System

Abstract

An object of a system according to an embodiment is to provide information and advice related to childbirth and childcare based on individual attributes of parents.SOLUTION: A system includes an attribute data collection unit, an information providing unit, and an advice providing unit. The attribute data collection unit collects attribute data of the parents. The information providing unit provides information on childbirth and childcare based on the attribute data of the parents collected by the attribute data collecting unit. The advice providing unit provides individual advice based on the information provided by the information providing unit and the attribute data of the parents.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] Conventional technologies do not adequately provide information and advice on childbirth and child-rearing based on the individual attributes of parents, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information and advice on childbirth and child-rearing based on the individual attributes of parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an attribute data collection unit, an information provision unit, and an advice provision unit. The attribute data collection unit collects attribute data of parents. The information provision unit provides information about childbirth and child-rearing based on the attribute data of parents collected by the attribute data collection unit. The advice provision unit provides individual advice based on the information provided by the information provision unit and the attribute data of parents. [Effects of the Invention]

[0007] The system according to the embodiment can provide information and advice on childbirth and child-rearing based on the individual attributes of parents. [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 support system according to an embodiment of the present invention collects attribute data of parents, analyzes it using a generation AI, provides information on childbirth and child-rearing, and gives individualized advice. This allows the support system to provide optimal advice based on the attributes of parents and support their anxieties and questions about childbirth and child-rearing.

[0029] The support system according to the embodiment includes an attribute data collection unit, an information provision unit, and an advice provision unit. The attribute data collection unit collects attribute data of parents, such as age, gender, occupation, residential area, family structure, and health status. The attribute data collection unit also collects data tailored to individual circumstances, such as whether both parents are working or whether this is their first child. The information provision unit provides information about childbirth and childcare based on the attribute data of parents collected by the attribute data collection unit. For example, it provides information on health management during pregnancy, preparation for childbirth, and basic knowledge and methods of childcare. Specifically, it provides information such as "dietary precautions to take during pregnancy" and "sleeping patterns of newborns." The advice provision unit provides personalized advice based on the information provided by the information provision unit and the attribute data of parents. For example, if parents are facing a specific problem, it proposes a specific solution to the problem. Specifically, it provides advice tailored to the individual situation, such as "what to do if your baby cries at night" or "advice if breastfeeding is not going well." As a result, the support system according to the embodiment can provide optimal advice based on the attributes of the parents and support them in dealing with their anxieties and questions regarding childbirth and child-rearing.

[0030] The attribute data collection unit can collect lifestyle habit data of parents from wearable devices and analyze it using AI. For example, the attribute data collection unit uses wearable devices to monitor the sleep patterns of parents and collect data. For example, it analyzes the quality and duration of sleep to understand the health status of parents. The attribute data collection unit also uses smartwatches and fitness trackers to record the dietary habits of parents. For example, it analyzes meal timing and nutrient intake to help manage health. The attribute data collection unit also analyzes exercise data collected from wearable devices to understand the activity level of parents. For example, it monitors the number of steps taken and calories burned to help improve lifestyle habits. In this way, it is possible to collect lifestyle habit data of parents and provide advice useful for health management.

[0031] The attribute data collection unit can analyze parents' social media activities and collect attribute data reflecting their interests and concerns. For example, the attribute data collection unit analyzes parents' social media accounts and collects the content of their posts and responses such as "likes." For example, it analyzes posts about childcare and accounts they follow to identify their interests and concerns. The attribute data collection unit also analyzes the time and frequency of parents' activities on social media to understand their online behavior patterns. For example, it analyzes the tendency to post frequently during certain times of the day to estimate their daily rhythms. The attribute data collection unit also analyzes parents' comments and messages on social media to collect their emotions and opinions. For example, it can identify worries and questions about childcare and use this information to provide individualized advice. This makes it possible to provide advice based on parents' interests and concerns.

[0032] The information providing unit can analyze parents' past search history and browsing history and provide individually customized information. The information providing unit, for example, analyzes parents' past search history and browsing history and provides individually customized information. For example, it prioritizes displaying information related to keywords searched in the past. The information providing unit also identifies themes and topics that parents are interested in based on the browsing history and provides that information. For example, it suggests information related to articles or videos viewed in the past. The information providing unit also analyzes search history and browsing history and provides information based on parents' interests. For example, it provides the latest information and news on a specific topic. This makes it possible to provide customized information based on parents' past behavior.

[0033] The information providing unit can provide specific advice regarding health management based on the health data of the parents. The information providing unit, for example, collects health data of the parents and provides specific advice regarding health management. For example, it gives advice on appropriate diet and exercise based on blood pressure and weight data. The information providing unit also analyzes the health data and provides advice according to the health condition. For example, if blood pressure is high, it suggests reducing salt intake. The information providing unit also provides advice for long-term health management based on the health data. For example, it explains the importance of weight management and regular health checks. In this way, it is possible to provide specific advice based on the health data of the parents.

[0034] The information provision unit can also provide information about childbirth and child-rearing in video and audio formats to make it easier to understand through sight and hearing. For example, the information provision unit provides information about childbirth and child-rearing in video format to make it easier to understand visually. For example, basic child-rearing methods and precautions are explained in video. The information provision unit also provides information in audio format to make it easier to understand through hearing. For example, it provides podcasts and audiobooks related to child-rearing. The information provision unit also provides information in a combination of video and audio formats to utilize both sight and hearing. For example, audio commentary is added to video to make the information easier to understand. This makes it possible to provide information that is easy to understand through sight and hearing.

[0035] The information section provides information on childbirth and child-rearing from different cultural regions and offers advice from a global perspective. For example, the information section collects information on childbirth and child-rearing from different cultural regions and offers advice from a global perspective. For example, it introduces child-rearing customs and traditional methods from each country. The information section also incorporates the opinions of experts and people with child-rearing experience from different cultural regions and offers advice from a global perspective. For example, it provides information in the form of interviews and discussions. The information section also compares childbirth and child-rearing information from different regions and clarifies similarities and differences. For example, it compares child-rearing methods and support systems in each region and suggests the most appropriate method. This makes it possible to provide information on different cultural regions and offer advice from a global perspective.

[0036] The advice providing unit can analyze the past behavioral data of parents and provide specific advice based on their behavioral patterns. The advice providing unit, for example, collects the past behavioral data of parents and analyzes their behavioral patterns. For example, the behavioral patterns are identified based on childcare diaries and app usage history. The advice providing unit also provides specific advice based on the behavioral patterns. For example, if the baby cries frequently at night, it suggests ways to review the nighttime routine. The advice providing unit also provides advice for creating a long-term childcare plan based on the past behavioral data. For example, it suggests schedules for regular health checks and vaccinations. This makes it possible to provide specific advice based on the past behavioral data of parents.

[0037] The advice providing unit can monitor the health data of parents in real time and provide advice according to their health condition. The advice providing unit, for example, monitors the health data of parents in real time and provides advice according to their health condition. For example, it provides advice on appropriate exercise and diet based on heart rate and blood pressure data. The advice providing unit also analyzes the health data and provides specific advice according to their health condition. For example, if the stress level is high, it suggests advice on relaxation methods and stress management. The advice providing unit also provides advice for long-term health management based on the health data. For example, it suggests schedules for regular health checks and vaccinations. This makes it possible to monitor the health data of parents in real time and provide advice according to their health condition.

[0038] The advice providing unit can provide advice tailored to the parents' occupations and lifestyles, and support balancing work and childcare. The advice providing unit, for example, collects data on the parents' occupations and lifestyles, and provides advice to support balancing work and childcare. For example, it suggests efficient time management methods to parents who work from home. The advice providing unit also provides specific advice tailored to the parents' occupations and lifestyles. For example, it suggests ways to adjust childcare and work schedules to parents who work shifts. The advice providing unit also suggests stress management and relaxation methods based on the parents' occupations and lifestyles. For example, it suggests ways to refresh in a short amount of time to parents who have busy jobs. In this way, advice tailored to the parents' occupations and lifestyles can be provided, and support can be provided to support balancing work and childcare.

[0039] The advice providing unit can provide advice based on the hobbies and interests of parents and make suggestions for enjoying child-rearing. The advice providing unit, for example, collects data on the hobbies and interests of parents and provides advice for enjoying child-rearing. For example, for parents whose hobby is the outdoors, it suggests outdoor activities that can be enjoyed together with their children. The advice providing unit also suggests ways to reduce the stress of child-rearing based on the hobbies and interests. For example, for parents whose hobby is art, it suggests art activities that can be enjoyed together with their children. The advice providing unit also makes specific suggestions for enjoying child-rearing based on the hobbies and interests of parents. For example, for parents whose hobby is cooking, it suggests cooking recipes that can be enjoyed together with their children. This makes it possible to provide advice based on the hobbies and interests of parents and make suggestions for enjoying child-rearing.

[0040] It is possible to collect data on a child's growth and provide specific support according to their stage of growth. It is possible to collect data on a child's growth and provide specific support according to their stage of growth. For example, advice on appropriate nutrition and exercise can be given based on height and weight data. It is also possible to analyze the growth data and provide specific advice according to their stage of growth. For example, it can suggest how to introduce solid food and how to discipline a child in early childhood. It is also possible to provide advice for long-term growth management based on the growth data. For example, it can suggest schedules for regular health checks and vaccinations. In this way, it is possible to collect data on a child's growth and provide specific support according to their stage of growth.

[0041] The lifestyle data of parents can be analyzed and continuous advice can be provided regarding lifestyle improvement. The lifestyle data of parents can be collected and, for example, continuous advice can be provided regarding lifestyle improvement. For example, sleep patterns and dietary content can be analyzed and healthy lifestyle habits can be suggested. The lifestyle data can also be analyzed and specific advice can be provided regarding lifestyle improvement. For example, if there is a lack of exercise, an appropriate exercise plan can be suggested. The lifestyle data can also be used to provide advice regarding long-term lifestyle improvement. For example, regular health checks and stress management methods can be suggested. In this way, the lifestyle data of parents can be analyzed and continuous advice can be provided regarding lifestyle improvement.

[0042] Continuous support can be provided through different devices. Continuous support can be provided, for example, through a smartphone. For example, real-time information and advice about child-rearing can be provided through a dedicated app. Continuous support can also be provided using a tablet. For example, video and audio content related to child-rearing can be provided on the tablet, conveying information visually and audibly. Continuous support can also be provided through a smart speaker. For example, a voice assistant can be used to provide answers to questions about child-rearing and advice. In this way, continuous support can be provided through different devices.

[0043] Supporting parents' community activities and strengthening ties with the local community. To support parents' community activities, for example, providing information about local childcare support groups and events. For example, sharing information about local childcare circles and workshops. Also, to strengthen ties with the local community, promoting interaction with local experts and people with childcare experience. For example, holding consultation sessions with local childcare counselors and doctors. Also, to support parents' community activities, providing information exchange and support through online forums and chat groups. For example, providing a platform where parents living in the same area can interact with each other. This will support parents' community activities and strengthen ties with the local community.

[0044] Based on the attribute data of parents, it is possible to connect members with common interests. By collecting attribute data of parents, for example, members with common interests can be connected. For example, parents with the same hobbies or special skills can be matched. Furthermore, based on the attribute data, members with common child-rearing experiences or challenges can be connected. For example, parents who are raising a child for the first time can be matched. Furthermore, by analyzing the attribute data of parents, a community can be formed to connect members with common interests. For example, parents who live in the same area can be connected. In this way, it is possible to connect members with common interests based on the attribute data of parents.

[0045] To promote the exchange of information within a community, AI can automatically suggest topics. For example, AI can automatically suggest topics to promote the exchange of information within a community. For example, it can suggest the latest news and trends related to child-rearing. AI can also analyze the interests and concerns of members within a community and suggest topics based on that. For example, it can suggest topics related to topics that interest members. To stimulate the exchange of information within a community, AI can automatically suggest topics and promote discussion. For example, it can suggest topics for questions and exchanges of opinions related to child-rearing. This allows AI to automatically suggest topics to promote the exchange of information within a community.

[0046] Communities can be formed not only online but also through offline events and workshops. Communities can be formed not only online but also through offline events and workshops, for example, by holding local parenting circles and workshops. Also, offline events can be used to promote interaction between parents, for example, by holding seminars and lectures on parenting. Also, communities can be formed both online and offline to exchange information and provide support. For example, by combining discussions in online forums with interactions at offline events. In this way, communities can be formed not only online but also through offline events and workshops.

[0047] It is possible to connect parents from different regions and cultures and form a global community. It is possible to connect parents from different regions and cultures and form a global community. For example, it is possible to provide international childcare forums and chat groups. It is also possible to provide translation functions to promote interaction between parents from different cultures. For example, it is possible to support communication in different languages. It is also possible to share childcare information and experiences from different regions to promote information exchange from a global perspective. For example, it is possible to introduce childcare customs and traditional methods from each country. In this way, it is possible to connect parents from different regions and cultures and form a global community.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The support system can also be equipped with a recommendation function based on parents' hobbies and interests. For example, if a parent enjoys outdoor activities, it can provide information on outdoor activities that can be enjoyed together with their children. For parents who enjoy cooking, it can suggest simple recipes that can be made together with children. Furthermore, for parents who are interested in music or art, it can provide information on music or art workshops that can be enjoyed together with children. This makes it possible to provide information based on parents' hobbies and interests, and supports them in making child-rearing more enjoyable.

[0050] The support system can also provide specific advice on health management based on the health data of parents. For example, it can provide advice on appropriate diet and exercise based on blood pressure and weight data. It can also analyze health data and provide advice based on the health condition. For example, if blood pressure is high, it can suggest reducing salt intake. It can also provide advice for long-term health management based on health data. For example, it can explain the importance of weight management and regular health checks. This makes it possible to provide specific advice based on the health data of parents.

[0051] The support system can also analyze parents' social media activity to collect attribute data that reflects their interests and concerns. For example, it can analyze parents' social media accounts and collect the content of their posts and reactions such as "likes." For example, it can analyze posts about child-rearing and the accounts they follow to identify their interests and concerns. It can also analyze the time and frequency of parents' social media activities to understand their online behavior patterns. For example, it can analyze the tendency for many posts to be made at certain times of the day to estimate their daily rhythms. It can also analyze parents' comments and messages on social media to collect their emotions and opinions. For example, it can identify concerns and questions about child-rearing and use this information to provide individualized advice. This makes it possible to provide advice based on parents' interests and concerns.

[0052] The support system can also analyze parents' past search and browsing histories to provide individually customized information. For example, it can prioritize the display of information related to keywords previously searched. It can also identify themes and topics that parents are interested in based on their browsing history and provide that information. For example, it can suggest information related to articles or videos previously viewed. It can also analyze search and browsing histories to provide information based on parents' interests. For example, it can provide the latest information and news on a specific topic. This makes it possible to provide customized information based on parents' past behavior.

[0053] The support system can also collect lifestyle data from parents from wearable devices and analyze it using AI. For example, wearable devices can be used to monitor and collect data on parents' sleep patterns. For example, sleep quality and duration can be analyzed to understand the parents' health status. Smartwatches and fitness trackers can also be used to record the parents' diet. For example, meal timing and nutrient intake can be analyzed to help with health management. Exercise data collected from wearable devices can also be analyzed to understand the parents' activity levels. For example, steps taken and calories burned can be monitored to help improve lifestyle habits. This allows the system to collect lifestyle data from parents and provide useful advice for health management.

[0054] The support system can also provide information on childbirth and child-rearing from different cultural spheres and regions, and offer advice from a global perspective. For example, it introduces child-rearing customs and traditional methods from each country. It also incorporates the opinions of experts and people with child-rearing experience from different cultures to offer advice from a global perspective. For example, it provides information in the form of interviews and discussions. It can also compare childbirth and child-rearing information from different regions to clarify similarities and differences. For example, it can compare child-rearing methods and support systems in each region and propose the most appropriate method. This makes it possible to provide information on different cultural spheres and regions, and offer advice from a global perspective.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The attribute data collection unit collects attribute data on the parents, such as age, gender, occupation, residential area, family structure, and health status. It also collects data tailored to individual circumstances, such as whether both parents are working or whether this is their first time raising a child. Step 2: The information provider provides information on childbirth and childcare based on the attribute data of the parents collected by the attribute data collector. For example, it provides information on health management during pregnancy, preparation for childbirth, and basic knowledge and methods of childcare. Specifically, it provides information such as "foods to be careful of during pregnancy" and "sleeping patterns of newborns." Step 3: The advice provider provides personalized advice based on the information provided by the information provider and the parents' attribute data. For example, if parents are facing a specific problem, the advice provider will suggest a specific solution to that problem. Specifically, the advice provider will provide advice tailored to each individual situation, such as "what to do if your baby cries at night" or "advice if breastfeeding is not going well."

[0057] (Example 2) The support system according to an embodiment of the present invention collects attribute data of parents, analyzes it using a generation AI, provides information on childbirth and child-rearing, and gives individualized advice. This allows the support system to provide optimal advice based on the attributes of parents and support their anxieties and questions about childbirth and child-rearing.

[0058] The support system according to the embodiment includes an attribute data collection unit, an information provision unit, and an advice provision unit. The attribute data collection unit collects attribute data of parents, such as age, gender, occupation, residential area, family structure, and health status. The attribute data collection unit also collects data tailored to individual circumstances, such as whether both parents are working or whether this is their first child. The information provision unit provides information about childbirth and childcare based on the attribute data of parents collected by the attribute data collection unit. For example, it provides information on health management during pregnancy, preparation for childbirth, and basic knowledge and methods of childcare. Specifically, it provides information such as "dietary precautions to take during pregnancy" and "sleeping patterns of newborns." The advice provision unit provides personalized advice based on the information provided by the information provision unit and the attribute data of parents. For example, if parents are facing a specific problem, it proposes a specific solution to the problem. Specifically, it provides advice tailored to the individual situation, such as "what to do if your baby cries at night" or "advice if breastfeeding is not going well." As a result, the support system according to the embodiment can provide optimal advice based on the attributes of the parents and support them in dealing with their anxieties and questions regarding childbirth and child-rearing.

[0059] The attribute data collection unit can monitor the emotional state of parents in real time and collect attribute data based on their emotions using an emotion estimation function. For example, the attribute data collection unit uses a wearable device to collect physiological data such as heart rate and electrodermal activity to monitor the emotional state of parents in real time. The emotion estimation function is then used to analyze changes in emotions and collect attribute data based on the emotions. The attribute data collection unit also uses a smartphone's camera and microphone to analyze the facial expressions and tone of voice of parents to estimate their emotional state in real time. For example, facial recognition technology is used to detect smiling and angry expressions and collect the emotion data. The attribute data collection unit also analyzes text messages and social media posts entered by parents to perform emotion analysis. For example, natural language processing technology is used to identify positive and negative emotions and collect the data as attribute data. This allows the collection of attribute data based on the emotional state of parents and the provision of more appropriate advice.

[0060] The attribute data collection unit can collect lifestyle habit data of parents from wearable devices and analyze it using AI. For example, the attribute data collection unit uses wearable devices to monitor the sleep patterns of parents and collect data. For example, it analyzes the quality and duration of sleep to understand the health status of parents. The attribute data collection unit also uses smartwatches and fitness trackers to record the dietary habits of parents. For example, it analyzes meal timing and nutrient intake to help manage health. The attribute data collection unit also analyzes exercise data collected from wearable devices to understand the activity level of parents. For example, it monitors the number of steps taken and calories burned to help improve lifestyle habits. In this way, it is possible to collect lifestyle habit data of parents and provide advice useful for health management.

[0061] The attribute data collection unit can analyze parents' social media activities and collect attribute data reflecting their interests and concerns. For example, the attribute data collection unit analyzes parents' social media accounts and collects the content of their posts and responses such as "likes." For example, it analyzes posts about childcare and accounts they follow to identify their interests and concerns. The attribute data collection unit also analyzes the time and frequency of parents' activities on social media to understand their online behavior patterns. For example, it analyzes the tendency to post frequently during certain times of the day to estimate their daily rhythms. The attribute data collection unit also analyzes parents' comments and messages on social media to collect their emotions and opinions. For example, it can identify worries and questions about childcare and use this information to provide individualized advice. This makes it possible to provide advice based on parents' interests and concerns.

[0062] The information providing unit can use the emotion estimation function to provide information according to the emotional state of the parents, and prioritize the provision of information that elicits positive emotions. The information providing unit, for example, uses the emotion estimation function to analyze the emotional state of the parents in real time and provide information that elicits positive emotions. For example, if the emotion score is low, it presents encouraging messages or success stories. The information providing unit also provides relaxing information or entertainment content according to the emotional state of the parents. For example, if the emotion score is low, it suggests relaxing music or videos. The information providing unit also identifies the information that the parents are most interested in based on the emotion estimation data and prioritizes the provision of that information. For example, it provides information related to topics with high emotion scores. In this way, it is possible to provide information according to the emotional state of the parents and elicit positive emotions.

[0063] The information providing unit can analyze parents' past search history and browsing history and provide individually customized information. The information providing unit, for example, analyzes parents' past search history and browsing history and provides individually customized information. For example, it prioritizes displaying information related to keywords searched in the past. The information providing unit also identifies themes and topics that parents are interested in based on the browsing history and provides that information. For example, it suggests information related to articles or videos viewed in the past. The information providing unit also analyzes search history and browsing history and provides information based on parents' interests. For example, it provides the latest information and news on a specific topic. This makes it possible to provide customized information based on parents' past behavior.

[0064] The information providing unit can provide specific advice regarding health management based on the health data of the parents. The information providing unit, for example, collects health data of the parents and provides specific advice regarding health management. For example, it gives advice on appropriate diet and exercise based on blood pressure and weight data. The information providing unit also analyzes the health data and provides advice according to the health condition. For example, if blood pressure is high, it suggests reducing salt intake. The information providing unit also provides advice for long-term health management based on the health data. For example, it explains the importance of weight management and regular health checks. In this way, it is possible to provide specific advice based on the health data of the parents.

[0065] The information provision unit can also provide information about childbirth and child-rearing in video and audio formats to make it easier to understand through sight and hearing. For example, the information provision unit provides information about childbirth and child-rearing in video format to make it easier to understand visually. For example, basic child-rearing methods and precautions are explained in video. The information provision unit also provides information in audio format to make it easier to understand through hearing. For example, it provides podcasts and audiobooks related to child-rearing. The information provision unit also provides information in a combination of video and audio formats to utilize both sight and hearing. For example, audio commentary is added to video to make the information easier to understand. This makes it possible to provide information that is easy to understand through sight and hearing.

[0066] The information section provides information on childbirth and child-rearing from different cultural regions and offers advice from a global perspective. For example, the information section collects information on childbirth and child-rearing from different cultural regions and offers advice from a global perspective. For example, it introduces child-rearing customs and traditional methods from each country. The information section also incorporates the opinions of experts and people with child-rearing experience from different cultural regions and offers advice from a global perspective. For example, it provides information in the form of interviews and discussions. The information section also compares childbirth and child-rearing information from different regions and clarifies similarities and differences. For example, it compares child-rearing methods and support systems in each region and suggests the most appropriate method. This makes it possible to provide information on different cultural regions and offer advice from a global perspective.

[0067] The information providing unit can use the emotion estimation function to identify information that parents are most interested in and provide that information preferentially. The information providing unit, for example, uses the emotion estimation function to identify information that parents are most interested in. For example, it provides preferentially information related to topics with high emotion scores. The information providing unit also analyzes the emotional responses of parents in real time to identify information that parents are interested in. For example, it displays preferentially articles and videos with high emotion scores. The information providing unit also customizes and provides information that parents are interested in based on the emotion estimation data. For example, it provides the latest information and news related to topics with high emotion scores. This makes it possible to provide preferentially information that parents are most interested in.

[0068] The advice providing unit can analyze the past behavioral data of parents and provide specific advice based on their behavioral patterns. The advice providing unit, for example, collects the past behavioral data of parents and analyzes their behavioral patterns. For example, the behavioral patterns are identified based on childcare diaries and app usage history. The advice providing unit also provides specific advice based on the behavioral patterns. For example, if the baby cries frequently at night, it suggests ways to review the nighttime routine. The advice providing unit also provides advice for creating a long-term childcare plan based on the past behavioral data. For example, it suggests schedules for regular health checks and vaccinations. This makes it possible to provide specific advice based on the past behavioral data of parents.

[0069] The advice providing unit can monitor the health data of parents in real time and provide advice according to their health condition. The advice providing unit, for example, monitors the health data of parents in real time and provides advice according to their health condition. For example, it provides advice on appropriate exercise and diet based on heart rate and blood pressure data. The advice providing unit also analyzes the health data and provides specific advice according to their health condition. For example, if the stress level is high, it suggests advice on relaxation methods and stress management. The advice providing unit also provides advice for long-term health management based on the health data. For example, it suggests schedules for regular health checks and vaccinations. This makes it possible to monitor the health data of parents in real time and provide advice according to their health condition.

[0070] The advice providing unit can provide advice tailored to the parents' occupations and lifestyles, and support balancing work and childcare. The advice providing unit, for example, collects data on the parents' occupations and lifestyles, and provides advice to support balancing work and childcare. For example, it suggests efficient time management methods to parents who work from home. The advice providing unit also provides specific advice tailored to the parents' occupations and lifestyles. For example, it suggests ways to adjust childcare and work schedules to parents who work shifts. The advice providing unit also suggests stress management and relaxation methods based on the parents' occupations and lifestyles. For example, it suggests ways to refresh in a short amount of time to parents who have busy jobs. In this way, advice tailored to the parents' occupations and lifestyles can be provided, and support can be provided to support balancing work and childcare.

[0071] The advice providing unit can provide advice based on the hobbies and interests of parents and make suggestions for enjoying child-rearing. The advice providing unit, for example, collects data on the hobbies and interests of parents and provides advice for enjoying child-rearing. For example, for parents whose hobby is the outdoors, it suggests outdoor activities that can be enjoyed together with their children. The advice providing unit also suggests ways to reduce the stress of child-rearing based on the hobbies and interests. For example, for parents whose hobby is art, it suggests art activities that can be enjoyed together with their children. The advice providing unit also makes specific suggestions for enjoying child-rearing based on the hobbies and interests of parents. For example, for parents whose hobby is cooking, it suggests cooking recipes that can be enjoyed together with their children. This makes it possible to provide advice based on the hobbies and interests of parents and make suggestions for enjoying child-rearing.

[0072] The advice providing unit can use the emotion estimation function to analyze the stress level of the parents and provide specific advice for stress reduction. The advice providing unit, for example, uses the emotion estimation function to analyze the stress level of the parents in real time and provide specific advice for stress reduction. For example, the heart rate and electrodermal activity are monitored to quantify the stress level. Furthermore, if the stress level is high, the advice providing unit provides advice on relaxation methods and stress reduction. For example, deep breathing and meditation methods are suggested. Furthermore, the advice providing unit regularly monitors the stress level of the parents and provides advice for long-term stress management. For example, the cause of stress is identified and measures to be taken are suggested. In this way, the stress level of the parents can be analyzed and specific advice for stress reduction can be provided.

[0073] The emotion estimation function can be used to monitor changes in the emotional state of parents and provide continuous support according to their emotions. The emotion estimation function can be used, for example, to monitor changes in the emotional state of parents in real time and provide continuous support according to their emotions. For example, if the emotional score is low, encouraging messages or relaxation techniques can be suggested. The emotion estimation function can also be used to provide emotional support according to changes in the emotional state of parents. For example, if the emotional score is low, information about counseling or support groups can be provided. The emotion estimation data can also be used to identify the support that parents need most and provide that information. For example, if the emotional score is low, stress management or relaxation techniques can be suggested. This makes it possible to monitor changes in the emotional state of parents and provide continuous support according to their emotions.

[0074] It is possible to collect data on a child's growth and provide specific support according to their stage of growth. It is possible to collect data on a child's growth and provide specific support according to their stage of growth. For example, advice on appropriate nutrition and exercise can be given based on height and weight data. It is also possible to analyze the growth data and provide specific advice according to their stage of growth. For example, it can suggest how to introduce solid food and how to discipline a child in early childhood. It is also possible to provide advice for long-term growth management based on the growth data. For example, it can suggest schedules for regular health checks and vaccinations. In this way, it is possible to collect data on a child's growth and provide specific support according to their stage of growth.

[0075] The lifestyle data of parents can be analyzed and continuous advice can be provided regarding lifestyle improvement. The lifestyle data of parents can be collected and, for example, continuous advice can be provided regarding lifestyle improvement. For example, sleep patterns and dietary content can be analyzed and healthy lifestyle habits can be suggested. The lifestyle data can also be analyzed and specific advice can be provided regarding lifestyle improvement. For example, if there is a lack of exercise, an appropriate exercise plan can be suggested. The lifestyle data can also be used to provide advice regarding long-term lifestyle improvement. For example, regular health checks and stress management methods can be suggested. In this way, the lifestyle data of parents can be analyzed and continuous advice can be provided regarding lifestyle improvement.

[0076] Continuous support can be provided through different devices. Continuous support can be provided, for example, through a smartphone. For example, real-time information and advice about child-rearing can be provided through a dedicated app. Continuous support can also be provided using a tablet. For example, video and audio content related to child-rearing can be provided on the tablet, conveying information visually and audibly. Continuous support can also be provided through a smart speaker. For example, a voice assistant can be used to provide answers to questions about child-rearing and advice. In this way, continuous support can be provided through different devices.

[0077] Supporting parents' community activities and strengthening ties with the local community. To support parents' community activities, for example, providing information about local childcare support groups and events. For example, sharing information about local childcare circles and workshops. Also, to strengthen ties with the local community, promoting interaction with local experts and people with childcare experience. For example, holding consultation sessions with local childcare counselors and doctors. Also, to support parents' community activities, providing information exchange and support through online forums and chat groups. For example, providing a platform where parents living in the same area can interact with each other. This will support parents' community activities and strengthen ties with the local community.

[0078] The emotion estimation function can be used to match communities based on the emotional state of parents, connecting members who are likely to empathize emotionally. For example, the emotion estimation function can be used to analyze the emotional state of parents in real time and match members who are likely to empathize emotionally. For example, parents with similar emotion scores can be connected. The emotion estimation function can also be used to form communities to connect members who are likely to empathize emotionally based on the emotional state of parents. For example, members who are interested in topics with high emotion scores can be connected. Furthermore, members who are most likely to empathize with parents can be identified based on the emotion estimation data, and these members can be connected. For example, members with high emotion scores can be matched preferentially. This makes it possible to match communities based on the emotional state of parents, connecting members who are likely to empathize emotionally.

[0079] Based on the attribute data of parents, it is possible to connect members with common interests. By collecting attribute data of parents, for example, members with common interests can be connected. For example, parents with the same hobbies or special skills can be matched. Furthermore, based on the attribute data, members with common child-rearing experiences or challenges can be connected. For example, parents who are raising a child for the first time can be matched. Furthermore, by analyzing the attribute data of parents, a community can be formed to connect members with common interests. For example, parents who live in the same area can be connected. In this way, it is possible to connect members with common interests based on the attribute data of parents.

[0080] To promote the exchange of information within a community, AI can automatically suggest topics. For example, AI can automatically suggest topics to promote the exchange of information within a community. For example, it can suggest the latest news and trends related to child-rearing. AI can also analyze the interests and concerns of members within a community and suggest topics based on that. For example, it can suggest topics related to topics that interest members. To stimulate the exchange of information within a community, AI can automatically suggest topics and promote discussion. For example, it can suggest topics for questions and exchanges of opinions related to child-rearing. This allows AI to automatically suggest topics to promote the exchange of information within a community.

[0081] Communities can be formed not only online but also through offline events and workshops. Communities can be formed not only online but also through offline events and workshops, for example, by holding local parenting circles and workshops. Also, offline events can be used to promote interaction between parents, for example, by holding seminars and lectures on parenting. Also, communities can be formed both online and offline to exchange information and provide support. For example, by combining discussions in online forums with interactions at offline events. In this way, communities can be formed not only online but also through offline events and workshops.

[0082] It is possible to connect parents from different regions and cultures and form a global community. It is possible to connect parents from different regions and cultures and form a global community. For example, it is possible to provide international childcare forums and chat groups. It is also possible to provide translation functions to promote interaction between parents from different cultures. For example, it is possible to support communication in different languages. It is also possible to share childcare information and experiences from different regions to promote information exchange from a global perspective. For example, it is possible to introduce childcare customs and traditional methods from each country. In this way, it is possible to connect parents from different regions and cultures and form a global community.

[0083] The emotion estimation function can be used to strengthen emotional support within a community and deepen the bonds between members. The emotion estimation function can be used, for example, to strengthen emotional support within a community. For example, encouraging messages and support can be provided to members with low emotion scores. In addition, to strengthen emotional support within a community, bonds between members can be deepened based on emotion estimation data. For example, members with similar emotion scores can be connected. The emotion estimation function can also be used to provide emotional support within a community and suggest events and activities to deepen bonds between members. For example, an event related to a theme with a high emotion score can be held. In this way, the emotion estimation function can be used to strengthen emotional support within a community and deepen bonds between members.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The support system can also be equipped with a recommendation function based on parents' hobbies and interests. For example, if a parent enjoys outdoor activities, it can provide information on outdoor activities that can be enjoyed together with their children. For parents who enjoy cooking, it can suggest simple recipes that can be made together with children. Furthermore, for parents who are interested in music or art, it can provide information on music or art workshops that can be enjoyed together with children. This makes it possible to provide information based on parents' hobbies and interests, and supports them in making child-rearing more enjoyable.

[0086] The support system can also suggest relaxation methods based on the parents' emotional state. For example, if the emotional score is low, it can suggest deep breathing or meditation. If the emotional score is high, it can provide relaxing music or natural sounds. It can also suggest relaxation methods such as aromatherapy or massage depending on the emotional state. This makes it possible to provide relaxation methods based on the parents' emotional state and support stress reduction.

[0087] The support system can also provide specific advice on health management based on the health data of parents. For example, it can provide advice on appropriate diet and exercise based on blood pressure and weight data. It can also analyze health data and provide advice based on the health condition. For example, if blood pressure is high, it can suggest reducing salt intake. It can also provide advice for long-term health management based on health data. For example, it can explain the importance of weight management and regular health checks. This makes it possible to provide specific advice based on the health data of parents.

[0088] The support system can also analyze parents' social media activity to collect attribute data that reflects their interests and concerns. For example, it can analyze parents' social media accounts and collect the content of their posts and reactions such as "likes." For example, it can analyze posts about child-rearing and the accounts they follow to identify their interests and concerns. It can also analyze the time and frequency of parents' social media activities to understand their online behavior patterns. For example, it can analyze the tendency for many posts to be made at certain times of the day to estimate their daily rhythms. It can also analyze parents' comments and messages on social media to collect their emotions and opinions. For example, it can identify concerns and questions about child-rearing and use this information to provide individualized advice. This makes it possible to provide advice based on parents' interests and concerns.

[0089] The support system can further use its emotion estimation function to provide information according to the emotional state of parents, and prioritize information that elicits positive emotions. For example, if the emotion score is low, encouraging messages or success stories can be presented. The support system can also provide relaxing information or entertainment content according to the emotional state. For example, if the emotion score is low, relaxing music or videos can be suggested. The support system can also identify the information that parents are most interested in based on the emotion estimation data and prioritize providing that information. For example, it can provide information related to topics with high emotion scores. This allows the support system to provide information according to the emotional state of parents and elicit positive emotions.

[0090] The support system can also analyze parents' past search and browsing histories to provide individually customized information. For example, it can prioritize the display of information related to keywords previously searched. It can also identify themes and topics that parents are interested in based on their browsing history and provide that information. For example, it can suggest information related to articles or videos previously viewed. It can also analyze search and browsing histories to provide information based on parents' interests. For example, it can provide the latest information and news on a specific topic. This makes it possible to provide customized information based on parents' past behavior.

[0091] The support system can also monitor changes in the emotional state of parents and provide ongoing support according to their emotions. For example, if the emotional score is low, it can suggest encouraging messages or relaxation techniques. It also uses the emotion estimation function to provide emotional support according to changes in the emotional state of parents. For example, if the emotional score is low, it can provide information about counseling or support groups. It also uses the emotion estimation data to identify the support that parents need most and provide that information. For example, if the emotional score is low, it can suggest stress management or relaxation techniques. This makes it possible to monitor changes in the emotional state of parents and provide ongoing support according to their emotions.

[0092] The support system can also collect lifestyle data from parents from wearable devices and analyze it using AI. For example, wearable devices can be used to monitor and collect data on parents' sleep patterns. For example, sleep quality and duration can be analyzed to understand the parents' health status. Smartwatches and fitness trackers can also be used to record the parents' diet. For example, meal timing and nutrient intake can be analyzed to help with health management. Exercise data collected from wearable devices can also be analyzed to understand the parents' activity levels. For example, steps taken and calories burned can be monitored to help improve lifestyle habits. This allows the system to collect lifestyle data from parents and provide useful advice for health management.

[0093] The support system can also provide information on childbirth and child-rearing from different cultural spheres and regions, and offer advice from a global perspective. For example, it introduces child-rearing customs and traditional methods from each country. It also incorporates the opinions of experts and people with child-rearing experience from different cultures to offer advice from a global perspective. For example, it provides information in the form of interviews and discussions. It can also compare childbirth and child-rearing information from different regions to clarify similarities and differences. For example, it can compare child-rearing methods and support systems in each region and propose the most appropriate method. This makes it possible to provide information on different cultural spheres and regions, and offer advice from a global perspective.

[0094] The support system can further use the emotion estimation function to identify the information that parents are most interested in and provide that information preferentially. For example, it can provide information related to topics with high emotion scores preferentially. It can also analyze parents' emotional responses in real time to identify information that they are interested in. For example, it can display articles and videos with high emotion scores preferentially. It can also customize and provide information that parents are interested in based on the emotion estimation data. For example, it can provide the latest information and news related to topics with high emotion scores. This allows it to provide information that parents are most interested in preferentially.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The attribute data collection unit collects attribute data on the parents, such as age, gender, occupation, residential area, family structure, and health status. It also collects data tailored to individual circumstances, such as whether both parents are working or whether this is their first time raising a child. Step 2: The information provider provides information on childbirth and childcare based on the attribute data of the parents collected by the attribute data collector. For example, it provides information on health management during pregnancy, preparation for childbirth, and basic knowledge and methods of childcare. Specifically, it provides information such as "foods to be careful of during pregnancy" and "sleeping patterns of newborns." Step 3: The advice provider provides personalized advice based on the information provided by the information provider and the parents' attribute data. For example, if parents are facing a specific problem, the advice provider will suggest a specific solution to that problem. Specifically, the advice provider will provide advice tailored to each individual situation, such as "what to do if your baby cries at night" or "advice if breastfeeding is not going well."

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 attribute data collection unit that collects attribute data of parents; an information providing unit that provides information about childbirth and child-rearing based on the attribute data of the parents collected by the attribute data collecting unit; an advice providing unit that provides individual advice based on the information provided by the information providing unit and the attribute data of the parents; A system characterized by:

2. The attribute data collection unit Monitor parents' emotional states in real time and collect emotion-based attribute data 2. The system of claim 1.

3. The attribute data collection unit Parental lifestyle data will be collected from wearable devices and analyzed using the AI.

2. The system of claim 1.

4. The attribute data collection unit Analyzing parents' social media activities and collecting the attribute data reflecting their interests 2. The system of claim 1.

5. The information providing unit Provide information according to the emotional state of the parents, and provide information that elicits positive emotions preferentially.

2. The system of claim 1.

Images