms worsen or if additional medical consultation is needed. In this way, the analysis unit can provide medical consultations and online medical consultations as needed in conjunction with HELPO. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can use generative AI to analyze the user's symptoms and present appropriate medical consultation or online medical consultation options.

[0078] The reception desk can estimate the user's emotions and adjust the input method for images of symptoms or injuries based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of images of symptoms or injuries. This allows users to input information more comfortably by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can use AI to analyze the user's emotions and suggest an appropriate input method.

[0079] The reception desk can refer to the child's past symptom history and evaluate the reliability of the entered information. For example, if the information matches the past symptom history, the reception desk will rate the reliability of the entered information highly. If the information differs from the past symptom history, the reception desk may also display a message prompting further confirmation. Furthermore, the reception desk can score the reliability of the entered information based on the past symptom history and present it to the user. This allows the reliability of the entered information to be evaluated by referring to the past symptom history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the past symptom history and evaluate the reliability of the entered information.

[0080] The reception desk can display guides to make it easier for users to input images of symptoms or injuries. For example, the reception desk can display guides on how to take images and the angles to ensure that users input appropriate images. The reception desk can also display step-by-step instructions for uploading images to ensure that users can input images without getting lost. Furthermore, the reception desk can perform image quality checks and display messages prompting users to retake images if inappropriate images are entered. This improves input accuracy by displaying guides to ensure that users input appropriate images. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze image quality and display appropriate guides.

[0081] The reception desk can estimate the user's emotions and prioritize the input information based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize processing urgent information. If the user is relaxed, the reception desk can also prioritize processing detailed information. Furthermore, if the user is in a hurry, the reception desk can prioritize processing information that requires quick processing. This allows for the priority processing of important information by prioritizing it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's emotions and determine appropriate priorities.

[0082] The reception system can prioritize retrieving highly relevant information by considering the user's geographical location when they input images of symptoms or injuries. For example, the reception system can prioritize retrieving information on region-specific diseases and injuries based on the user's current location. It can also prioritize retrieving information on nearby medical facilities based on the user's geographical location. Furthermore, the reception system can prioritize retrieving information related to the local climate and environment by considering the user's geographical location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can use AI to analyze geographical location information and automatically retrieve highly relevant information.

[0083] The reception desk can analyze the user's social media activity and obtain relevant information when the user inputs images of symptoms or injuries. For example, the reception desk can obtain information about past symptoms or injuries from the user's social media posts. The reception desk can also obtain relevant medical information based on the user's social media activity. Furthermore, the reception desk can obtain relevant information from posts by the user's friends and followers on social media. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze social media activity and automatically obtain relevant information.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit may present the results in gentle language. If the user is relaxed, the analysis unit may also provide detailed results. Furthermore, if the user is in a hurry, the analysis unit may provide concise and to-the-point results. By adjusting the presentation of the analysis results according to the user's emotions, the system can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the user's emotions and determine an appropriate presentation method.

[0085] The analysis unit can adjust the accuracy of its analysis based on the level of detail in the images of symptoms or injuries during the analysis. For example, if high-resolution images are provided, the analysis unit will perform a detailed analysis. Conversely, if low-resolution images are provided, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail in the images. This allows for the provision of more accurate analysis results by adjusting the accuracy of the analysis according to the level of detail in the images. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the level of detail in the images and determine the appropriate analysis accuracy.

[0086] The analysis unit can apply different analysis algorithms depending on the category of symptoms or injuries during analysis. For example, the analysis unit can apply a specialized skin analysis algorithm to skin symptoms. It can also apply a specialized injury analysis algorithm to injuries such as fractures and bruises. Furthermore, it can apply a specialized respiratory analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the category of symptoms or injuries, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the category of symptoms or injuries and apply an appropriate analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize presenting analysis results that indicate urgency. If the user is relaxed, the analysis unit can also prioritize presenting detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize presenting analysis results that require immediate attention. This allows for the priority of important analysis results by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the user's emotions and determine appropriate priorities.

[0088] The analysis unit can determine the priority of analysis based on the timing of the onset of symptoms or injuries. For example, the analysis unit may prioritize the analysis of recently occurring symptoms or injuries. The analysis unit can also analyze current symptoms or injuries while referring to symptoms or injuries that have occurred in the past. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the timing of onset. This allows for the priority of analysis of symptoms or injuries that require rapid attention, by determining the priority of analysis according to the timing of onset. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the timing of onset and determine appropriate priorities.

[0089] The analysis unit can adjust the order of analysis results based on the relationships between symptoms and injuries during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant symptoms and injuries. It can also postpone the analysis of less relevant symptoms and injuries. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to the relationships between symptoms and injuries. This allows for the priority provision of highly relevant information by adjusting the order of analysis results based on the relationships between symptoms and injuries. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the relationships between symptoms and injuries and determine the appropriate order.

[0090] The sharing section can estimate the user's emotions and adjust how shared information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the sharing section can display shared information in gentle language. If the user is relaxed, the sharing section can also provide detailed shared information. Furthermore, if the user is in a hurry, the sharing section can provide concise and to-the-point shared information. This allows for the provision of easily understandable information by adjusting the display method of shared information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section may use AI to analyze the user's emotions and determine the appropriate display method.

[0091] The sharing unit can select the optimal sharing method by referring to past sharing information when sharing. For example, the sharing unit can prioritize the selection of sharing methods that have received high ratings in the past. The sharing unit can also propose the optimal sharing method based on past sharing information. Furthermore, the sharing unit can analyze past sharing history and select the most effective sharing method. In this way, the optimal sharing method can be selected by referring to past sharing information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can use AI to analyze past sharing information and determine the optimal sharing method.

[0092] The sharing function can customize shared information by considering the user's attribute information at the time of sharing. For example, the sharing function can customize shared information according to the user's age and gender. It can also provide optimal shared information based on the user's past sharing history. Furthermore, the sharing function can prioritize displaying highly relevant shared information by considering the user's attribute information. This allows for the provision of highly relevant shared information by considering the user's attribute information. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze the user's attribute information and determine appropriate shared information.

[0093] The sharing section can estimate the user's emotions and prioritize shared information based on those emotions. For example, if the user is feeling anxious, the sharing section will prioritize displaying urgent shared information. It can also prioritize displaying detailed shared information if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize displaying shared information requiring immediate attention. This allows for the priority of important information by prioritizing shared information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section may use AI to analyze the user's emotions and determine appropriate priorities.

[0094] The sharing function can select the optimal sharing method when sharing information, taking into account the user's geographical location. For example, the sharing function can prioritize sharing region-specific information based on the user's current location. It can also prioritize sharing information about nearby medical facilities based on the user's geographical location. Furthermore, the sharing function can prioritize sharing information related to the local climate and environment, taking into account the user's geographical location. This allows for the priority sharing of region-specific information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze geographical location information and determine the optimal sharing method.

[0095] The sharing function can analyze the user's social media activity and suggest information to share when sharing. For example, the sharing function can prioritize sharing relevant information from the user's social media posts. It can also prioritize sharing relevant medical information based on the user's social media activity. Furthermore, the sharing function can prioritize sharing relevant information from the posts of the user's friends and followers on social media. In this way, relevant information can be shared preferentially by analyzing the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can use AI to analyze social media activity and suggest appropriate information to share.

[0096] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will send a notification using gentle language. If the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a concise and to-the-point notification. This allows for notifications that are easy for the user to understand by adjusting the content according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze the user's emotions and determine appropriate notification content.

[0097] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit may prioritize notification methods that have received high ratings in the past. The notification unit can also suggest the optimal notification method based on past notification history. Furthermore, the notification unit can analyze past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze past notification history and determine the optimal notification method.

[0098] The notification unit can customize notification content by considering the user's attribute information when sending notifications. For example, the notification unit can customize notification content according to the user's age and gender. Furthermore, the notification unit can provide optimal notification content based on the user's past notification history. In addition, the notification unit can prioritize displaying highly relevant notification content by considering the user's attribute information. This allows for the provision of highly relevant notification content by considering the user's attribute information. Some or all of the above processing in the notification unit may be performed using AI, or without AI. For example, the notification unit can use AI to analyze the user's attribute information and determine appropriate notification content.

[0099] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is feeling anxious, the notification unit will prioritize sending urgent notifications. It can also prioritize sending detailed notifications if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can prioritize sending notifications requiring immediate attention. This ensures that important notifications are delivered promptly by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze the user's emotions and determine appropriate priorities.

[0100] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can prioritize notifying users of region-specific information based on the user's current location. It can also prioritize notifying users of information about nearby medical facilities based on the user's geographical location information. Furthermore, the notification unit can prioritize notifying users of information related to the local climate and environment, taking into account the user's geographical location information. In this way, region-specific information can be prioritized when the user's geographical location information is considered. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit may use AI to analyze geographical location information and determine the optimal notification method.

[0101] The notification unit can analyze the user's social media activity and suggest notification content when sending a notification. For example, the notification unit can prioritize notifying users of relevant information from the user's social media posts. It can also prioritize notifying users of relevant medical information based on the user's social media activity. Furthermore, the notification unit can prioritize notifying users of relevant information from posts by the user's friends and followers on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize notifying users of relevant information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze social media activity and suggest appropriate notification content.

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

[0103] The reception desk can estimate the user's emotions and adjust the input method for images of symptoms or injuries based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of images of symptoms or injuries. This allows users to input information more comfortably by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can use AI to analyze the user's emotions and suggest an appropriate input method.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit may present the results in gentle language. If the user is relaxed, the analysis unit may also provide detailed results. Furthermore, if the user is in a hurry, the analysis unit may provide concise and to-the-point results. By adjusting the presentation of the analysis results according to the user's emotions, the system can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the user's emotions and determine an appropriate presentation method.

[0105] The sharing section can estimate the user's emotions and adjust how shared information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the sharing section can display shared information in gentle language. If the user is relaxed, the sharing section can also provide detailed shared information. Furthermore, if the user is in a hurry, the sharing section can provide concise and to-the-point shared information. This allows for the provision of easily understandable information by adjusting the display method of shared information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section may use AI to analyze the user's emotions and determine the appropriate display method.

[0106] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will send a notification using gentle language. If the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a concise and to-the-point notification. This allows for notifications that are easy for the user to understand by adjusting the content according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze the user's emotions and determine appropriate notification content.

[0107] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize presenting analysis results that indicate urgency. If the user is relaxed, the analysis unit can also prioritize presenting detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize presenting analysis results that require immediate attention. This allows for the priority of important analysis results by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the user's emotions and determine appropriate priorities.

[0108] The reception desk can refer to the child's past symptom history and evaluate the reliability of the entered information. For example, if the information matches the past symptom history, the reception desk will rate the reliability of the entered information highly. If the information differs from the past symptom history, the reception desk may also display a message prompting further confirmation. Furthermore, the reception desk can score the reliability of the entered information based on the past symptom history and present it to the user. This allows the reliability of the entered information to be evaluated by referring to the past symptom history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the past symptom history and evaluate the reliability of the entered information.

[0109] The analysis unit can adjust the accuracy of its analysis based on the level of detail in the images of symptoms or injuries during the analysis. For example, if high-resolution images are provided, the analysis unit will perform a detailed analysis. Conversely, if low-resolution images are provided, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis according to the level of detail in the images. This allows for the provision of more accurate analysis results by adjusting the accuracy of the analysis according to the level of detail in the images. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the level of detail in the images and determine the appropriate analysis accuracy.

[0110] The analysis unit can apply different analysis algorithms depending on the category of symptoms or injuries during analysis. For example, the analysis unit can apply a specialized skin analysis algorithm to skin symptoms. It can also apply a specialized injury analysis algorithm to injuries such as fractures and bruises. Furthermore, it can apply a specialized respiratory analysis algorithm to respiratory system symptoms. By applying the appropriate analysis algorithm according to the category of symptoms or injuries, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the category of symptoms or injuries and apply an appropriate analysis algorithm.

[0111] The sharing unit can select the optimal sharing method by referring to past sharing information when sharing. For example, the sharing unit can prioritize the selection of sharing methods that have received high ratings in the past. The sharing unit can also propose the optimal sharing method based on past sharing information. Furthermore, the sharing unit can analyze past sharing history and select the most effective sharing method. In this way, the optimal sharing method can be selected by referring to past sharing information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can use AI to analyze past sharing information and determine the optimal sharing method.

[0112] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit may prioritize notification methods that have received high ratings in the past. The notification unit can also suggest the optimal notification method based on past notification history. Furthermore, the notification unit can analyze past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze past notification history and determine the optimal notification method.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk inputs images of the child's symptoms or injuries. For example, images of the child's symptoms or injuries can be taken using a smartphone camera and uploaded to the app. It is also possible to select and upload existing images, and video information can also be entered. For example, the child's movements and facial expressions can be recorded as a video and uploaded to the app. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and presents possible diagnoses, whether hospital visits are necessary, and the estimated number of days until complete recovery. For example, the generation AI analyzes the entered images and videos to estimate the diagnoses. It can also evaluate the severity and urgency of symptoms to determine whether hospital visits are necessary. Furthermore, it can calculate the estimated number of days until complete recovery based on past data and the progression of symptoms. Step 3: The sharing section provides a platform for parents to share information obtained by the analysis section. For example, parents can post experiences and advice, and other parents can comment or ask questions. Information can also be shared along with images and videos. Furthermore, it has a function to award points to information providers; points are awarded for providing useful information. Step 4: The notification unit sends push notifications to check on progress based on the information shared by the sharing unit. For example, it can send a push notification to check on the progress after 3 days. It can also notify progress information including hospital diagnosis results. Furthermore, it can send customized notifications based on the frequency and content of notifications set by the user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, sharing unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can use the camera 42 of the smart device 14 to take images of the child's symptoms or injuries and upload them to the app via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses a generation AI to analyze the input images and videos and estimate the name of the illness. The sharing unit is implemented in the control unit 46A of the smart device 14, for example, and provides a platform for parents to share information with each other. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and sends push notifications to check on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, sharing unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can use the camera 42 of the smart glasses 214 to take images of the child's symptoms or injuries and upload them to the app via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input images and videos using a generation AI and estimates the name of the illness. The sharing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides a platform for parents to share information with each other. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which sends push notifications to check on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, sharing unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can use the camera 42 of the headset terminal 314 to take images of the child's symptoms or injuries and upload them to the app via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the input images and videos and estimate the name of the illness. The sharing unit is implemented in the control unit 46A of the headset terminal 314, which provides a platform for parents to share information with each other. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, which sends push notifications to check on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, sharing unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit can use the camera 42 of the robot 414 to take images of a child's symptoms or injuries and upload them to the app via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes input images and videos using a generation AI and estimates the name of the illness. The sharing unit is implemented, for example, by the control unit 46A of the robot 414, which provides a platform for parents to share information with each other. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which sends push notifications to check on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The reception area is where you input images of your child's symptoms or injuries, The analysis unit analyzes the information entered by the reception unit and presents possible diagnoses, whether outpatient visits are necessary, and the estimated number of days until complete recovery. A sharing unit that shares the information obtained by the analysis unit with the parent units, The system includes a notification unit that sends a push notification to check on the progress based on the information shared by the aforementioned sharing unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter images of your child's symptoms or injuries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The AI ​​generates information to suggest possible illnesses, whether hospital visits are necessary, and the estimated number of days until full recovery. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned shared portion is, We provide a platform for parents to share their experiences and advice with each other. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Send a push notification to check on the progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, Store and share information uploaded along with images. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned shared portion is, Points will be awarded to information providers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, In collaboration with HELPO, we provide medical consultations and online medical services. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for images of symptoms and injuries based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We refer to the child's past symptom history and evaluate the reliability of the entered information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input images of symptoms or injuries, guides are displayed to make the input process easier. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and prioritizes the input information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users input images of symptoms or injuries, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users input images of symptoms or injuries, the system analyzes their social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the accuracy of the analysis is adjusted based on the level of detail in the images of symptoms and injuries. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of symptoms or injuries. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the symptoms and the timing of the injury. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis results is adjusted based on the relationship between symptoms and injuries. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, It estimates the user's emotions and adjusts how shared information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, When sharing information, refer to past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shared portion is, When sharing, customize the shared information by taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, It estimates user sentiment and prioritizes shared information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, When sharing, the system selects the optimal sharing method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing, the system analyzes the user's social media activity to suggest sharing information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending a notification, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, customize the notification content by taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to suggest appropriate notification content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The reception area is where you input images of your child's symptoms or injuries, The analysis unit analyzes the information entered by the reception unit and presents possible diagnoses, whether outpatient visits are necessary, and the estimated number of days until complete recovery. A sharing unit that shares the information obtained by the analysis unit with the parent units, The system includes a notification unit that sends a push notification to check on the progress based on the information shared by the aforementioned sharing unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter images of your child's symptoms or injuries. The system according to feature 1.

3. The aforementioned analysis unit, The AI ​​generates information to suggest possible illnesses, whether hospital visits are necessary, and the estimated number of days until full recovery. The system according to feature 1.

4. The aforementioned shared portion is, We provide a platform for parents to share their experiences and advice with each other. The system according to feature 1.

5. The aforementioned notification unit, Send a push notification to check on the progress. The system according to feature 1.

6. The aforementioned shared portion is, Store and share information uploaded along with images. The system according to feature 1.

7. The aforementioned shared portion is, Points will be awarded to information providers. The system according to feature 1.

8. The aforementioned analysis unit, In collaboration with HELPO, we provide medical consultations and online medical services. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for images of symptoms and injuries based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is We refer to the child's past symptom history and evaluate the reliability of the entered information. The system according to feature 1.

Images