System

Abstract

An object of a system according to an embodiment is to observe meal contents of a child and provide appropriate child-care advice.SOLUTION: A system includes an observation unit, an analysis unit, and an advice providing unit. The observation unit observes meal contents of the child. The analysis unit analyzes the meal content observed by the observation unit. The advice providing unit provides child-care advice based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for busy parents to closely monitor their children's diet and receive appropriate childcare advice.

[0005] The system according to the embodiment aims to monitor the dietary content of a child and provide appropriate parenting advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an observation unit, an analysis unit, and an advice providing unit. The observation unit observes the child's diet. The analysis unit analyzes the diet observed by the observation unit. The advice providing unit provides child-rearing advice based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the dietary content of a child and provide appropriate parenting advice. [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 robot-type AI service according to the embodiment of the present invention is a system that observes a child's diet, sleep patterns, and emotional fluctuations in real time and provides scientifically based parenting advice based on the observations. This reduces the burden on parents and supports the health and development of children.

[0029] A robot-type AI service according to an embodiment includes an observation unit, an analysis unit, and an advice providing unit. The observation unit observes a child's diet. For example, the observation unit uses a camera to take pictures of ingredients the child eats and collects the data. The observation unit can also record dietary information reported by a parent using voice input. The observation unit can also record dietary information entered by a parent using text input. The analysis unit analyzes the dietary information observed by the observation unit. For example, the analysis unit can analyze the nutritional value of ingredients in real time using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also evaluate the nutritional balance of the dietary information using a machine learning algorithm. The analysis unit can also perform statistical analysis of the data to identify dietary trends. The advice providing unit provides childcare advice based on the data analyzed by the analysis unit. For example, the advice providing unit can suggest a balanced diet. The advice providing unit can also suggest ideas for enjoyable eating methods. The advice providing unit can also suggest improvements to the dietary information. As a result, the robot-type AI service according to the embodiment can observe and analyze the dietary contents of children and provide childcare advice, thereby reducing the burden of childcare on parents.

[0030] The observation unit observes the child's sleep patterns, and the analysis unit analyzes the sleep patterns observed by the observation unit and can provide parenting advice. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The analysis unit analyzes the sleep patterns observed by the observation unit. For example, the analysis unit can evaluate sleep quality using generative AI. The analysis unit can also identify trends in sleep patterns using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data to identify areas for improvement in the sleep patterns. In this way, the child's sleep quality can be improved by observing and analyzing the child's sleep patterns and providing parenting advice.

[0031] The observation unit observes the child's emotional fluctuations, and the analysis unit analyzes the emotional fluctuations observed by the observation unit and can provide parenting advice. The observation unit, for example, captures the child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The analysis unit analyzes the emotional fluctuations observed by the observation unit. For example, the analysis unit identifies patterns of emotional fluctuations using generative AI. The analysis unit can also grasp trends in emotional fluctuations using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data to identify areas for improvement in emotional fluctuations. In this way, the child's emotional fluctuations can be observed and analyzed and parenting advice can be provided, thereby stabilizing the child's emotions.

[0032] The analysis unit can comprehensively analyze dietary content, sleep patterns, and emotional fluctuations to provide scientifically based parenting advice. For example, the analysis unit integrates data on dietary content, sleep patterns, and emotional fluctuations and performs a comprehensive analysis using generative AI. The analysis unit can also use machine learning algorithms to identify correlations in the data. Furthermore, the analysis unit can perform statistical analysis of the data to provide scientifically based parenting advice. This makes it possible to support a child's health and growth by comprehensively analyzing a child's dietary content, sleep patterns, and emotional fluctuations and providing scientifically based parenting advice.

[0033] The observation unit observes the meal contents, and the generation AI analyzes the nutritional value of ingredients in the meal contents observed by the observation unit in real time, allowing for instant evaluation of nutritional balance. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record meal contents reported by parents using voice input. The observation unit can also record meal contents entered by parents using text input. The generation AI analyzes the nutritional value of ingredients in the meal contents observed by the observation unit in real time. For example, the generation AI analyzes images of ingredients photographed with a camera and evaluates nutritional value. The generation AI can also analyze meal contents recorded using voice input or text input and evaluate nutritional balance. In this way, by observing meal contents and the generation AI analyzing the nutritional value of ingredients in real time and instantly evaluating nutritional balance, a well-balanced meal can be provided.

[0034] The observation unit observes the meal content, and the generation AI can learn the child's meal preferences based on the meal content observed by the observation unit and generate an individually customized meal plan. The observation unit, for example, photographs the meal content with a camera and collects the data. The observation unit can also record the meal content reported by the parent using voice input. The observation unit can also record the meal content entered by the parent using text input. The generation AI learns the child's meal preferences based on the meal content observed by the observation unit. For example, the generation AI analyzes images of ingredients photographed with a camera to learn the child's preferences. The generation AI can also analyze the meal content recorded using voice input or text input to learn the child's preferences. In this way, the generation AI can observe the meal content, learn the child's meal preferences, and generate an individually customized meal plan, thereby improving the child's meal satisfaction.

[0035] The observation unit observes meal contents, and the generation AI can make suggestions for home gardening and advice on how to select ingredients based on the observation data of the meal contents observed by the observation unit. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record the meal contents reported by parents using voice input. The observation unit can also record the meal contents entered by parents using text input. The generation AI makes suggestions for home gardening and advice on how to select ingredients based on the observation data of the meal contents observed by the observation unit. For example, the generation AI can suggest vegetables and fruits that should be grown in a home garden based on ingredients that children like. The generation AI can also give advice on how to select ingredients according to the season. In this way, by observing meal contents and the generation AI making suggestions for home gardening and advice on how to select ingredients based on the observation data, it can support ingredient selection and cultivation at home.

[0036] The observation unit observes meal contents, and the generation AI can add a community function that compares the meal contents observed by the observation unit with the meal data of other households and shares best practices. The observation unit, for example, photographs meal contents with a camera and collects the data. The observation unit can also record meal contents reported by parents using voice input. The observation unit can also record meal contents entered by parents using text input. The generation AI adds a community function that compares the meal contents observed by the observation unit with the meal data of other households and shares best practices. For example, the generation AI collects meal data of other households and analyzes that data to extract best practices. The generation AI can also add a community function and build a platform where parents can share meal ideas and recipes. This can promote information sharing among parents by adding a community function that observes meal contents, compares them with the meal data of other households, and shares best practices.

[0037] The observation unit observes sleep patterns, and the generation AI can analyze environmental factors in real time based on the sleep patterns observed by the observation unit and suggest an optimal sleep environment. The observation unit, for example, captures a child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The generation AI analyzes environmental factors in real time based on the sleep patterns observed by the observation unit. For example, the generation AI can measure the temperature in a child's bedroom with a sensor and analyze the data to suggest an optimal temperature. The generation AI can also measure the humidity in a child's bedroom with a sensor and analyze the data to suggest an optimal humidity level. This allows the generation AI to observe sleep patterns, analyze environmental factors in real time, and suggest an optimal sleep environment, thereby improving the quality of a child's sleep.

[0038] The observation unit observes sleep patterns, and the generation AI analyzes the child's movements during sleep based on the sleep patterns observed by the observation unit, thereby developing new indices for evaluating sleep quality. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also record the child's movements during sleep using a sensor. Furthermore, the observation unit can record sleep patterns reported by parents via voice input. The generation AI analyzes the child's movements during sleep based on the sleep patterns observed by the observation unit. For example, the generation AI can analyze movement data detected by a motion sensor to develop new indices for evaluating sleep quality. The generation AI can also analyze heart rate and breathing pattern data to develop new indices for evaluating sleep quality. This allows for more accurate evaluation of a child's sleep quality by observing sleep patterns, analyzing the child's movements during sleep, and developing new indices for evaluating sleep quality.

[0039] The observation unit observes sleep patterns, and the generation AI can provide parents with sleep improvement advice based on the sleep pattern data observed by the observation unit. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The generation AI provides parents with sleep improvement advice based on the sleep pattern data observed by the observation unit. For example, the generation AI can analyze the child's sleep pattern data and suggest that parents and children sleep at the same rhythm. The generation AI can also make suggestions for improving the parent's bedroom environment. In this way, the generation AI can observe sleep patterns and provide parents with sleep improvement advice based on the sleep pattern data, thereby improving the quality of the parents' sleep.

[0040] The observation unit observes sleep patterns, and the generation AI can add a community function that compares the sleep patterns observed by the observation unit with the sleep data of other families and shares best practices. For example, the observation unit may capture a child's sleep patterns with a camera and collect the data. The observation unit may also use sensors to record the child's movements while sleeping. The observation unit may also record sleep patterns reported by parents via voice input. The generation AI can add a community function that compares the sleep patterns observed by the observation unit with the sleep data of other families and shares best practices. For example, the generation AI may collect sleep data from other families and analyze that data to extract best practices. The generation AI can also add a community function to build a platform where parents can share ideas and methods for improving sleep. This can encourage information sharing among parents by adding a community function that observes sleep patterns, compares them with the sleep data of other families, and shares best practices.

[0041] The observation unit observes emotional fluctuations, and the generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit, identifies stress factors, and proposes a specific action plan for stress reduction. The observation unit, for example, captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit. For example, if the generation AI becomes emotionally unstable during a specific time period or situation, it can identify the cause. After identifying the stress factors, the generation AI can also propose a specific action plan. In this way, children's stress can be reduced by observing emotional fluctuations, identifying stress factors based on the emotional fluctuation data, and proposing a specific action plan for stress reduction.

[0042] The observation unit observes emotional fluctuations, and the generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit, and can collaborate with schools and daycare centers to propose support in the educational setting. The observation unit, for example, captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit. For example, if the generation AI becomes emotionally unstable at a specific time or in a specific situation, it can identify the cause. The generation AI can also collaborate with schools and daycare centers based on the emotional fluctuation data and propose support in the educational setting. This makes it possible to improve children's educational environment by observing emotional fluctuations and collaborating with schools and daycare centers based on the emotional fluctuation data to propose support in the educational setting.

[0043] The observation unit observes emotional fluctuations, and the generation AI compares the emotional fluctuations observed by the observation unit with the emotional data of other families, and can add a community function for sharing best practices. For example, the observation unit captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using voice input. Furthermore, the observation unit can also record the child's behavior using a sensor. The generation AI compares the emotional fluctuations observed by the observation unit with the emotional data of other families and adds a community function for sharing best practices. For example, the generation AI collects emotional data from other families and analyzes that data to extract best practices. The generation AI can also add a community function to build a platform where parents can share countermeasures and methods for dealing with emotional fluctuations. This can promote information sharing among parents by adding a community function for observing emotional fluctuations, comparing them with the emotional data of other families, and sharing best practices.

[0044] Based on the collected data, the analysis unit allows the generating AI to analyze the data collected by the analysis unit, refer to the latest childcare research, and provide advice based on scientific evidence. The analysis unit, for example, integrates the collected data and performs a comprehensive analysis using the generating AI. The analysis unit can also grasp correlations in the data using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data, refer to the latest childcare research, and provide advice based on scientific evidence. In this way, the generating AI can refer to the latest childcare research based on the collected data and provide advice based on scientific evidence, making it possible to provide highly reliable childcare advice.

[0045] The analysis unit generates a childcare plan according to the child's developmental stage, and the generation AI can periodically update the childcare plan generated by the analysis unit. For example, the analysis unit collects data according to the child's developmental stage and generates an individual childcare plan using the generation AI. The analysis unit can also analyze data according to the child's developmental stage using a machine learning algorithm to generate a childcare plan. Furthermore, the generation AI can periodically update the childcare plan to reflect the latest data. In this way, by generating a childcare plan according to the child's developmental stage and periodically updating it, it is possible to provide optimal childcare advice tailored to the child's development.

[0046] The analysis unit can compare the parenting advice with parenting methods from different cultural spheres and regions and provide advice from a global perspective. For example, the analysis unit collects parenting methods from different cultural spheres and regions and analyzes the data using generative AI. The analysis unit can also use machine learning algorithms to compare parenting methods from different cultural spheres and regions and provide advice from a global perspective. Furthermore, the analysis unit can perform statistical analysis of the data to compare parenting methods from different cultural spheres and regions. This makes it possible to provide advice that incorporates diverse parenting methods by comparing parenting advice with parenting methods from different cultural spheres and regions and providing advice from a global perspective.

[0047] The analysis unit can add a community function that compares the childcare data with that of other families and shares best practices. The analysis unit, for example, collects childcare data from other families and analyzes the data using generative AI. The analysis unit can also use machine learning algorithms to compare the childcare data of other families and extract best practices. Furthermore, the analysis unit can perform statistical analysis of the data and compare the childcare data of other families. This can promote information sharing among parents by adding a community function that compares the childcare data with that of other families and shares best practices.

[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 robot-type AI service can further be equipped with a health management unit. The health management unit can monitor a child's health condition based on their diet and sleep patterns, and recommend a medical visit if necessary. For example, if a child's diet is unbalanced, it can suggest a consultation with a nutritionist. Also, if a child's sleep pattern is disrupted, it can suggest a visit to a pediatrician. Furthermore, if a child is not feeling well, it can suggest an early visit to a doctor. In this way, the health management unit can provide comprehensive support for a child's health.

[0050] The robot-type AI service may further include an environmental monitoring unit. The environmental monitoring unit may monitor the child's living environment and make appropriate suggestions for improving the environment. For example, it may monitor the temperature and humidity in the room and suggest adjusting the temperature and humidity to an appropriate level. It may also monitor the lighting and sound levels in the room and suggest appropriate lighting and sound environments. It may also monitor the air quality and suggest using an air purifier. In this way, the environmental monitoring unit may optimize the child's living environment and improve their health and comfort.

[0051] The robot-type AI service can further include a schedule management unit. The schedule management unit can manage a child's activity schedule and suggest a balanced daily life. For example, it can suggest a balanced allocation of study time, play time, meal time, and sleep time. It can also manage schedules for special events and activities and send reminders to parents. Furthermore, it can adjust the schedule according to the child's growth and suggest an optimal daily life. In this way, the schedule management unit can regulate a child's daily rhythm and support healthy growth.

[0052] The robot-type AI service can further be equipped with a safety management unit. The safety management unit can make suggestions to ensure children's safety. For example, it can identify dangerous places and objects in the home and suggest safety measures. It can also suggest safety measures when going out. Furthermore, in the event of an emergency, it can send an alert to parents to prompt them to take action quickly. In this way, the safety management unit can provide comprehensive support for children's safety.

[0053] The robot-type AI service can further include a social support unit. The social support unit can suggest activities to foster a child's social skills. For example, it can suggest playtime with friends or group activities. It can also provide games and activities to improve communication skills. It can also suggest events and workshops that parents and children can participate in together. In this way, the social support unit can foster a child's social skills and support them in building healthy relationships.

[0054] The robot-type AI service may further include a creativity support unit. The creativity support unit may suggest activities to bring out a child's creativity. For example, it may suggest activities such as drawing, crafting, or making music. It may also suggest creative projects based on the child's interests. It may also suggest creative activities that parents and children can enjoy together. In this way, the creativity support unit can foster a child's creativity and provide opportunities for self-expression.

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

[0056] Step 1: The observation unit observes the child's diet. For example, the observation unit may use a camera to take pictures of the food the child eats and collect the data. The observation unit may also record the dietary information reported by the parent using voice input. Furthermore, the observation unit may also record the dietary information entered by the parent using text input. Step 2: The analysis unit analyzes the dietary content observed by the observation unit. For example, the analysis unit may use generative AI (e.g., text generation AI or multimodal generation AI) to analyze the nutritional value of ingredients in real time. The analysis unit may also use machine learning algorithms to evaluate the nutritional balance of the dietary content. Furthermore, the analysis unit may perform statistical analysis of the data to identify trends in dietary content. Step 3: The advice providing unit provides childcare advice based on the data analyzed by the analysis unit. For example, the advice providing unit suggests a balanced diet. The advice providing unit can also present ideas for enjoyable mealtimes. Furthermore, the advice providing unit can also suggest improvements to the diet.

[0057] (Example 2) The robot-type AI service according to the embodiment of the present invention is a system that observes a child's diet, sleep patterns, and emotional fluctuations in real time and provides scientifically based parenting advice based on the observations. This reduces the burden on parents and supports the health and development of children.

[0058] A robot-type AI service according to an embodiment includes an observation unit, an analysis unit, and an advice providing unit. The observation unit observes a child's diet. For example, the observation unit uses a camera to take pictures of ingredients the child eats and collects the data. The observation unit can also record dietary information reported by a parent using voice input. The observation unit can also record dietary information entered by a parent using text input. The analysis unit analyzes the dietary information observed by the observation unit. For example, the analysis unit can analyze the nutritional value of ingredients in real time using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also evaluate the nutritional balance of the dietary information using a machine learning algorithm. The analysis unit can also perform statistical analysis of the data to identify dietary trends. The advice providing unit provides childcare advice based on the data analyzed by the analysis unit. For example, the advice providing unit can suggest a balanced diet. The advice providing unit can also suggest ideas for enjoyable eating methods. The advice providing unit can also suggest improvements to the dietary information. As a result, the robot-type AI service according to the embodiment can observe and analyze the dietary contents of children and provide childcare advice, thereby reducing the burden of childcare on parents.

[0059] The observation unit observes the child's sleep patterns, and the analysis unit analyzes the sleep patterns observed by the observation unit and can provide parenting advice. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The analysis unit analyzes the sleep patterns observed by the observation unit. For example, the analysis unit can evaluate sleep quality using generative AI. The analysis unit can also identify trends in sleep patterns using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data to identify areas for improvement in the sleep patterns. In this way, the child's sleep quality can be improved by observing and analyzing the child's sleep patterns and providing parenting advice.

[0060] The observation unit observes the child's emotional fluctuations, and the analysis unit analyzes the emotional fluctuations observed by the observation unit and can provide parenting advice. The observation unit, for example, captures the child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The analysis unit analyzes the emotional fluctuations observed by the observation unit. For example, the analysis unit identifies patterns of emotional fluctuations using generative AI. The analysis unit can also grasp trends in emotional fluctuations using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data to identify areas for improvement in emotional fluctuations. In this way, the child's emotional fluctuations can be observed and analyzed and parenting advice can be provided, thereby stabilizing the child's emotions.

[0061] The analysis unit can comprehensively analyze dietary content, sleep patterns, and emotional fluctuations to provide scientifically based parenting advice. For example, the analysis unit integrates data on dietary content, sleep patterns, and emotional fluctuations and performs a comprehensive analysis using generative AI. The analysis unit can also use machine learning algorithms to identify correlations in the data. Furthermore, the analysis unit can perform statistical analysis of the data to provide scientifically based parenting advice. This makes it possible to support a child's health and growth by comprehensively analyzing a child's dietary content, sleep patterns, and emotional fluctuations and providing scientifically based parenting advice.

[0062] The observation unit observes the meal contents, and the generation AI analyzes the nutritional value of ingredients in the meal contents observed by the observation unit in real time, allowing for instant evaluation of nutritional balance. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record meal contents reported by parents using voice input. The observation unit can also record meal contents entered by parents using text input. The generation AI analyzes the nutritional value of ingredients in the meal contents observed by the observation unit in real time. For example, the generation AI analyzes images of ingredients photographed with a camera and evaluates nutritional value. The generation AI can also analyze meal contents recorded using voice input or text input and evaluate nutritional balance. In this way, by observing meal contents and the generation AI analyzing the nutritional value of ingredients in real time and instantly evaluating nutritional balance, a well-balanced meal can be provided.

[0063] The observation unit observes the meal content, and the generation AI can learn the child's meal preferences based on the meal content observed by the observation unit and generate an individually customized meal plan. The observation unit, for example, photographs the meal content with a camera and collects the data. The observation unit can also record the meal content reported by the parent using voice input. The observation unit can also record the meal content entered by the parent using text input. The generation AI learns the child's meal preferences based on the meal content observed by the observation unit. For example, the generation AI analyzes images of ingredients photographed with a camera to learn the child's preferences. The generation AI can also analyze the meal content recorded using voice input or text input to learn the child's preferences. In this way, the generation AI can observe the meal content, learn the child's meal preferences, and generate an individually customized meal plan, thereby improving the child's meal satisfaction.

[0064] The observation unit observes the meal contents, and the generation AI can observe the child's emotions using an emotion estimation function based on the meal contents observed by the observation unit and suggest a meal method that elicits positive emotions. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record the meal contents reported by the parent using voice input. The observation unit can also record the meal contents entered by the parent using text input. The generation AI observes the child's emotions using the emotion estimation function based on the meal contents observed by the observation unit. For example, the generation AI analyzes the child's facial expressions photographed with a camera and estimates emotions. The generation AI can also analyze the child's tone of voice recorded using voice input and estimate emotions. This makes it possible to improve the child's meal experience by observing the meal contents and using the emotion estimation function to observe the child's emotions and suggest a meal method that elicits positive emotions.

[0065] The observation unit observes meal contents, and the generation AI can make suggestions for home gardening and advice on how to select ingredients based on the observation data of the meal contents observed by the observation unit. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record the meal contents reported by parents using voice input. The observation unit can also record the meal contents entered by parents using text input. The generation AI makes suggestions for home gardening and advice on how to select ingredients based on the observation data of the meal contents observed by the observation unit. For example, the generation AI can suggest vegetables and fruits that should be grown in a home garden based on ingredients that children like. The generation AI can also give advice on how to select ingredients according to the season. In this way, by observing meal contents and the generation AI making suggestions for home gardening and advice on how to select ingredients based on the observation data, it can support ingredient selection and cultivation at home.

[0066] The observation unit observes meal contents, and the generation AI can add a community function that compares the meal contents observed by the observation unit with the meal data of other households and shares best practices. The observation unit, for example, photographs meal contents with a camera and collects the data. The observation unit can also record meal contents reported by parents using voice input. The observation unit can also record meal contents entered by parents using text input. The generation AI adds a community function that compares the meal contents observed by the observation unit with the meal data of other households and shares best practices. For example, the generation AI collects meal data of other households and analyzes that data to extract best practices. The generation AI can also add a community function and build a platform where parents can share meal ideas and recipes. This can promote information sharing among parents by adding a community function that observes meal contents, compares them with the meal data of other households, and shares best practices.

[0067] The observation unit observes the meal contents, and the generation AI uses the emotion estimation function to observe the parent's emotions based on the meal contents observed by the observation unit, and can suggest a mealtime experience that is enjoyable for both parent and child. The observation unit, for example, photographs the meal contents with a camera and collects the data. The observation unit can also record the meal contents reported by the parent using voice input. The observation unit can also record the meal contents entered by the parent using text input. The generation AI observes the parent's emotions using the emotion estimation function based on the meal contents observed by the observation unit. For example, the generation AI analyzes the parent's facial expressions photographed with a camera and estimates their emotions. The generation AI can also analyze the tone of the parent's voice recorded using voice input and estimate their emotions. This allows the generation AI to observe the meal contents, observe the parent's emotions using the emotion estimation function, and suggest a mealtime experience that is enjoyable for both parent and child, thereby improving the parent-child mealtime experience.

[0068] The observation unit observes sleep patterns, and the generation AI can analyze environmental factors in real time based on the sleep patterns observed by the observation unit and suggest an optimal sleep environment. The observation unit, for example, captures a child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The generation AI analyzes environmental factors in real time based on the sleep patterns observed by the observation unit. For example, the generation AI can measure the temperature in a child's bedroom with a sensor and analyze the data to suggest an optimal temperature. The generation AI can also measure the humidity in a child's bedroom with a sensor and analyze the data to suggest an optimal humidity level. This allows the generation AI to observe sleep patterns, analyze environmental factors in real time, and suggest an optimal sleep environment, thereby improving the quality of a child's sleep.

[0069] The observation unit observes sleep patterns, and the generation AI analyzes the child's movements during sleep based on the sleep patterns observed by the observation unit, thereby developing new indices for evaluating sleep quality. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also record the child's movements during sleep using a sensor. Furthermore, the observation unit can record sleep patterns reported by parents via voice input. The generation AI analyzes the child's movements during sleep based on the sleep patterns observed by the observation unit. For example, the generation AI can analyze movement data detected by a motion sensor to develop new indices for evaluating sleep quality. The generation AI can also analyze heart rate and breathing pattern data to develop new indices for evaluating sleep quality. This allows for more accurate evaluation of a child's sleep quality by observing sleep patterns, analyzing the child's movements during sleep, and developing new indices for evaluating sleep quality.

[0070] The observation unit observes the sleep patterns, and the generation AI uses an emotion estimation function to observe the child's emotions before and after sleep based on the sleep patterns observed by the observation unit, and can suggest a highly relaxing method for putting the child to sleep. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The generation AI uses the emotion estimation function to observe the child's emotions before and after sleep based on the sleep patterns observed by the observation unit. For example, the generation AI analyzes the child's facial expressions captured with a camera and estimates their emotions. The generation AI can also analyze the tone of the child's voice recorded via voice input and estimate their emotions. In this way, the quality of the child's sleep can be improved by observing the sleep patterns and using the emotion estimation function to observe the child's emotions before and after sleep and suggest a highly relaxing method for putting the child to sleep.

[0071] The observation unit observes sleep patterns, and the generation AI can provide parents with sleep improvement advice based on the sleep pattern data observed by the observation unit. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record sleep patterns reported by parents via voice input. The generation AI provides parents with sleep improvement advice based on the sleep pattern data observed by the observation unit. For example, the generation AI can analyze the child's sleep pattern data and suggest that parents and children sleep at the same rhythm. The generation AI can also make suggestions for improving the parent's bedroom environment. In this way, the generation AI can observe sleep patterns and provide parents with sleep improvement advice based on the sleep pattern data, thereby improving the quality of the parents' sleep.

[0072] The observation unit observes sleep patterns, and the generation AI can add a community function that compares the sleep patterns observed by the observation unit with the sleep data of other families and shares best practices. For example, the observation unit may capture a child's sleep patterns with a camera and collect the data. The observation unit may also use sensors to record the child's movements while sleeping. The observation unit may also record sleep patterns reported by parents via voice input. The generation AI can add a community function that compares the sleep patterns observed by the observation unit with the sleep data of other families and shares best practices. For example, the generation AI may collect sleep data from other families and analyze that data to extract best practices. The generation AI can also add a community function to build a platform where parents can share ideas and methods for improving sleep. This can encourage information sharing among parents by adding a community function that observes sleep patterns, compares them with the sleep data of other families, and shares best practices.

[0073] The observation unit observes sleep patterns, and the generation AI uses an emotion estimation function to comprehensively analyze the parent-child sleep patterns based on the sleep patterns observed by the observation unit and suggests ways to improve the sleep of the entire family. The observation unit, for example, captures the child's sleep patterns with a camera and collects the data. The observation unit can also use sensors to record the child's movements while sleeping. The observation unit can also record the sleep patterns reported by the parent via voice input. The generation AI uses the emotion estimation function to comprehensively analyze the parent-child sleep patterns based on the sleep patterns observed by the observation unit. For example, the generation AI analyzes the emotions of the parent and child before and after sleep and suggests ways to relax the entire family. The generation AI can also comprehensively analyze the parent-child sleep environment and make suggestions to improve the sleep environment of the entire family. This allows the generation AI to observe sleep patterns, comprehensively analyze the parent-child sleep patterns using the emotion estimation function, and suggest ways to improve the sleep of the entire family, thereby improving the quality of sleep for the entire family.

[0074] The observation unit observes emotional fluctuations, and the generation AI can analyze the child's facial expressions and tone of voice based on the emotional fluctuations observed by the observation unit, thereby identifying detailed patterns of emotional fluctuations. The observation unit, for example, captures the child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI analyzes the child's facial expressions and tone of voice based on the emotional fluctuations observed by the observation unit. For example, the generation AI analyzes the child's facial expressions captured with a camera and identifies patterns of emotional fluctuations. The generation AI can also analyze the child's tone of voice recorded via audio input and identify patterns of emotional fluctuations. In this way, by observing emotional fluctuations and analyzing the child's facial expressions and tone of voice to identify detailed patterns of emotional fluctuations, the child's emotional fluctuations can be more accurately understood.

[0075] The observation unit observes emotional fluctuations, and the generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit, identifies stress factors, and proposes a specific action plan for stress reduction. The observation unit, for example, captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit. For example, if the generation AI becomes emotionally unstable during a specific time period or situation, it can identify the cause. After identifying the stress factors, the generation AI can also propose a specific action plan. In this way, children's stress can be reduced by observing emotional fluctuations, identifying stress factors based on the emotional fluctuation data, and proposing a specific action plan for stress reduction.

[0076] The observation unit observes emotional fluctuations, and the generation AI uses an emotion estimation function based on the emotional fluctuations observed by the observation unit to observe the parent's reaction to the child's emotional fluctuations and provide advice to improve the parent-child relationship. The observation unit, for example, captures the child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's behavior using a sensor. The generation AI uses the emotion estimation function to observe the parent's reaction to the child's emotional fluctuations based on the emotional fluctuations observed by the observation unit. For example, the generation AI analyzes the parent's facial expression captured with a camera and observes the parent's reaction. The generation AI can also analyze the parent's tone of voice recorded with audio input and observe the parent's reaction. In this way, the parent-child relationship can be improved by observing emotional fluctuations and the generation AI using the emotion estimation function to observe the parent's reaction to the child's emotional fluctuations and provide advice to improve the parent-child relationship.

[0077] The observation unit observes emotional fluctuations, and the generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit, and can collaborate with schools and daycare centers to propose support in the educational setting. The observation unit, for example, captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI analyzes the emotional fluctuation data based on the emotional fluctuations observed by the observation unit. For example, if the generation AI becomes emotionally unstable at a specific time or in a specific situation, it can identify the cause. The generation AI can also collaborate with schools and daycare centers based on the emotional fluctuation data and propose support in the educational setting. This makes it possible to improve children's educational environment by observing emotional fluctuations and collaborating with schools and daycare centers based on the emotional fluctuation data to propose support in the educational setting.

[0078] The observation unit observes emotional fluctuations, and the generation AI compares the emotional fluctuations observed by the observation unit with the emotional data of other families, and can add a community function for sharing best practices. For example, the observation unit captures a child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using voice input. Furthermore, the observation unit can also record the child's behavior using a sensor. The generation AI compares the emotional fluctuations observed by the observation unit with the emotional data of other families and adds a community function for sharing best practices. For example, the generation AI collects emotional data from other families and analyzes that data to extract best practices. The generation AI can also add a community function to build a platform where parents can share countermeasures and methods for dealing with emotional fluctuations. This can promote information sharing among parents by adding a community function for observing emotional fluctuations, comparing them with the emotional data of other families, and sharing best practices.

[0079] The observation unit observes emotional fluctuations, and the generation AI can observe the parent's emotional fluctuations using an emotion estimation function based on the emotional fluctuations observed by the observation unit, and suggest activities that promote emotional sharing between parent and child. The observation unit, for example, captures the child's emotional fluctuations with a camera and collects the data. The observation unit can also record the child's tone of voice using audio input. The observation unit can also record the child's movements using a sensor. The generation AI also observes the parent's emotional fluctuations using the emotion estimation function based on the emotional fluctuations observed by the observation unit. For example, the generation AI analyzes the parent's facial expression captured with a camera and estimates their emotion. The generation AI can also analyze the parent's tone of voice recorded via audio input and estimate their emotion. In this way, the generation AI can observe emotional fluctuations, observe the parent's emotional fluctuations using the emotion estimation function, and suggest activities that promote emotional sharing between parent and child, thereby deepening the bond between parent and child.

[0080] Based on the collected data, the analysis unit allows the generating AI to analyze the data collected by the analysis unit, refer to the latest childcare research, and provide advice based on scientific evidence. The analysis unit, for example, integrates the collected data and performs a comprehensive analysis using the generating AI. The analysis unit can also grasp correlations in the data using machine learning algorithms. Furthermore, the analysis unit can perform statistical analysis of the data, refer to the latest childcare research, and provide advice based on scientific evidence. In this way, the generating AI can refer to the latest childcare research based on the collected data and provide advice based on scientific evidence, making it possible to provide highly reliable childcare advice.

[0081] The analysis unit generates a childcare plan according to the child's developmental stage, and the generation AI can periodically update the childcare plan generated by the analysis unit. For example, the analysis unit collects data according to the child's developmental stage and generates an individual childcare plan using the generation AI. The analysis unit can also analyze data according to the child's developmental stage using a machine learning algorithm to generate a childcare plan. Furthermore, the generation AI can periodically update the childcare plan to reflect the latest data. In this way, by generating a childcare plan according to the child's developmental stage and periodically updating it, it is possible to provide optimal childcare advice tailored to the child's development.

[0082] The analysis unit can use the emotion estimation function to evaluate the effectiveness of the parenting advice and strengthen the advice that will bring about positive results. The analysis unit, for example, uses the emotion estimation function to evaluate the effectiveness of the parenting advice. The analysis unit can also analyze the effectiveness of the parenting advice using a machine learning algorithm and strengthen the advice that will bring about positive results. Furthermore, the analysis unit can perform statistical analysis of the data to evaluate the effectiveness of the parenting advice. In this way, by using the emotion estimation function to evaluate the effectiveness of the parenting advice and strengthening the advice that will bring about positive results, it is possible to provide more effective parenting advice.

[0083] The analysis unit can compare the parenting advice with parenting methods from different cultural spheres and regions and provide advice from a global perspective. For example, the analysis unit collects parenting methods from different cultural spheres and regions and analyzes the data using generative AI. The analysis unit can also use machine learning algorithms to compare parenting methods from different cultural spheres and regions and provide advice from a global perspective. Furthermore, the analysis unit can perform statistical analysis of the data to compare parenting methods from different cultural spheres and regions. This makes it possible to provide advice that incorporates diverse parenting methods by comparing parenting advice with parenting methods from different cultural spheres and regions and providing advice from a global perspective.

[0084] The analysis unit can add a community function that compares the childcare data with that of other families and shares best practices. The analysis unit, for example, collects childcare data from other families and analyzes the data using generative AI. The analysis unit can also use machine learning algorithms to compare the childcare data of other families and extract best practices. Furthermore, the analysis unit can perform statistical analysis of the data and compare the childcare data of other families. This can promote information sharing among parents by adding a community function that compares the childcare data with that of other families and shares best practices.

[0085] The analysis unit can use the emotion estimation function to provide parenting advice that takes into account the parent's emotions, thereby reducing parental stress. The analysis unit, for example, uses the emotion estimation function to analyze the parent's emotions and provides parenting advice based on the data. The analysis unit can also analyze the parent's emotion data using a machine learning algorithm and provide parenting advice to reduce stress. Furthermore, the analysis unit can perform statistical analysis of the data and provide parenting advice that takes into account the parent's emotions. In this way, the parenting burden on parents can be reduced by providing parenting advice that takes into account the parent's emotions using the emotion estimation function and reducing parental stress.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The robot-type AI service can further include a music providing unit. The music providing unit can select and play appropriate music based on the child's emotions and sleep patterns. For example, when the child is relaxed, it can play calm classical music. When the child is active, it can play upbeat pop music. Furthermore, it can play a lullaby before the child falls asleep. In this way, the music providing unit can provide music according to the child's emotions and sleep patterns, thereby improving the child's emotional stability and sleep quality.

[0088] The robot-type AI service can further include an exercise suggestion unit. The exercise suggestion unit can suggest appropriate exercises based on a child's sleep patterns and emotional fluctuations. For example, if a child has been sitting for a long time, it can suggest light stretching or exercises. If a child is feeling stressed, it can suggest yoga, which has a relaxing effect. Furthermore, if a child has too much energy, it can suggest outdoor play or sports. In this way, the exercise suggestion unit can support a child's health and emotional stability.

[0089] The robot-type AI service can further include a learning support unit. The learning support unit can adjust the timing and content of learning based on the child's emotions and fluctuations in concentration. For example, when the child is concentrating, it can provide a more difficult task. Also, when the child is tired, it can provide a simple review or a relaxing activity. Furthermore, it can provide learning content related to the child's areas of interest. In this way, the learning support unit can maximize the child's learning effect and stimulate their interest in learning.

[0090] The robot-type AI service can further include a communication support unit. The communication support unit can suggest appropriate communication methods based on the child's emotions and behavior. For example, if a child feels anxious, it can suggest kind words or a hug to the parent. If a child is excited, it can also suggest calmly listening to what the parent has to say. Furthermore, if a child feels lonely, it can suggest time to play together. In this way, the communication support unit can deepen the bond between parent and child and stabilize the child's emotions.

[0091] The robot-type AI service can further be equipped with a health management unit. The health management unit can monitor a child's health condition based on their diet and sleep patterns, and recommend a medical visit if necessary. For example, if a child's diet is unbalanced, it can suggest a consultation with a nutritionist. Also, if a child's sleep pattern is disrupted, it can suggest a visit to a pediatrician. Furthermore, if a child is not feeling well, it can suggest an early visit to a doctor. In this way, the health management unit can provide comprehensive support for a child's health.

[0092] The robot-type AI service may further include an environmental monitoring unit. The environmental monitoring unit may monitor the child's living environment and make appropriate suggestions for improving the environment. For example, it may monitor the temperature and humidity in the room and suggest adjusting the temperature and humidity to an appropriate level. It may also monitor the lighting and sound levels in the room and suggest appropriate lighting and sound environments. It may also monitor the air quality and suggest using an air purifier. In this way, the environmental monitoring unit may optimize the child's living environment and improve their health and comfort.

[0093] The robot-type AI service can further include a schedule management unit. The schedule management unit can manage a child's activity schedule and suggest a balanced daily life. For example, it can suggest a balanced allocation of study time, play time, meal time, and sleep time. It can also manage schedules for special events and activities and send reminders to parents. Furthermore, it can adjust the schedule according to the child's growth and suggest an optimal daily life. In this way, the schedule management unit can regulate a child's daily rhythm and support healthy growth.

[0094] The robot-type AI service can further be equipped with a safety management unit. The safety management unit can make suggestions to ensure children's safety. For example, it can identify dangerous places and objects in the home and suggest safety measures. It can also suggest safety measures when going out. Furthermore, in the event of an emergency, it can send an alert to parents to prompt them to take action quickly. In this way, the safety management unit can provide comprehensive support for children's safety.

[0095] The robot-type AI service can further include a social support unit. The social support unit can suggest activities to foster a child's social skills. For example, it can suggest playtime with friends or group activities. It can also provide games and activities to improve communication skills. It can also suggest events and workshops that parents and children can participate in together. In this way, the social support unit can foster a child's social skills and support them in building healthy relationships.

[0096] The robot-type AI service may further include a creativity support unit. The creativity support unit may suggest activities to bring out a child's creativity. For example, it may suggest activities such as drawing, crafting, or making music. It may also suggest creative projects based on the child's interests. It may also suggest creative activities that parents and children can enjoy together. In this way, the creativity support unit can foster a child's creativity and provide opportunities for self-expression.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The observation unit observes the child's diet. For example, the observation unit may use a camera to take pictures of the food the child eats and collect the data. The observation unit may also record the dietary information reported by the parent using voice input. Furthermore, the observation unit may also record the dietary information entered by the parent using text input. Step 2: The analysis unit analyzes the dietary content observed by the observation unit. For example, the analysis unit may use generative AI (e.g., text generation AI or multimodal generation AI) to analyze the nutritional value of ingredients in real time. The analysis unit may also use machine learning algorithms to evaluate the nutritional balance of the dietary content. Furthermore, the analysis unit may perform statistical analysis of the data to identify trends in dietary content. Step 3: The advice providing unit provides childcare advice based on the data analyzed by the analysis unit. For example, the advice providing unit suggests a balanced diet. The advice providing unit can also present ideas for enjoyable mealtimes. Furthermore, the advice providing unit can also suggest improvements to the diet.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 observation section to observe children's dietary habits, an analysis unit that analyzes the meal contents observed by the observation unit; an advice providing unit that provides child-rearing advice based on the data analyzed by the analysis unit. A system characterized by:

2. The observation unit is Monitor your child's sleep patterns The analysis unit analyzing the sleep pattern observed by the observation unit; Providing parenting advice The system of claim 1 .

3. The observation unit is Observe the child's emotional fluctuations The analysis unit analyzing the emotional fluctuations observed by the observation unit; Providing parenting advice The system of claim 1 .

4. The analysis unit Comprehensively analyze the dietary content, sleep patterns, and emotional fluctuations, Providing evidence-based parenting advice The system of claim 1 .

5. The observation unit is Observe the dietary content, The generating AI is Analyzing the nutritional values ​​of ingredients of the meal contents observed by the observation unit in real time; Instantly assess nutritional balance The system of claim 1 .

Images