System

Abstract

An object of a system according to an embodiment is to provide information related to childcare in an integrated manner and provide optimal advice to individual babies.SOLUTION: A system includes an information providing unit, a data storage unit, an analysis unit, and an advice providing unit. The information providing unit provides information on childcare. The data storage part stores the data of the baby. The analysis unit analyzes the data accumulated by the data accumulation unit. The advice providing unit provides advice on the basis of a result analyzed by the analysis unit.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, information about child-rearing is scattered, making it difficult for first-time parents to obtain appropriate information.

[0005] The system according to the embodiment aims to provide information on child-rearing in a unified manner and to provide optimal advice for each individual baby. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, a data storage unit, an analysis unit, and an advice providing unit. The information providing unit provides information about child rearing. The data storage unit stores data about the baby. The analysis unit analyzes the data stored by the data storage unit. The advice providing unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on child-rearing in a unified manner and provide optimal advice for each individual baby. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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 system according to an embodiment of the present invention provides a comprehensive range of information, from general information about child-rearing to actual events, advice from specialists, and baby management. This eliminates the need to rely on various reference books and online information, allowing users to receive optimal child-rearing support for their own children. It also makes it possible to supplement baby management and child-rearing knowledge from remote locations, allowing users to participate in child-rearing while working.

[0029] A child-rearing support system according to an embodiment includes an information providing unit, a data storage unit, an analysis unit, and an advice providing unit. The information providing unit provides information about child-rearing. For example, it provides appropriate care methods according to the baby's developmental stage, disease prevention methods, meal timing and content, etc. The data storage unit stores baby data. For example, it stores data such as the intonation and nuance of the baby's crying, sleep duration and quality, meal times, and diaper change timing. The analysis unit uses a generation AI to analyze the data stored by the data storage unit. For example, the generation AI analyzes the baby's condition based on data on the baby's crying and movements and generates appropriate suggestions. The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the generation AI suggests appropriate care methods and disease prevention methods according to the baby's health condition and developmental status. As a result, the child-rearing support system according to an embodiment provides information about child-rearing in one place, accumulates and analyzes baby data, and provides appropriate advice, thereby alleviating the anxiety of couples who have no experience raising children.

[0030] The information provision unit can provide child-rearing information based on the family's culture and values. For example, the information provision unit uses a generation AI to provide child-rearing information based on the family's culture and values. For example, it may suggest child-rearing methods that take into account religious background and family traditions. In addition, in order to customize child-rearing information according to the family's values, the generation AI learns the family's history and the parents' child-rearing policies and provides advice based on that. Furthermore, the information provision unit uses the generation AI to understand the family's cultural background and provide appropriate child-rearing information based on that. For example, it may provide advice that takes into account eating and sleeping habits in a particular culture. This allows the provision of child-rearing information based on the family's culture and values, making it possible to provide advice that is optimal for each individual family.

[0031] The information provision unit can perform simulations based on past successes and failures and provide specific advice. For example, the generation AI learns from past successes and failures and performs simulations based on them to provide specific parenting advice. For example, it suggests the optimal way to respond in a specific situation. The information provision unit also performs simulations based on past child-rearing data and provides specific advice to parents. For example, it derives solutions to specific problems from the simulation results. Furthermore, the information provision unit has the generation AI analyze past cases and provide specific parenting advice to parents through simulations. For example, it predicts the results of specific actions and suggests the optimal response. In this way, specific advice can be provided by performing simulations based on past successes and failures.

[0032] The information providing unit can provide parenting information as visual infographics or animations. For example, the information providing unit uses a generation AI to generate parenting information as visual infographics and provide it in a form that is easy for parents to understand. For example, it shows a baby's developmental stages in diagrams. The information providing unit also uses a generation AI to create parenting information as animations and provide it in a form that is visually easy for parents to understand. For example, it explains child-rearing steps in animations. Furthermore, the information providing unit uses a generation AI to provide parenting information as visually easy-to-understand infographics or animations. For example, it shows the timing and content of meals in diagrams. In this way, providing parenting information as visual infographics or animations makes it easier for parents to understand.

[0033] The information provision unit can collect parenting information in different languages ​​and regions and provide advice from a global perspective. For example, the information provision unit uses a generation AI to collect parenting information in different languages ​​and regions and provide advice from a global perspective. For example, it introduces child-rearing methods in different cultural spheres. The information provision unit also collects parenting information in different regions and the generation AI uses that information to provide advice from a global perspective. For example, it provides advice that takes into account the child-rearing customs and traditions of each country. Furthermore, the information provision unit uses the generation AI to collect parenting information in different languages ​​and regions and provide advice to parents from a global perspective. For example, it introduces eating and sleeping habits in different cultural spheres. This makes it possible to respond to diverse cultures by collecting parenting information in different languages ​​and regions and providing advice from a global perspective.

[0034] It is possible to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, using generative AI, we can develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, by analyzing the frequency and rhythm of the crying. We can also collect baby crying data and develop an algorithm that uses generative AI to analyze it and identify the cause of the crying. For example, by analyzing the volume and duration of the crying. We can also use generative AI to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, by analyzing the tone and pitch of the crying. This makes it possible to analyze a baby's crying patterns and identify the cause of the crying, allowing for appropriate responses.

[0035] A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. For example, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, smiling and crying facial expressions can be analyzed. Data on a baby's movements and facial expressions can also be collected, and generative AI can analyze it in real time to estimate its health and emotional state. For example, it can analyze limb movements and facial expressions. Furthermore, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, it can analyze movement patterns and changes in facial expressions. This allows a baby's movements and facial expressions to be analyzed in real time to estimate its health and emotional state, enabling appropriate care.

[0036] Based on the data, it is possible to predict future health risks and suggest preventive measures. Based on the data, it is possible to predict future health risks and suggest preventive measures. For example, generative AI can be used to predict future health risks based on a baby's data and suggest preventive measures. For example, advice on diet and exercise can be provided. In addition, baby data can be collected and generative AI can analyze it to predict future health risks and suggest preventive measures. For example, it can predict the risk of allergies. Furthermore, generative AI can be used to predict future health risks based on the baby's data and suggest preventive measures. For example, it can predict the risk of obesity and diabetes. This makes it possible to predict future health risks based on the baby's data and suggest preventive measures, making baby health management more effective.

[0037] The data can be compared with data from other families to identify common problems and solutions. The data can be compared with data from other families to identify common problems and solutions. For example, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, data from babies of the same age can be compared. Alternatively, baby data can be collected and generative AI can compare it with data from other families to identify common problems and solutions. For example, sleep patterns and meal timings can be compared. Furthermore, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, health and developmental status can be compared. This allows for more effective parenting support by comparing a baby's data with data from other families to identify common problems and solutions.

[0038] Specialist advice can be customized to suit the family's situation. Specialist advice can be customized to suit the family's situation. For example, generative AI can be used to customize specialist advice to suit the individual family's situation. For example, advice can be provided that takes into account the family's daily routine and the parents' child-rearing preferences. In addition, to customize specialist advice to suit the family's situation, generative AI learns from family data and provides advice based on that. Furthermore, generative AI can understand the family's situation and customize the specialist advice based on that. For example, advice can be provided that is tailored to specific health or developmental conditions. This allows specialist advice to be customized to suit the individual family's situation, making it possible to provide more effective advice.

[0039] Specialist advice can be optimized by comparing it with past data. Specialist advice can be optimized by comparing it with past data. For example, generative AI can be used to optimize specialist advice by comparing it with past data. For example, the optimal care method can be suggested based on past health data. Past data can also be collected and analyzed by generative AI to optimize specialist advice. For example, advice can be provided based on past medical history and developmental data. Furthermore, generative AI can be used to optimize specialist advice by comparing it with past data. For example, the optimal treatment method can be suggested based on past treatment results. In this way, specialist advice can be optimized by comparing it with past data, making it possible to provide more accurate advice.

[0040] Specialist advice can be provided in real time via video calls or chat. Specialist advice can be provided in real time via video calls or chat. For example, a system could be developed using generative AI to provide specialist advice in real time via video calls or chat. For example, a function could be provided that allows direct consultation with a specialist in an emergency. In addition, a system could be developed in which generative AI responds immediately to parents' questions in order to provide specialist advice in real time via video calls or chat. Furthermore, a system could be developed using generative AI to provide specialist advice in real time via video calls or chat. For example, a function could be provided to perform regular health checks online. This would allow specialist advice to be provided in real time via video calls or chat, enabling a quick response even in emergencies.

[0041] It is possible to provide specialist advice in collaboration with doctors of different specialties. Providing specialist advice in collaboration with doctors of different specialties. For example, a system could be developed using generative AI to provide specialist advice in collaboration with doctors of different specialties. For example, pediatricians and nutritionists could jointly provide advice. In addition, a system could be developed in which generative AI integrates data from each specialty in order to provide specialist advice in collaboration with doctors of different specialties. Furthermore, a system could be developed using generative AI to provide specialist advice in collaboration with doctors of different specialties. For example, collaborating with psychologists to support the mental health of parents. This would enable more comprehensive medical support by collaborating with doctors of different specialties to provide specialist advice.

[0042] It is possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities. It is possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, using generative AI, we will develop a system that can monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, we will monitor body temperature and heart rate. We will also develop an algorithm that uses generative AI to detect abnormalities, even to monitor a baby's health in real time. For example, we will analyze breathing patterns. We will also use generative AI to develop a system that can monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, we will monitor movements while sleeping. This will make it possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities, enabling rapid response.

[0043] Data can be stored in the cloud and made accessible from anywhere. Data can be stored in the cloud and made accessible from anywhere. For example, we will use generative AI to develop a system that stores baby data in the cloud and makes it accessible from anywhere. For example, data can be checked from a smartphone. We will also develop a system that uses generative AI to ensure data security, so that baby data can be stored in the cloud and made accessible from anywhere. We will also use generative AI to develop a system that stores baby data in the cloud and makes it accessible from anywhere. For example, the whole family can share the data. This will allow baby data to be stored in the cloud and made accessible from anywhere, so that you can keep track of your baby's condition even from a distance.

[0044] Data can be shared even from a distance, allowing the whole family to participate in childcare. Data can be shared even from a distance, allowing the whole family to participate in childcare. For example, using generative AI, we will develop a system that allows baby data to be shared even from a distance, allowing the whole family to participate in childcare. For example, data can be shared via a smartphone app. Also, to share baby data and allow the whole family to participate in childcare, we will develop a system in which generative AI manages data access rights. Furthermore, using generative AI, we will develop a system that allows baby data to be shared even from a distance, allowing the whole family to participate in childcare. For example, data can be updated in real time. This will allow baby data to be shared even from a distance, allowing the whole family to participate in childcare, thereby distributing the burden of childcare.

[0045] Based on the data, it is possible to link with experts who provide remote childcare support. Based on the data, it is possible to link with experts who provide remote childcare support. For example, using generative AI, a system can be developed that links with experts who provide remote childcare support based on baby data. For example, providing online consultations. Furthermore, in order to link with experts who provide remote childcare support based on baby data, a system can be developed in which generative AI provides the results of data analysis to the experts. Furthermore, using generative AI, a system can be developed that links with experts who provide remote childcare support based on baby data. For example, regular health checks can be conducted online. This will enable more specialized support by linking with experts who provide remote childcare support based on baby data.

[0046] Data can be analyzed to identify common problems and solutions. Data can be analyzed to identify common problems and solutions. For example, a system could be developed using generative AI to analyze the data of all users and identify common problems and solutions. For example, data could be compared between babies of the same age. A system could also be developed that collects the data of all users and uses generative AI to analyze it to identify common problems and solutions. For example, sleep patterns and meal timings could be compared. Furthermore, a system could be developed using generative AI to analyze the data of all users and identify common problems and solutions. For example, health conditions and developmental status could be compared. This would enable more effective parenting support by analyzing the data of all users and identifying common problems and solutions.

[0047] Future trends and risks can be predicted based on data. Future trends and risks can be predicted based on data. For example, a system could be developed using generative AI to predict future trends and risks based on the data of all users. For example, predicting health risks and developmental trends. A system could also be developed that collects data from all users, analyzes it using generative AI, and predicts future trends and risks. For example, predicting the risk of allergies. A system could also be developed using generative AI to predict future trends and risks based on the data of all users. For example, predicting the risk of obesity and diabetes. This would enable more effective support for child-rearing by predicting future trends and risks based on the data of all users.

[0048] It is possible to compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method. Compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method. For example, we will use generative AI to develop a system that compares child-rearing methods from different regions and cultural spheres and proposes the most appropriate method. For example, we will provide advice that takes into account the child-rearing customs and traditions of each country. We will also develop a system that collects child-rearing data from different regions and uses generative AI to analyze it and propose the most appropriate child-rearing method. For example, we will compare eating and sleeping habits. We will also use generative AI to develop a system that compares child-rearing methods from different cultural spheres and propose the most appropriate method. For example, we will compare methods of health management and developmental support. This will enable us to compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method, making it possible to respond to diverse cultures.

[0049] It will be possible to propose new research and technologies related to childcare. Propose new research and technologies related to childcare. For example, we will develop a system that uses generative AI to propose new research and technologies related to childcare based on the data of all users. For example, we will provide advice based on the latest childcare research findings. We will also develop a system that collects data from all users, analyzes it using generative AI, and proposes new research and technologies related to childcare. For example, we will introduce new childcare products and techniques. We will also develop a system that uses generative AI to propose new research and technologies related to childcare based on the data of all users. For example, we will propose ways to apply the latest medical technology to childcare. This will make it possible to support childcare that incorporates the latest knowledge by proposing new research and technologies related to childcare.

[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 information providing unit can provide parenting information as visual infographics or animations. For example, the information providing unit can use a generation AI to generate parenting information as visual infographics and provide it in a form that is easy for parents to understand. For example, the baby's developmental stages are shown in diagrams. The information providing unit can also use a generation AI to create animations of parenting information and provide it in a form that is easy for parents to understand visually. For example, childcare steps are explained using animations. The information providing unit can also use a generation AI to provide parenting information as visually easy-to-understand infographics or animations. For example, meal timing and content are shown in diagrams. In this way, providing parenting information as visual infographics or animations makes it easier for parents to understand.

[0052] The information provision unit can collect parenting information from different languages ​​and regions and provide advice from a global perspective. For example, the generation AI can be used to collect parenting information from different languages ​​and regions and provide advice from a global perspective. For example, it can introduce parenting methods in different cultural spheres. The information provision unit can also collect parenting information from different regions and the generation AI can use that information to provide advice from a global perspective. For example, it can provide advice that takes into account the child-rearing customs and traditions of each country. Furthermore, the information provision unit can use the generation AI to collect parenting information from different languages ​​and regions and provide advice to parents from a global perspective. For example, it can introduce eating and sleeping habits in different cultural spheres. This allows the system to respond to diverse cultures by collecting parenting information from different languages ​​and regions and providing advice from a global perspective.

[0053] It is possible to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, generative AI can be used to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, the frequency and rhythm of the crying can be analyzed. Alternatively, data on a baby's crying can be collected and an algorithm developed using generative AI to analyze it and identify the cause of the crying. For example, the volume and duration of the crying can be analyzed. Furthermore, generative AI can be used to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, the tone and pitch of the crying can be analyzed. This makes it possible to analyze a baby's crying patterns and identify the cause of the crying, allowing for appropriate responses.

[0054] A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. For example, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, smiling and crying facial expressions can be analyzed. Data on a baby's movements and facial expressions can also be collected, and generative AI can analyze it in real time to estimate its health and emotional state. For example, it can analyze limb movements and facial expressions. Generative AI can also be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, it can analyze movement patterns and changes in facial expressions. This allows a baby's movements and facial expressions to be analyzed in real time to estimate its health and emotional state, enabling appropriate care.

[0055] Based on the data, it is possible to predict future health risks and suggest preventive measures. For example, generative AI can be used to predict future health risks based on a baby's data and suggest preventive measures. For example, advice on diet and exercise can be provided. In addition, baby data can be collected and generative AI can analyze it to predict future health risks and suggest preventive measures. For example, it can predict the risk of allergies. Furthermore, generative AI can be used to predict future health risks based on the baby's data and suggest preventive measures. For example, it can predict the risk of obesity and diabetes. This makes it possible to predict future health risks based on the baby's data and suggest preventive measures, making baby health management more effective.

[0056] The data can be compared with data from other families to identify common problems and solutions. For example, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, data from babies of the same age can be compared. Alternatively, baby data can be collected and generative AI can compare it with data from other families to identify common problems and solutions. For example, sleep patterns and meal timings can be compared. Generative AI can also be used to compare a baby's data with data from other families to identify common problems and solutions. For example, health and developmental status can be compared. This allows for more effective parenting support by comparing a baby's data with data from other families to identify common problems and solutions.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information department provides information on child-rearing, such as appropriate care methods for each stage of a baby's development, disease prevention methods, and meal timing and content. Step 2: The data storage unit stores data about the baby, such as the intonation and nuances of the baby's crying, the amount of sleep and its quality, meal times, and when to change the baby's diaper. Step 3: The analysis unit uses the generation AI to analyze the data accumulated by the data accumulation unit. For example, the generation AI analyzes the baby's condition based on data on the baby's crying and movements and generates appropriate suggestions. Step 4: The advice provider provides advice based on the results of the analysis by the analysis unit. For example, the generative AI suggests appropriate care methods and disease prevention methods based on the baby's health and developmental status.

[0059] (Example 2) The child-rearing support system according to an embodiment of the present invention provides a comprehensive range of information, from general information about child-rearing to actual events, advice from specialists, and baby management. This eliminates the need to rely on various reference books and online information, allowing users to receive optimal child-rearing support for their own children. It also makes it possible to supplement baby management and child-rearing knowledge from remote locations, allowing users to participate in child-rearing while working.

[0060] A child-rearing support system according to an embodiment includes an information providing unit, a data storage unit, an analysis unit, and an advice providing unit. The information providing unit provides information about child-rearing. For example, it provides appropriate care methods according to the baby's developmental stage, disease prevention methods, meal timing and content, etc. The data storage unit stores baby data. For example, it stores data such as the intonation and nuance of the baby's crying, sleep duration and quality, meal times, and diaper change timing. The analysis unit uses a generation AI to analyze the data stored by the data storage unit. For example, the generation AI analyzes the baby's condition based on data on the baby's crying and movements and generates appropriate suggestions. The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the generation AI suggests appropriate care methods and disease prevention methods according to the baby's health condition and developmental status. As a result, the child-rearing support system according to an embodiment provides information about child-rearing in one place, accumulates and analyzes baby data, and provides appropriate advice, thereby alleviating the anxiety of couples who have no experience raising children.

[0061] The information provision unit can provide child-rearing information based on the family's culture and values. For example, the information provision unit uses a generation AI to provide child-rearing information based on the family's culture and values. For example, it may suggest child-rearing methods that take into account religious background and family traditions. In addition, in order to customize child-rearing information according to the family's values, the generation AI learns the family's history and the parents' child-rearing policies and provides advice based on that. Furthermore, the information provision unit uses the generation AI to understand the family's cultural background and provide appropriate child-rearing information based on that. For example, it may provide advice that takes into account eating and sleeping habits in a particular culture. This allows the provision of child-rearing information based on the family's culture and values, making it possible to provide advice that is optimal for each individual family.

[0062] The information provision unit can perform simulations based on past successes and failures and provide specific advice. For example, the generation AI learns from past successes and failures and performs simulations based on them to provide specific parenting advice. For example, it suggests the optimal way to respond in a specific situation. The information provision unit also performs simulations based on past child-rearing data and provides specific advice to parents. For example, it derives solutions to specific problems from the simulation results. Furthermore, the information provision unit has the generation AI analyze past cases and provide specific parenting advice to parents through simulations. For example, it predicts the results of specific actions and suggests the optimal response. In this way, specific advice can be provided by performing simulations based on past successes and failures.

[0063] The information providing unit can analyze the parent's stress level and provide parenting information to reduce stress. The information providing unit, for example, uses an emotion estimation function to analyze the parent's stress level in real time and provide specific parenting information to reduce stress. For example, it provides advice on relaxation methods and stress management. The information providing unit also measures the parent's stress level using the emotion estimation function and provides parenting information to reduce stress based on the results. For example, it suggests parenting techniques that are useful when stress is high. Furthermore, the information providing unit uses the emotion estimation function to analyze the parent's stress level and provide specific advice to reduce stress. For example, it suggests relaxation methods that are effective when stress is high. In this way, the burden on parents can be reduced by analyzing the parent's stress level and providing parenting information to reduce stress.

[0064] The information providing unit can provide parenting information as visual infographics or animations. For example, the information providing unit uses a generation AI to generate parenting information as visual infographics and provide it in a form that is easy for parents to understand. For example, it shows a baby's developmental stages in diagrams. The information providing unit also uses a generation AI to create parenting information as animations and provide it in a form that is visually easy for parents to understand. For example, it explains child-rearing steps in animations. Furthermore, the information providing unit uses a generation AI to provide parenting information as visually easy-to-understand infographics or animations. For example, it shows the timing and content of meals in diagrams. In this way, providing parenting information as visual infographics or animations makes it easier for parents to understand.

[0065] The information provision unit can collect parenting information in different languages ​​and regions and provide advice from a global perspective. For example, the information provision unit uses a generation AI to collect parenting information in different languages ​​and regions and provide advice from a global perspective. For example, it introduces child-rearing methods in different cultural spheres. The information provision unit also collects parenting information in different regions and the generation AI uses that information to provide advice from a global perspective. For example, it provides advice that takes into account the child-rearing customs and traditions of each country. Furthermore, the information provision unit uses the generation AI to collect parenting information in different languages ​​and regions and provide advice to parents from a global perspective. For example, it introduces eating and sleeping habits in different cultural spheres. This makes it possible to respond to diverse cultures by collecting parenting information in different languages ​​and regions and providing advice from a global perspective.

[0066] The information providing unit can provide encouraging or comforting messages according to the parent's emotional state. For example, the information providing unit uses an emotion estimation function to analyze the parent's emotional state in real time and provide encouraging or comforting messages using the generation AI. For example, an encouraging message is sent when the parent is under high stress. The information providing unit also measures the parent's emotional state using the emotion estimation function and provides encouraging or comforting messages using the generation AI based on the results. For example, it suggests relaxing when the parent is tired. Furthermore, the information providing unit uses the emotion estimation function to analyze the parent's emotional state and provides encouraging or comforting messages using the generation AI. For example, it sends words of encouragement when the parent is feeling down. In this way, encouraging or comforting messages according to the parent's emotional state can be provided, providing psychological support to the parent.

[0067] It is possible to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, using generative AI, we can develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, by analyzing the frequency and rhythm of the crying. We can also collect baby crying data and develop an algorithm that uses generative AI to analyze it and identify the cause of the crying. For example, by analyzing the volume and duration of the crying. We can also use generative AI to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, by analyzing the tone and pitch of the crying. This makes it possible to analyze a baby's crying patterns and identify the cause of the crying, allowing for appropriate responses.

[0068] A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. For example, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, smiling and crying facial expressions can be analyzed. Data on a baby's movements and facial expressions can also be collected, and generative AI can analyze it in real time to estimate its health and emotional state. For example, it can analyze limb movements and facial expressions. Furthermore, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, it can analyze movement patterns and changes in facial expressions. This allows a baby's movements and facial expressions to be analyzed in real time to estimate its health and emotional state, enabling appropriate care.

[0069] It is possible to analyze a baby's emotional state and suggest appropriate ways of responding to the parent. Analyze a baby's emotional state and suggest appropriate ways of responding to the parent. For example, the emotion estimation function can be used to analyze a baby's emotional state in real time and suggest appropriate ways of responding to the parent. For example, it can suggest ways to hold the baby when it is crying. The emotion estimation function can also be used to measure a baby's emotional state and suggest appropriate ways of responding to the parent based on the results. For example, it can suggest ways to play when the baby is smiling. Furthermore, the emotion estimation function can be used to analyze a baby's emotional state and suggest appropriate ways of responding to the parent. For example, it can suggest ways to calm the baby when it is angry. In this way, by analyzing a baby's emotional state and suggesting appropriate ways of responding to the parent, baby care can be made more effective.

[0070] Based on the data, it is possible to predict future health risks and suggest preventive measures. Based on the data, it is possible to predict future health risks and suggest preventive measures. For example, generative AI can be used to predict future health risks based on a baby's data and suggest preventive measures. For example, advice on diet and exercise can be provided. In addition, baby data can be collected and generative AI can analyze it to predict future health risks and suggest preventive measures. For example, it can predict the risk of allergies. Furthermore, generative AI can be used to predict future health risks based on the baby's data and suggest preventive measures. For example, it can predict the risk of obesity and diabetes. This makes it possible to predict future health risks based on the baby's data and suggest preventive measures, making baby health management more effective.

[0071] The data can be compared with data from other families to identify common problems and solutions. The data can be compared with data from other families to identify common problems and solutions. For example, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, data from babies of the same age can be compared. Alternatively, baby data can be collected and generative AI can compare it with data from other families to identify common problems and solutions. For example, sleep patterns and meal timings can be compared. Furthermore, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, health and developmental status can be compared. This allows for more effective parenting support by comparing a baby's data with data from other families to identify common problems and solutions.

[0072] It is possible to provide music and videos that match a baby's emotional state to help relax the baby. It is possible to provide music and videos that match a baby's emotional state to help relax the baby. For example, using the emotion estimation function, a baby's emotional state is analyzed in real time, and the generation AI provides music and videos to help relax the baby. For example, calming music can be played when the baby is crying. The emotion estimation function can also be used to measure a baby's emotional state, and the generation AI can provide music and videos to help relax the baby based on the results, for example, playing videos that induce sleep. Furthermore, the emotion estimation function can be used to analyze a baby's emotional state, and the generation AI can provide music and videos to help relax the baby. For example, cheerful videos can be played when the baby is smiling. In this way, music and videos that match a baby's emotional state can be provided to help relax the baby, reducing stress.

[0073] Specialist advice can be customized to suit the family's situation. Specialist advice can be customized to suit the family's situation. For example, generative AI can be used to customize specialist advice to suit the individual family's situation. For example, advice can be provided that takes into account the family's daily routine and the parents' child-rearing preferences. In addition, to customize specialist advice to suit the family's situation, generative AI learns from family data and provides advice based on that. Furthermore, generative AI can understand the family's situation and customize the specialist advice based on that. For example, advice can be provided that is tailored to specific health or developmental conditions. This allows specialist advice to be customized to suit the individual family's situation, making it possible to provide more effective advice.

[0074] Specialist advice can be optimized by comparing it with past data. Specialist advice can be optimized by comparing it with past data. For example, generative AI can be used to optimize specialist advice by comparing it with past data. For example, the optimal care method can be suggested based on past health data. Past data can also be collected and analyzed by generative AI to optimize specialist advice. For example, advice can be provided based on past medical history and developmental data. Furthermore, generative AI can be used to optimize specialist advice by comparing it with past data. For example, the optimal treatment method can be suggested based on past treatment results. In this way, specialist advice can be optimized by comparing it with past data, making it possible to provide more accurate advice.

[0075] It is possible to provide specialist medical advice according to the emotional state. It is possible to provide specialist medical advice according to the emotional state. For example, the emotion estimation function can be used to analyze the parent's emotional state in real time and provide specialist medical advice. For example, relaxation methods can be suggested when stress is high. The emotion estimation function can also be used to measure the parent's emotional state and provide specialist medical advice based on the results. For example, a suggestion to rest when the parent is tired. The emotion estimation function can also be used to analyze the parent's emotional state and provide specialist medical advice. For example, words of encouragement can be sent when the parent is feeling down. In this way, specialist medical advice according to the parent's emotional state can be provided, providing psychological support to the parent.

[0076] Specialist advice can be provided in real time via video calls or chat. Specialist advice can be provided in real time via video calls or chat. For example, a system could be developed using generative AI to provide specialist advice in real time via video calls or chat. For example, a function could be provided that allows direct consultation with a specialist in an emergency. In addition, a system could be developed in which generative AI responds immediately to parents' questions in order to provide specialist advice in real time via video calls or chat. Furthermore, a system could be developed using generative AI to provide specialist advice in real time via video calls or chat. For example, a function could be provided to perform regular health checks online. This would allow specialist advice to be provided in real time via video calls or chat, enabling a quick response even in emergencies.

[0077] It is possible to provide specialist advice in collaboration with doctors of different specialties. Providing specialist advice in collaboration with doctors of different specialties. For example, a system could be developed using generative AI to provide specialist advice in collaboration with doctors of different specialties. For example, pediatricians and nutritionists could jointly provide advice. In addition, a system could be developed in which generative AI integrates data from each specialty in order to provide specialist advice in collaboration with doctors of different specialties. Furthermore, a system could be developed using generative AI to provide specialist advice in collaboration with doctors of different specialties. For example, collaborating with psychologists to support the mental health of parents. This would enable more comprehensive medical support by collaborating with doctors of different specialties to provide specialist advice.

[0078] It is possible to provide priority advice from specialists based on the emotional state. Priority advice from specialists based on the emotional state. For example, the emotion estimation function can be used to analyze the parent's emotional state in real time, and priority advice from specialists can be provided. For example, relaxation methods can be suggested when stress is high. The emotion estimation function can also measure the parent's emotional state, and priority advice from specialists can be provided based on the results. For example, a recommendation to rest can be made when the parent is tired. Furthermore, the emotion estimation function can be used to analyze the parent's emotional state, and priority advice from specialists can be provided. For example, words of encouragement can be sent when the parent is feeling down. In this way, priority advice from specialists based on the parent's emotional state can be provided, allowing parents to receive prompt psychological support.

[0079] It is possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities. It is possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, using generative AI, we will develop a system that can monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, we will monitor body temperature and heart rate. We will also develop an algorithm that uses generative AI to detect abnormalities, even to monitor a baby's health in real time. For example, we will analyze breathing patterns. We will also use generative AI to develop a system that can monitor a baby's health in real time, even from a remote location, and detect abnormalities. For example, we will monitor movements while sleeping. This will make it possible to monitor a baby's health in real time, even from a remote location, and detect abnormalities, enabling rapid response.

[0080] Data can be stored in the cloud and made accessible from anywhere. Data can be stored in the cloud and made accessible from anywhere. For example, we will use generative AI to develop a system that stores baby data in the cloud and makes it accessible from anywhere. For example, data can be checked from a smartphone. We will also develop a system that uses generative AI to ensure data security, so that baby data can be stored in the cloud and made accessible from anywhere. We will also use generative AI to develop a system that stores baby data in the cloud and makes it accessible from anywhere. For example, the whole family can share the data. This will allow baby data to be stored in the cloud and made accessible from anywhere, so that you can keep track of your baby's condition even from a distance.

[0081] It is possible to make remote care suggestions based on the emotional state. Remote care suggestions based on the emotional state can be made. For example, the emotion estimation function can be used to analyze the parent's emotional state in real time and make remote care suggestions. For example, relaxation methods can be suggested when stress is high. The emotion estimation function can also measure the parent's emotional state and make remote care suggestions based on the results. For example, a rest suggestion can be made when the parent is tired. The emotion estimation function can also be used to analyze the parent's emotional state and make remote care suggestions. For example, words of encouragement can be sent when the parent is feeling down. In this way, remote care suggestions based on the parent's emotional state can be made, providing psychological support to the parent.

[0082] Data can be shared even from a distance, allowing the whole family to participate in childcare. Data can be shared even from a distance, allowing the whole family to participate in childcare. For example, using generative AI, we will develop a system that allows baby data to be shared even from a distance, allowing the whole family to participate in childcare. For example, data can be shared via a smartphone app. Also, to share baby data and allow the whole family to participate in childcare, we will develop a system in which generative AI manages data access rights. Furthermore, using generative AI, we will develop a system that allows baby data to be shared even from a distance, allowing the whole family to participate in childcare. For example, data can be updated in real time. This will allow baby data to be shared even from a distance, allowing the whole family to participate in childcare, thereby distributing the burden of childcare.

[0083] Based on the data, it is possible to link with experts who provide remote childcare support. Based on the data, it is possible to link with experts who provide remote childcare support. For example, using generative AI, a system can be developed that links with experts who provide remote childcare support based on baby data. For example, providing online consultations. Furthermore, in order to link with experts who provide remote childcare support based on baby data, a system can be developed in which generative AI provides the results of data analysis to the experts. Furthermore, using generative AI, a system can be developed that links with experts who provide remote childcare support based on baby data. For example, regular health checks can be conducted online. This will enable more specialized support by linking with experts who provide remote childcare support based on baby data.

[0084] It is possible to provide remote care advice according to the emotional state. It is possible to provide remote care advice according to the emotional state. For example, the emotion estimation function can be used to analyze the parent's emotional state in real time and provide remote care advice. For example, relaxation methods can be suggested when stress is high. The emotion estimation function can also measure the parent's emotional state and provide remote care advice based on the results. For example, a rest can be suggested when the parent is tired. The emotion estimation function can also be used to analyze the parent's emotional state and provide remote care advice. For example, words of encouragement can be sent when the parent is feeling down. In this way, remote care advice according to the parent's emotional state can be provided, providing psychological support to the parent.

[0085] Data can be analyzed to identify common problems and solutions. Data can be analyzed to identify common problems and solutions. For example, a system could be developed using generative AI to analyze the data of all users and identify common problems and solutions. For example, data could be compared between babies of the same age. A system could also be developed that collects the data of all users and uses generative AI to analyze it to identify common problems and solutions. For example, sleep patterns and meal timings could be compared. Furthermore, a system could be developed using generative AI to analyze the data of all users and identify common problems and solutions. For example, health conditions and developmental status could be compared. This would enable more effective parenting support by analyzing the data of all users and identifying common problems and solutions.

[0086] Future trends and risks can be predicted based on data. Future trends and risks can be predicted based on data. For example, a system could be developed using generative AI to predict future trends and risks based on the data of all users. For example, predicting health risks and developmental trends. A system could also be developed that collects data from all users, analyzes it using generative AI, and predicts future trends and risks. For example, predicting the risk of allergies. A system could also be developed using generative AI to predict future trends and risks based on the data of all users. For example, predicting the risk of obesity and diabetes. This would enable more effective support for child-rearing by predicting future trends and risks based on the data of all users.

[0087] It is possible to analyze emotional states and provide advice that elicits empathy. It is possible to analyze emotional states and provide advice that elicits empathy. For example, the emotion estimation function can be used to analyze the emotional states of all users in real time and provide advice that elicits empathy. For example, it can suggest ways to relax when stress is high. The emotion estimation function can also measure the emotional states of all users and provide advice that elicits empathy based on the results. For example, it can suggest taking a rest when you are tired. Furthermore, the emotion estimation function can be used to analyze the emotional states of all users and provide advice that elicits empathy. For example, it can send words of encouragement when you are feeling down. In this way, parents can be provided with psychological support by analyzing the emotional states of all users and providing advice that elicits empathy.

[0088] It is possible to compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method. Compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method. For example, we will use generative AI to develop a system that compares child-rearing methods from different regions and cultural spheres and proposes the most appropriate method. For example, we will provide advice that takes into account the child-rearing customs and traditions of each country. We will also develop a system that collects child-rearing data from different regions and uses generative AI to analyze it and propose the most appropriate child-rearing method. For example, we will compare eating and sleeping habits. We will also use generative AI to develop a system that compares child-rearing methods from different cultural spheres and propose the most appropriate method. For example, we will compare methods of health management and developmental support. This will enable us to compare child-rearing methods from different regions and cultural spheres and propose the most appropriate method, making it possible to respond to diverse cultures.

[0089] It will be possible to propose new research and technologies related to childcare. Propose new research and technologies related to childcare. For example, we will develop a system that uses generative AI to propose new research and technologies related to childcare based on the data of all users. For example, we will provide advice based on the latest childcare research findings. We will also develop a system that collects data from all users, analyzes it using generative AI, and proposes new research and technologies related to childcare. For example, we will introduce new childcare products and techniques. We will also develop a system that uses generative AI to propose new research and technologies related to childcare based on the data of all users. For example, we will propose ways to apply the latest medical technology to childcare. This will make it possible to support childcare that incorporates the latest knowledge by proposing new research and technologies related to childcare.

[0090] It is possible to provide parenting advice according to the emotional state. Parenting advice according to the emotional state is provided. For example, the emotion estimation function is used to analyze the emotional state of all users in real time and provide parenting advice according to their emotional state. For example, relaxation methods are suggested when stress is high. The emotion estimation function can also measure the emotional state of all users and provide parenting advice based on the results. For example, a rest is suggested when tired. Furthermore, the emotion estimation function can be used to analyze the emotional state of all users and provide parenting advice according to their emotional state. For example, words of encouragement are sent when users are feeling down. In this way, parenting advice according to the emotional state of all users can be provided, providing psychological support to parents.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information provision unit can provide encouraging or comforting messages according to the parent's emotional state. For example, it uses the emotion estimation function to analyze the parent's emotional state in real time, and the generation AI provides encouraging or comforting messages. For example, it sends an encouraging message when the parent is under high stress. The information provision unit also measures the parent's emotional state using the emotion estimation function, and the generation AI provides encouraging or comforting messages based on the results. For example, it suggests relaxing when the parent is tired. Furthermore, the information provision unit uses the emotion estimation function to analyze the parent's emotional state, and the generation AI provides encouraging or comforting messages. For example, it sends words of encouragement when the parent is feeling down. In this way, encouraging or comforting messages according to the parent's emotional state can be provided, providing psychological support to the parent.

[0093] The information providing unit can provide parenting information as visual infographics or animations. For example, the information providing unit can use a generation AI to generate parenting information as visual infographics and provide it in a form that is easy for parents to understand. For example, the baby's developmental stages are shown in diagrams. The information providing unit can also use a generation AI to create animations of parenting information and provide it in a form that is easy for parents to understand visually. For example, childcare steps are explained using animations. The information providing unit can also use a generation AI to provide parenting information as visually easy-to-understand infographics or animations. For example, meal timing and content are shown in diagrams. In this way, providing parenting information as visual infographics or animations makes it easier for parents to understand.

[0094] The information provision unit can collect parenting information from different languages ​​and regions and provide advice from a global perspective. For example, the generation AI can be used to collect parenting information from different languages ​​and regions and provide advice from a global perspective. For example, it can introduce parenting methods in different cultural spheres. The information provision unit can also collect parenting information from different regions and the generation AI can use that information to provide advice from a global perspective. For example, it can provide advice that takes into account the child-rearing customs and traditions of each country. Furthermore, the information provision unit can use the generation AI to collect parenting information from different languages ​​and regions and provide advice to parents from a global perspective. For example, it can introduce eating and sleeping habits in different cultural spheres. This allows the system to respond to diverse cultures by collecting parenting information from different languages ​​and regions and providing advice from a global perspective.

[0095] The information providing unit can analyze the parent's stress level and provide parenting information to reduce stress. For example, it uses the emotion estimation function to analyze the parent's stress level in real time and provide specific parenting information to reduce stress. For example, it provides advice on relaxation methods and stress management. The information providing unit can also measure the parent's stress level using the emotion estimation function and provide parenting information to reduce stress based on the results. For example, it can suggest parenting techniques that are useful when stress is high. Furthermore, the information providing unit can analyze the parent's stress level using the emotion estimation function and provide specific advice to reduce stress. For example, it can suggest relaxation methods that are effective when stress is high. In this way, the burden on parents can be reduced by analyzing the parent's stress level and providing parenting information to reduce stress.

[0096] It is possible to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, generative AI can be used to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, the frequency and rhythm of the crying can be analyzed. Alternatively, data on a baby's crying can be collected and an algorithm developed using generative AI to analyze it and identify the cause of the crying. For example, the volume and duration of the crying can be analyzed. Furthermore, generative AI can be used to develop an algorithm that analyzes a baby's crying patterns and identifies the cause of the crying. For example, the tone and pitch of the crying can be analyzed. This makes it possible to analyze a baby's crying patterns and identify the cause of the crying, allowing for appropriate responses.

[0097] A baby's movements and facial expressions can be analyzed in real time to estimate its health and emotional state. For example, generative AI can be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, smiling and crying facial expressions can be analyzed. Data on a baby's movements and facial expressions can also be collected, and generative AI can analyze it in real time to estimate its health and emotional state. For example, it can analyze limb movements and facial expressions. Generative AI can also be used to analyze a baby's movements and facial expressions in real time to estimate its health and emotional state. For example, it can analyze movement patterns and changes in facial expressions. This allows a baby's movements and facial expressions to be analyzed in real time to estimate its health and emotional state, enabling appropriate care.

[0098] It is possible to provide music and videos that match a baby's emotional state to help relax the baby. For example, the emotion estimation function can be used to analyze a baby's emotional state in real time, and the generation AI can provide music and videos to relax the baby. For example, calming music can be played when the baby is crying. The emotion estimation function can also be used to measure a baby's emotional state, and the generation AI can provide music and videos to relax the baby based on the results. For example, videos that induce sleep can be played. Furthermore, the emotion estimation function can be used to analyze a baby's emotional state, and the generation AI can provide music and videos to relax the baby. For example, fun videos can be played when the baby is smiling. In this way, music and videos that match a baby's emotional state can be provided to relax the baby, reducing stress.

[0099] Based on the data, it is possible to predict future health risks and suggest preventive measures. For example, generative AI can be used to predict future health risks based on a baby's data and suggest preventive measures. For example, advice on diet and exercise can be provided. In addition, baby data can be collected and generative AI can analyze it to predict future health risks and suggest preventive measures. For example, it can predict the risk of allergies. Furthermore, generative AI can be used to predict future health risks based on the baby's data and suggest preventive measures. For example, it can predict the risk of obesity and diabetes. This makes it possible to predict future health risks based on the baby's data and suggest preventive measures, making baby health management more effective.

[0100] The data can be compared with data from other families to identify common problems and solutions. For example, generative AI can be used to compare a baby's data with data from other families to identify common problems and solutions. For example, data from babies of the same age can be compared. Alternatively, baby data can be collected and generative AI can compare it with data from other families to identify common problems and solutions. For example, sleep patterns and meal timings can be compared. Generative AI can also be used to compare a baby's data with data from other families to identify common problems and solutions. For example, health and developmental status can be compared. This allows for more effective parenting support by comparing a baby's data with data from other families to identify common problems and solutions.

[0101] It is possible to make remote care suggestions based on the parent's emotional state. For example, the emotion estimation function can be used to analyze the parent's emotional state in real time and make remote care suggestions. For example, relaxation methods can be suggested when stress levels are high. The emotion estimation function can also measure the parent's emotional state and make remote care suggestions based on the results. For example, a rest suggestion can be made when the parent is tired. The emotion estimation function can also be used to analyze the parent's emotional state and make remote care suggestions. For example, words of encouragement can be sent when the parent is feeling down. In this way, remote care suggestions based on the parent's emotional state can be made, providing psychological support to the parent.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The information department provides information on child-rearing, such as appropriate care methods for each stage of a baby's development, disease prevention methods, and meal timing and content. Step 2: The data storage unit stores data about the baby, such as the intonation and nuances of the baby's crying, the amount of sleep and its quality, meal times, and when to change the baby's diaper. Step 3: The analysis unit uses the generation AI to analyze the data accumulated by the data accumulation unit. For example, the generation AI analyzes the baby's condition based on data on the baby's crying and movements and generates appropriate suggestions. Step 4: The advice provider provides advice based on the results of the analysis by the analysis unit. For example, the generative AI suggests appropriate care methods and disease prevention methods based on the baby's health and developmental status.

[0104] 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.

[0105] 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.

[0106] 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.

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] 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).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. An information department that provides information on child-rearing; a data storage unit that stores data on the baby; an analysis unit that analyzes the data accumulated by the data accumulation unit; an advice providing unit that provides advice based on the results of the analysis by the analysis unit; A system characterized by:

2. The information providing unit Providing parenting information based on the family's culture and values 2. The system of claim 1.

3. The information providing unit We conduct simulations based on past successes and failures and provide specific advice 2. The system of claim 1.

4. The information providing unit Analyzes parental stress levels and provides parenting information to reduce stress 2. The system of claim 1.

5. The information providing unit Presenting parenting information as visual infographics and animations 2. The system of claim 1.

6. The information providing unit Gathering parenting information from different languages ​​and regions to provide advice from a global perspective 2. The system of claim 1.

7. The information providing unit Offer encouraging and comforting messages that respond to the parent's emotional state 2. The system of claim 1.

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