System

Abstract

An object of a system according to an embodiment is to enable parents to obtain appropriate advice when a child is in a poor physical condition and to efficiently share experience and information between the parents.SOLUTION: A system according to an embodiment includes a symptom analysis unit, an advice providing unit, an experience sharing unit, and a push notification unit. The symptom analysis unit analyzes symptoms and images of the child. The advice providing unit provides advice based on a result analyzed by the symptom analysis unit. The experience sharing unit shares experiences and advice between parents. The push notification unit transmits a push notification of progress confirmation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult for parents to get appropriate advice when their child was unwell, and there was a lack of efficient ways for parents to share their experiences and information with each other.

[0005] The system according to the embodiment aims to enable parents to receive appropriate advice when their children are unwell, and to enable parents to efficiently share their experiences and information with each other. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom analysis unit, an advice providing unit, an experience sharing unit, and a push notification unit. The symptom analysis unit analyzes the child's symptoms and images. The advice providing unit provides advice based on the results of the analysis by the symptom analysis unit. The experience sharing unit allows parents to share their experiences and advice with each other. The push notification unit sends a push notification to check progress. [Effects of the Invention]

[0007] The system according to the embodiment allows parents to receive appropriate advice when their child is unwell, and also allows parents to efficiently share their experiences and information with each other. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The child-rearing support app according to the embodiment of the present invention is a system that analyzes images of a child's symptoms and injuries, and presents possible illnesses, whether or not a hospital visit is necessary, and the estimated number of days until recovery, along with friendly advice. This allows parents to take appropriate action when their child is unwell, and allows them to raise their children with peace of mind while receiving support from other parents.

[0029] A parenting support app according to an embodiment includes a symptom analysis unit, an advice providing unit, an experience sharing unit, and a push notification unit. The symptom analysis unit analyzes a child's symptoms and images. For example, the symptom analysis unit analyzes symptoms such as fever and cough to estimate a possible illness. The symptom analysis unit can also analyze images of injuries and determine the severity of the injury. The symptom analysis unit can also refer to past case data to perform more accurate analysis. The advice providing unit provides advice based on the results of the analysis by the symptom analysis unit. For example, if a child is likely to have a cold, the advice providing unit may provide advice such as "If the symptoms persist for more than three days, please see a doctor." The advice providing unit may also provide videos of home care methods. The advice providing unit may also provide counseling advice to reduce parents' anxiety and stress using an emotion estimation function. The experience sharing unit allows parents to share their experiences and advice with each other. For example, the experience sharing unit allows parents to post their child's symptoms and treatment progress and receive comments and advice from other parents. The experience sharing unit may also provide a chat function between parents, enabling real-time information exchange. The experience sharing unit can also use the emotion estimation function to perform emotion analysis of the posted content and prioritize positive experiences. The push notification unit can send a push notification to check progress. For example, the push notification unit can send a notification such as, "Please tell us about the progress of the symptoms you entered three days ago." The user can then enter their progress and hospital diagnosis results. The push notification unit can also use the emotion estimation function to send notifications containing encouragement or advice based on the parent's emotional state. The push notification unit can also optimize the timing of notifications to match the user's daily routine. As a result, the parenting support app according to the embodiment allows parents to take appropriate measures when their child is unwell and raise their child with peace of mind while receiving support from other parents. For example, if a child's symptoms are mild, parents can make appropriate decisions, such as continuing to care for their child at home and, if they are severe, taking them to a hospital early. Furthermore, information sharing between parents accumulates parenting experience points and strengthens the support system for the entire community.

[0030] The symptom analysis unit can refer to a database of similar past cases to make more accurate disease name predictions. For example, when the generation AI analyzes symptoms and images, the symptom analysis unit refers to a database of similar past cases to extract highly similar cases. For example, if the patient has symptoms of fever and cough, the disease name will be predicted based on similar past cases. By referring to past data, the accuracy of disease name predictions can be improved.

[0031] The advice providing unit can provide the user with specific home care methods in the form of a video based on the analysis results of the symptoms and images. For example, the advice providing unit uses a generation AI to analyze the symptoms and images, and provides home care methods in the form of a video based on the results. For example, a video may explain how to cool down when you have a fever and how to hydrate. This allows the user to take appropriate care by providing specific home care methods in the form of a video.

[0032] The symptom analysis unit can provide a function to analyze voice input in addition to inputting symptoms and images. For example, the symptom analysis unit provides a function to analyze voice input in addition to inputting symptoms and images by the generation AI. For example, the symptoms described orally by a parent can be analyzed using voice recognition technology and used to estimate the name of the disease. In this way, by analyzing the voice input, the content described orally by a parent can also be included in the analysis.

[0033] The symptom analysis unit can provide a function to share the analysis results of symptoms and images with other medical institutions and specialists to obtain a second opinion. For example, the generative AI can share the analysis results of symptoms and images with other medical institutions and specialists to obtain a second opinion. For example, the analysis results can be sent to a specialist to request their opinion. This can increase the accuracy of the diagnosis by obtaining a second opinion.

[0034] The generation AI can automatically categorize posted experiences and advice and generate a report that compiles related information. For example, the generation AI can automatically categorize posted experiences and advice and generate a report that compiles related information. For example, it can group posts about the same symptoms together into a report. This makes it more efficient to organize and share information by automatically categorizing posted experiences and advice and generating a report that compiles related information.

[0035] A platform for sharing experiences between parents can add a chat function between parents, enabling real-time information exchange. A platform for sharing experiences between parents can add a chat function between parents, enabling real-time information exchange. For example, a chat room can be provided where parents can consult in real time about their children's symptoms. By adding a chat function between parents, real-time information exchange becomes possible.

[0036] The experience-sharing platform for parents can hold regular webinars by medical experts to provide the latest medical information. For example, in addition to sharing experiences between parents, the experience-sharing platform for parents can hold regular webinars by medical experts to provide the latest medical information. For example, a webinar on child health management can be held. This allows parents to share the latest medical information with each other by holding a webinar by a medical expert.

[0037] The experience sharing platform for parents can add ratings and word-of-mouth information about medical institutions by region. The experience sharing platform for parents can add ratings and word-of-mouth information about medical institutions by region. For example, it provides a function to share ratings of nearby hospitals and clinics. This allows parents to share information more effectively by adding ratings and word-of-mouth information about medical institutions by region.

[0038] The generation AI can customize the content of the push notification based on the user's past input data and send a more personalized notification. The generation AI can, for example, customize the content of the push notification based on the user's past input data and send a personalized notification. For example, it can provide appropriate advice based on the user's past symptoms. This allows the user to send a more personalized notification by customizing the content of the push notification based on the user's past input data.

[0039] The progress check push notification can provide checklist-style questions that the user can easily enter. The progress check push notification can provide checklist-style questions that the user can easily enter, for example, when checking progress. For example, the user can select the presence or absence of symptoms and the state of improvement in a checklist. In this way, by providing checklist-style questions, the user can easily check progress.

[0040] The timing of progress check push notifications can be optimized to match the user's lifestyle. For example, the timing of progress check push notifications can be optimized to match the user's lifestyle. For example, notifications can be sent during the time period when the user is most relaxed. By optimizing the timing of push notifications to match the user's lifestyle, the effectiveness of notifications can be increased.

[0041] The progress check push notification allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms. The progress check push notification, for example, allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms. For example, a function for sharing progress reports is provided. This allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms.

[0042] The generation AI can analyze uploaded images and compare them with similar cases to make an evaluation. For example, the generation AI can analyze uploaded images and compare them with similar cases to make an evaluation. For example, it can analyze an image of a skin rash and compare it with past rash cases to make an evaluation. This allows for more accurate diagnoses by analyzing uploaded images and comparing them with similar cases to make an evaluation.

[0043] The amount of points awarded can be dynamically adjusted according to the frequency and quality of information provided. The amount of points awarded can be dynamically adjusted according to the frequency and quality of information provided. For example, more points can be awarded to users who frequently provide information. This makes it possible to increase user motivation by dynamically adjusting the amount of points awarded according to the frequency and quality of information provided.

[0044] In addition to uploading images and information, it is also possible to upload videos, allowing for more detailed information to be shared. In addition to uploading images and information, it is also possible to upload videos, allowing for more detailed information to be shared. For example, a video showing how to treat an injury can be shared. By enabling the upload of videos, more detailed information can be shared.

[0045] It is possible to make it possible to exchange points for coupons that can be used at affiliated online shops and services. It is possible to make it possible to exchange points for coupons that can be used at affiliated online shops and services. For example, a discount coupon that can be used at an online shop can be provided. By making it possible to exchange points for coupons, it is possible to increase incentives for users.

[0046] The generation AI periodically re-learns the accumulated data, allowing for continuous improvement in analysis accuracy. The generation AI, for example, periodically re-learns the accumulated data, allowing for continuous improvement in analysis accuracy. For example, the generation AI re-learns data every month to improve the accuracy of disease name estimation. In this way, the generation AI can periodically re-learn, allowing for continuous improvement in analysis accuracy.

[0047] Based on the accumulated data, an individual health management plan can be proposed to the user. Based on the accumulated data, an individual health management plan can be proposed to the user. For example, a health management plan can be created based on past symptoms and treatment history. This makes it possible to support the user's health management by proposing an individual health management plan based on the accumulated data.

[0048] Sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge. Sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge. For example, providing case data to research institutions can be used to develop new treatments. In this way, sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge.

[0049] The results of the data analysis are fed back to the user on a regular basis, raising awareness of health management. The results of the data analysis are fed back to the user on a regular basis, raising awareness of health management. For example, a monthly report can be provided to notify the user of changes in their health status. In this way, by feeding back the results of the data analysis on a regular basis, it is possible to raise awareness of health management for the user.

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

[0051] The parenting support app can provide voice guidance when parents input their child's symptoms. For example, when parents input symptoms, a voice prompt can ask, "What is your child's temperature?" and the parent can respond to assist them. The voice prompt can also guide parents step by step through the steps until they complete the input, allowing parents to smoothly input symptoms.

[0052] When parents enter their child's symptoms, parenting support apps can provide a function that automatically completes the information by referencing previously entered data. For example, if a symptom previously entered recurs, the app can automatically display the previous data, allowing parents to easily complete the input. It can also provide appropriate advice to parents based on the past data. This reduces the burden on parents of inputting information.

[0053] When parents input their child's symptoms, the parenting support app can automatically analyze them using image recognition technology. For example, if a parent uploads a picture of their child's rash, the app will automatically determine the type of rash and provide appropriate advice. Image recognition technology can also be used to determine the severity of the injury, allowing parents to quickly and accurately understand the symptoms.

[0054] Parenting support apps can provide a function that allows parents to refer to the experiences of other parents when they enter their child's symptoms. For example, they can display posts from parents whose children have the same symptoms for reference. They can also suggest appropriate ways to respond based on the advice of other parents. This allows parents to take appropriate action by taking advantage of the experiences of other parents.

[0055] When parents input their child's symptoms, the parenting support app can automatically determine the severity of the symptoms and suggest appropriate responses. For example, it can suggest home care methods for mild symptoms, and advise parents to visit a medical institution immediately for severe symptoms. It can also provide appropriate support to parents depending on the severity of the symptoms, allowing parents to take appropriate measures.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The symptom analysis unit analyzes the child's symptoms and images. For example, it analyzes symptoms such as fever and coughing to estimate possible illnesses. It can also analyze images of injuries to determine their severity. It can also refer to past case data to perform more accurate analysis. Step 2: The advice provider provides advice based on the results of the analysis by the symptom analyzer. For example, if there is a high possibility of a cold, the advice provider may provide advice such as "If the symptoms persist for more than three days, please see a doctor." It can also provide videos on how to care for the child at home. Furthermore, it can use its emotion estimation function to provide counseling advice to reduce parents' anxiety and stress. Step 3: The experience sharing section allows parents to share their experiences and advice with each other. For example, parents can post information about their child's symptoms and treatment progress, and receive comments and advice from other parents. It can also provide a chat function between parents, enabling real-time information exchange. Furthermore, it can use an emotion estimation function to analyze the sentiment of the posts and prioritize positive experiences. Step 4: The push notification module sends a progress check push notification. For example, it can send a notification such as "Please tell us the progress of the symptoms you entered three days ago," and the user can enter the progress and hospital diagnosis results. It can also use the emotion estimation function to send notifications containing encouragement and advice based on the parent's emotional state. Furthermore, it can optimize the timing of notifications to match the user's daily routine.

[0058] (Example 2) The child-rearing support app according to the embodiment of the present invention is a system that analyzes images of a child's symptoms and injuries, and presents possible illnesses, whether or not a hospital visit is necessary, and the estimated number of days until recovery, along with friendly advice. This allows parents to take appropriate action when their child is unwell, and allows them to raise their children with peace of mind while receiving support from other parents.

[0059] A parenting support app according to an embodiment includes a symptom analysis unit, an advice providing unit, an experience sharing unit, and a push notification unit. The symptom analysis unit analyzes a child's symptoms and images. For example, the symptom analysis unit analyzes symptoms such as fever and cough to estimate a possible illness. The symptom analysis unit can also analyze images of injuries and determine the severity of the injury. The symptom analysis unit can also refer to past case data to perform more accurate analysis. The advice providing unit provides advice based on the results of the analysis by the symptom analysis unit. For example, if a child is likely to have a cold, the advice providing unit may provide advice such as "If the symptoms persist for more than three days, please see a doctor." The advice providing unit may also provide videos of home care methods. The advice providing unit may also provide counseling advice to reduce parents' anxiety and stress using an emotion estimation function. The experience sharing unit allows parents to share their experiences and advice with each other. For example, the experience sharing unit allows parents to post their child's symptoms and treatment progress and receive comments and advice from other parents. The experience sharing unit may also provide a chat function between parents, enabling real-time information exchange. The experience sharing unit can also use the emotion estimation function to perform emotion analysis of the posted content and prioritize positive experiences. The push notification unit can send a push notification to check progress. For example, the push notification unit can send a notification such as, "Please tell us about the progress of the symptoms you entered three days ago." The user can then enter their progress and hospital diagnosis results. The push notification unit can also use the emotion estimation function to send notifications containing encouragement or advice based on the parent's emotional state. The push notification unit can also optimize the timing of notifications to match the user's daily routine. As a result, the parenting support app according to the embodiment allows parents to take appropriate measures when their child is unwell and raise their child with peace of mind while receiving support from other parents. For example, if a child's symptoms are mild, parents can make appropriate decisions, such as continuing to care for their child at home and, if they are severe, taking them to a hospital early. Furthermore, information sharing between parents accumulates parenting experience points and strengthens the support system for the entire community.

[0060] The symptom analysis unit can refer to a database of similar past cases to make more accurate disease name predictions. For example, when the generation AI analyzes symptoms and images, the symptom analysis unit refers to a database of similar past cases to extract highly similar cases. For example, if the patient has symptoms of fever and cough, the disease name will be predicted based on similar past cases. By referring to past data, the accuracy of disease name predictions can be improved.

[0061] The advice providing unit can provide the user with specific home care methods in the form of a video based on the analysis results of the symptoms and images. For example, the advice providing unit uses a generation AI to analyze the symptoms and images, and provides home care methods in the form of a video based on the results. For example, a video may explain how to cool down when you have a fever and how to hydrate. This allows the user to take appropriate care by providing specific home care methods in the form of a video.

[0062] The advice providing unit can use the emotion estimation function to provide counseling advice to reduce the anxiety and stress of parents. For example, the generation AI analyzes symptoms and images, and the advice providing unit uses the emotion estimation function to provide counseling advice to reduce the anxiety and stress of parents. For example, it provides advice on relaxation methods and stress management. In this way, counseling advice to reduce the anxiety and stress of parents can be provided, thereby reducing the psychological burden on parents.

[0063] The symptom analysis unit can provide a function to analyze voice input in addition to inputting symptoms and images. For example, the symptom analysis unit provides a function to analyze voice input in addition to inputting symptoms and images by the generation AI. For example, the symptoms described orally by a parent can be analyzed using voice recognition technology and used to estimate the name of the disease. In this way, by analyzing the voice input, the content described orally by a parent can also be included in the analysis.

[0064] The symptom analysis unit can provide a function to share the analysis results of symptoms and images with other medical institutions and specialists to obtain a second opinion. For example, the generative AI can share the analysis results of symptoms and images with other medical institutions and specialists to obtain a second opinion. For example, the analysis results can be sent to a specialist to request their opinion. This can increase the accuracy of the diagnosis by obtaining a second opinion.

[0065] The advice providing unit can use the emotion estimation function to customize the tone and content of advice according to the parent's emotional state. For example, the generation AI uses the emotion estimation function to customize the tone and content of advice according to the parent's emotional state. For example, if the parent is feeling very anxious, the advice can be provided in a gentle tone. This makes it possible to provide advice according to the parent's emotional state, thereby reducing the psychological burden on the parent.

[0066] The generation AI can automatically categorize posted experiences and advice and generate a report that compiles related information. For example, the generation AI can automatically categorize posted experiences and advice and generate a report that compiles related information. For example, it can group posts about the same symptoms together into a report. This makes it more efficient to organize and share information by automatically categorizing posted experiences and advice and generating a report that compiles related information.

[0067] A platform for sharing experiences between parents can add a chat function between parents, enabling real-time information exchange. A platform for sharing experiences between parents can add a chat function between parents, enabling real-time information exchange. For example, a chat room can be provided where parents can consult in real time about their children's symptoms. By adding a chat function between parents, real-time information exchange becomes possible.

[0068] The emotion estimation function can perform an emotion analysis of the posted content and prioritize the display of positive experiences. The emotion estimation function, for example, performs an emotion analysis of the posted content and prioritizes the display of positive experiences. For example, posts containing gratitude are prioritized. In this way, by performing an emotion analysis and prioritizing the display of positive experiences, interactions between parents become more positive.

[0069] The experience-sharing platform for parents can hold regular webinars by medical experts to provide the latest medical information. For example, in addition to sharing experiences between parents, the experience-sharing platform for parents can hold regular webinars by medical experts to provide the latest medical information. For example, a webinar on child health management can be held. This allows parents to share the latest medical information with each other by holding a webinar by a medical expert.

[0070] The experience sharing platform for parents can add ratings and word-of-mouth information about medical institutions by region. The experience sharing platform for parents can add ratings and word-of-mouth information about medical institutions by region. For example, it provides a function to share ratings of nearby hospitals and clinics. This allows parents to share information more effectively by adding ratings and word-of-mouth information about medical institutions by region.

[0071] The emotion estimation function can analyze the emotional state of parents when they post and provide appropriate support. The emotion estimation function can, for example, analyze the emotional state of parents when they post and provide appropriate support. For example, if they are feeling very anxious, it can display an encouraging message. In this way, by analyzing the emotional state of parents when they post and providing appropriate support, it is possible to reduce the psychological burden on parents.

[0072] The generation AI can customize the content of the push notification based on the user's past input data and send a more personalized notification. The generation AI can, for example, customize the content of the push notification based on the user's past input data and send a personalized notification. For example, it can provide appropriate advice based on the user's past symptoms. This allows the user to send a more personalized notification by customizing the content of the push notification based on the user's past input data.

[0073] The progress check push notification can provide checklist-style questions that the user can easily enter. The progress check push notification can provide checklist-style questions that the user can easily enter, for example, when checking progress. For example, the user can select the presence or absence of symptoms and the state of improvement in a checklist. In this way, by providing checklist-style questions, the user can easily check progress.

[0074] The emotion estimation function can send notifications containing encouragement or advice according to the parent's emotional state. The emotion estimation function, for example, sends notifications containing encouragement or advice according to the parent's emotional state. For example, if the parent is feeling very anxious, an encouraging message is sent. In this way, by sending notifications containing encouragement or advice according to the parent's emotional state, the psychological burden on the parent can be reduced.

[0075] The timing of progress check push notifications can be optimized to match the user's lifestyle. For example, the timing of progress check push notifications can be optimized to match the user's lifestyle. For example, notifications can be sent during the time period when the user is most relaxed. By optimizing the timing of push notifications to match the user's lifestyle, the effectiveness of notifications can be increased.

[0076] The progress check push notification allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms. The progress check push notification, for example, allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms. For example, a function for sharing progress reports is provided. This allows the results of the progress check to be shared with other parents, promoting information exchange between parents of children with the same symptoms.

[0077] The emotion estimation function can adjust the content of notifications according to the parent's emotional state, thereby reducing stress. For example, if the parent is feeling very anxious, the emotion estimation function can send a notification in a gentle tone. This allows the parent's stress to be reduced by adjusting the content of notifications according to the parent's emotional state.

[0078] The generation AI can analyze uploaded images and compare them with similar cases to make an evaluation. For example, the generation AI can analyze uploaded images and compare them with similar cases to make an evaluation. For example, it can analyze an image of a skin rash and compare it with past rash cases to make an evaluation. This allows for more accurate diagnoses by analyzing uploaded images and comparing them with similar cases to make an evaluation.

[0079] The amount of points awarded can be dynamically adjusted according to the frequency and quality of information provided. The amount of points awarded can be dynamically adjusted according to the frequency and quality of information provided. For example, more points can be awarded to users who frequently provide information. This makes it possible to increase user motivation by dynamically adjusting the amount of points awarded according to the frequency and quality of information provided.

[0080] The emotion estimation function can analyze the emotional state of the information provider and provide positive feedback. The emotion estimation function can, for example, analyze the emotional state of the information provider and provide positive feedback. For example, it can send a message of gratitude. In this way, by analyzing the emotional state of the information provider and providing positive feedback, it is possible to increase the motivation of the information provider.

[0081] In addition to uploading images and information, it is also possible to upload videos, allowing for more detailed information to be shared. In addition to uploading images and information, it is also possible to upload videos, allowing for more detailed information to be shared. For example, a video showing how to treat an injury can be shared. By enabling the upload of videos, more detailed information can be shared.

[0082] It is possible to make it possible to exchange points for coupons that can be used at affiliated online shops and services. It is possible to make it possible to exchange points for coupons that can be used at affiliated online shops and services. For example, a discount coupon that can be used at an online shop can be provided. By making it possible to exchange points for coupons, it is possible to increase incentives for users.

[0083] The emotion estimation function can provide incentives according to the emotional state of the information provider, thereby increasing motivation. The emotion estimation function can provide incentives according to the emotional state of the information provider, thereby increasing motivation. For example, if positive emotions are strong, additional points can be awarded. In this way, by providing incentives according to the emotional state of the information provider, it is possible to increase the motivation of the information provider.

[0084] The generation AI periodically re-learns the accumulated data, allowing for continuous improvement in analysis accuracy. The generation AI, for example, periodically re-learns the accumulated data, allowing for continuous improvement in analysis accuracy. For example, the generation AI re-learns data every month to improve the accuracy of disease name estimation. In this way, the generation AI can periodically re-learn, allowing for continuous improvement in analysis accuracy.

[0085] Based on the accumulated data, an individual health management plan can be proposed to the user. Based on the accumulated data, an individual health management plan can be proposed to the user. For example, a health management plan can be created based on past symptoms and treatment history. This makes it possible to support the user's health management by proposing an individual health management plan based on the accumulated data.

[0086] The emotion estimation function can provide analysis results that take into account the user's emotional state. For example, if the user is highly anxious, the emotion estimation function can provide analysis results in a gentler tone. This can reduce the user's psychological burden by providing analysis results that take into account the user's emotional state.

[0087] Sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge. Sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge. For example, providing case data to research institutions can be used to develop new treatments. In this way, sharing accumulated data with medical research institutions can contribute to the discovery of new medical knowledge.

[0088] The results of the data analysis are fed back to the user on a regular basis, raising awareness of health management. The results of the data analysis are fed back to the user on a regular basis, raising awareness of health management. For example, a monthly report can be provided to notify the user of changes in their health status. In this way, by feeding back the results of the data analysis on a regular basis, it is possible to raise awareness of health management for the user.

[0089] The emotion estimation function provides feedback according to the user's emotional state, which can encourage continued use. The emotion estimation function provides feedback according to the user's emotional state, which can encourage continued use. For example, if the user is feeling very anxious, an encouraging message can be sent. In this way, by providing feedback according to the user's emotional state, it is possible to encourage continued use.

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

[0091] Parenting support apps can monitor parents' stress levels and suggest relaxation methods. For example, if a parent is feeling high stress, they can suggest deep breathing or meditation. They can also provide music or podcasts to reduce stress. Furthermore, they can suggest relaxation activities at times when parents are most comfortable.

[0092] The parenting support app can provide voice guidance when parents input their child's symptoms. For example, when parents input symptoms, a voice prompt can ask, "What is your child's temperature?" and the parent can respond to assist them. The voice prompt can also guide parents step by step through the steps until they complete the input, allowing parents to smoothly input symptoms.

[0093] When parents enter their child's symptoms, parenting support apps can provide a function that automatically completes the information by referencing previously entered data. For example, if a symptom previously entered recurs, the app can automatically display the previous data, allowing parents to easily complete the input. It can also provide appropriate advice to parents based on the past data. This reduces the burden on parents of inputting information.

[0094] When parents input their child's symptoms, the parenting support app uses an emotion estimation function to analyze the parent's emotional state and provide appropriate support. For example, if a parent is feeling anxious, the app will display an encouraging message. If a parent is feeling stressed, the app can also suggest relaxation methods. This helps to reduce the psychological burden on parents.

[0095] When parents input their child's symptoms, the parenting support app can automatically analyze them using image recognition technology. For example, if a parent uploads a picture of their child's rash, the app will automatically determine the type of rash and provide appropriate advice. Image recognition technology can also be used to determine the severity of the injury, allowing parents to quickly and accurately understand the symptoms.

[0096] When parents input their child's symptoms, the parenting support app can use its emotion estimation function to provide advice based on the parent's emotional state. For example, if a parent is feeling anxious, the app will provide advice in a gentle tone. If a parent is feeling stressed, the app can also suggest ways to manage stress. This can help reduce the psychological burden on parents.

[0097] Parenting support apps can provide a function that allows parents to refer to the experiences of other parents when they enter their child's symptoms. For example, they can display posts from parents whose children have the same symptoms for reference. They can also suggest appropriate ways to respond based on the advice of other parents. This allows parents to take appropriate action by taking advantage of the experiences of other parents.

[0098] When parents input their child's symptoms, the parenting support app can use its emotion estimation function to analyze the parent's emotional state and provide appropriate counseling advice. For example, if a parent is feeling anxious, the app can provide advice on relaxation methods and stress management. It can also provide a function to encourage interaction with other parents if a parent feels lonely. This can help reduce the psychological burden on parents.

[0099] When parents input their child's symptoms, the parenting support app can automatically determine the severity of the symptoms and suggest appropriate responses. For example, it can suggest home care methods for mild symptoms, and advise parents to visit a medical institution immediately for severe symptoms. It can also provide appropriate support to parents depending on the severity of the symptoms, allowing parents to take appropriate measures.

[0100] When parents input their child's symptoms, the parenting support app uses an emotion estimation function to analyze the parent's emotional state and provide appropriate feedback. For example, if a parent is feeling anxious, the app will display an encouraging message. If a parent is feeling stressed, the app can also suggest relaxation methods. This helps to reduce the psychological burden on parents.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The symptom analysis unit analyzes the child's symptoms and images. For example, it analyzes symptoms such as fever and coughing to estimate possible illnesses. It can also analyze images of injuries to determine their severity. It can also refer to past case data to perform more accurate analysis. Step 2: The advice provider provides advice based on the results of the analysis by the symptom analyzer. For example, if there is a high possibility of a cold, the advice provider may provide advice such as "If the symptoms persist for more than three days, please see a doctor." It can also provide videos on how to care for the child at home. Furthermore, it can use its emotion estimation function to provide counseling advice to reduce parents' anxiety and stress. Step 3: The experience sharing section allows parents to share their experiences and advice with each other. For example, parents can post information about their child's symptoms and treatment progress, and receive comments and advice from other parents. It can also provide a chat function between parents, enabling real-time information exchange. Furthermore, it can use an emotion estimation function to analyze the sentiment of the posts and prioritize positive experiences. Step 4: The push notification module sends a progress check push notification. For example, it can send a notification such as "Please tell us the progress of the symptoms you entered three days ago," and the user can enter the progress and hospital diagnosis results. It can also use the emotion estimation function to send notifications containing encouragement and advice based on the parent's emotional state. Furthermore, it can optimize the timing of notifications to match the user's daily routine.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A symptom analysis section that analyzes the child's symptoms and images, an advice providing unit that provides advice based on the results of the analysis by the symptom analysis unit; An experience sharing section where parents can share their experiences and advice with each other, A push notification unit that transmits a progress confirmation push notification. A system characterized by:

2. The symptom analysis unit In addition to inputting the symptoms and images, it also provides a function to analyze voice input.

2. The system of claim 1.

3. The generating AI is Automatically categorize the submitted experiences and advice and generate a report summarizing the relevant information.

2. The system of claim 1.

4. The generating AI is Customize the content of the push notification based on the user's past input data to send more personalized notifications.

2. The system of claim 1.

5. The generating AI is The uploaded images are analyzed and compared with similar cases for evaluation.

2. The system of claim 1.

6. The emotion estimation function is Providing analysis results that take into account the user's emotional state 2. The system of claim 1.

7. The advice providing unit Offer counseling advice to reduce the parent's anxiety and stress 2. The system of claim 1.

8. The emotion estimation function is Analyzes the sentiment of posts and prioritizes positive experiences 2. The system of claim 1.

Images