system

Abstract

The system according to this embodiment aims to grasp a child's emotional state in real time and propose appropriate responses. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a dialogue unit. The collection unit collects biometric information such as the child's facial expressions, tone of voice, body movements, and heart rate. The analysis unit analyzes the biometric information collected by the collection unit to grasp the child's emotional state in real time. The provision unit makes specific suggestions for action to parents and teachers based on the analysis results obtained by the analysis unit. The dialogue unit engages in natural dialogue with the child to build a relationship of trust.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to grasp a child's emotional state in real time and take appropriate measures.

[0005] The system according to the embodiment aims to grasp a child's emotional state in real time and propose appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a dialogue unit. The data collection unit collects biometric information such as the child's facial expressions, tone of voice, body movements, and heart rate. The analysis unit analyzes the biometric information collected by the data collection unit to understand the child's emotional state in real time. The data provision unit makes specific suggestions for action to parents and teachers based on the analysis results obtained by the analysis unit. The dialogue unit engages in natural dialogue with the child to build a relationship of trust. [Effects of the Invention]

[0007] The system according to this embodiment can grasp a child's emotional state in real time and suggest appropriate responses. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The plush toy system according to an embodiment of the present invention is a system that grasps a child's emotional state in real time by collecting biometric information such as a child's facial expressions, tone of voice, body movements, and heart rate, and analyzing it with a multimodal AI. For example, the plush toy system collects biometric information such as a child's facial expressions, tone of voice, body movements, and heart rate, and analyzes it with a multimodal AI to grasp the child's emotional state in real time. Next, the multimodal AI analyzes the collected biometric information to grasp the child's emotional state in real time. Furthermore, an AI chatbot engages in natural dialogue with the child to build a relationship of trust. This provides a safe environment for children to express their emotions and promotes self-understanding. A detailed emotional state report is also generated, and specific suggestions for action are provided to parents and teachers. For example, parents can improve their understanding of their child's emotions and communicate more effectively. Teachers can grasp the emotional trends of the entire class and provide individually optimized instruction. Furthermore, the multimodal AI utilizes technologies such as image recognition, speech recognition, natural language processing, and biometric data analysis, and is equipped with advanced privacy protection technology. This allows for 24-hour monitoring and continuous analysis of a child's emotional state. For example, if a child is being bullied at school, the AI ​​can detect early signs and suggest appropriate responses to parents and teachers. This is expected to significantly reduce bullying and school absenteeism. It also contributes to the individualization of education, strengthening of family relationships, and the democratization of mental health care. For instance, if a child is stressed, the AI ​​can identify the cause and provide advice on how to relax. This allows children to deepen their self-understanding and improve their mental health. In this way, the plush toy system can grasp a child's emotional state in real time and suggest appropriate responses.

[0029] The stuffed animal system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a dialogue unit. The collection unit collects biometric information such as a child's facial expressions, voice tone, body movements, and heart rate. For example, the collection unit can capture a child's facial expressions with a camera and save them as image data. The collection unit can also record a child's voice tone with a microphone and save it as audio data. Furthermore, the collection unit can detect a child's body movements with a motion sensor and collect movement data. For example, the collection unit can measure a child's heart rate with a heart rate sensor and save it as heart rate data. The analysis unit analyzes the biometric information collected by the collection unit to grasp the child's emotional state in real time. For example, the analysis unit can analyze a child's facial expressions using image recognition technology and estimate their emotional state. The analysis unit can also analyze a child's voice tone using voice recognition technology and estimate their emotional state. Furthermore, the analysis unit can analyze motion sensor data and estimate the emotional state from the child's body movements. For example, the analysis unit analyzes heart rate data and estimates the child's emotional state from the fluctuations in their heart rate. The service unit provides specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, the service unit can notify parents of the child's emotional state based on the analysis results and suggest appropriate responses. The service unit can also notify teachers of the child's emotional state based on the analysis results and suggest individually optimized instruction. Furthermore, the service unit can generate a detailed emotional state report and provide specific response suggestions to parents and teachers. For example, the service unit can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. The dialogue unit uses an AI chatbot to engage in natural conversations with children and build trust. For example, the dialogue unit can understand the child's emotional state through conversations and take appropriate responses. The dialogue unit can also promote the child's emotional expression and deepen their self-understanding through conversations. Furthermore, the dialogue unit can notify parents and teachers of the child's emotional state through conversations and suggest appropriate responses. For example, the dialogue department uses dialogue with children to understand their emotional state in real time and build a relationship of trust.As a result, the plush toy system according to this embodiment can grasp the child's emotional state in real time and suggest appropriate responses.

[0030] The data collection unit collects biometric information such as a child's facial expressions, voice tone, body movements, and heart rate. For example, the unit can capture a child's facial expressions with a camera and save them as image data. The camera has high resolution and can capture even subtle changes in facial expressions. The unit can also record a child's voice tone with a microphone and save it as audio data. The microphone is highly sensitive and can accurately capture subtle changes in a child's voice and nuances of emotion. Furthermore, the unit can detect a child's body movements with a motion sensor and collect movement data. The motion sensor records the speed, direction, and intensity of the child's movements in detail, providing data for analyzing movement patterns. For example, the unit can measure a child's heart rate with a heart rate sensor and save it as heart rate data. The heart rate sensor can accurately measure heart rate in real time by making direct contact with the child's skin. This allows the unit to collect biometric information from multiple angles and provide detailed data. Furthermore, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes biometric information collected by the data collection unit to understand the child's emotional state in real time. For example, the analysis unit uses image recognition technology to analyze the child's facial expressions and estimate their emotional state. Image recognition technology can detect facial feature points and analyze subtle changes in facial expressions to estimate emotions such as joy, sadness, anger, and surprise with high accuracy. The analysis unit can also use speech recognition technology to analyze the tone of the child's voice and estimate their emotional state. Speech recognition technology can analyze features such as pitch, volume, and rhythm of the voice to capture nuances of emotion. Furthermore, the analysis unit can analyze motion sensor data to estimate the child's emotional state from their body movements. Motion sensor data can estimate emotions such as excitement and relaxation by analyzing movement patterns and intensity. For example, the analysis unit analyzes heart rate data to estimate the child's emotional state from fluctuations in their heart rate. Fluctuations in heart rate reflect emotional states such as stress, excitement, and relaxation, and by analyzing this, the child's emotional state can be understood in real time. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term emotional trends and patterns. This allows the analysis unit to quickly and accurately analyze the collected data and understand the child's emotional state in real time.

[0032] The service provider makes specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, the service provider can notify parents of their child's emotional state based on the analysis results and suggest appropriate responses. Notifications are made via smartphone apps, email, SMS, etc., allowing parents to understand their child's emotional state in real time. The service provider can also notify teachers of their child's emotional state based on the analysis results and suggest individually optimized instruction. Teachers can select instruction methods that match the child's emotional state and provide more effective education. Furthermore, the service provider can generate detailed emotional state reports and make specific response suggestions to parents and teachers. The reports include changes and trends in the child's emotional state and specific response methods, which parents and teachers can use as a reference to take appropriate action. For example, the service provider can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. In this way, the service provider can provide parents and teachers with specific and practical information and support appropriate responses according to the child's emotional state. Furthermore, the service provider can collect feedback from parents and teachers and continuously improve the accuracy and effectiveness of the suggestions. This allows the service provider to offer highly accurate solutions based on the latest information, supporting the growth and development of children.

[0033] The dialogue unit uses an AI chatbot to engage in natural conversations with children and build trust. For example, the dialogue unit can understand children's emotional states through conversations and respond appropriately. The AI ​​chatbot uses natural language processing technology to understand children's statements and generate appropriate responses. The dialogue unit can also facilitate children's emotional expression and deepen their self-understanding through conversations. The AI ​​chatbot provides an environment where children can easily express their emotions by offering empathetic and encouraging words in response to what they say. Furthermore, the dialogue unit can notify parents and teachers of children's emotional states through conversations and suggest appropriate responses. For example, the dialogue unit can understand children's emotional states in real time through conversations and build trust. The AI ​​chatbot can analyze children's statements and actions to estimate their emotional states and take appropriate responses. This allows the dialogue unit to build trust with children and understand their emotional states in real time. Furthermore, the dialogue unit can continuously monitor children's emotional states through conversations and notify parents and teachers as needed. This allows the dialogue unit to understand the child's emotional state in real time and respond appropriately.

[0034] The data collection unit can analyze a child's past biometric information and select the optimal data collection method. For example, if the data collection unit finds that emotional fluctuations are significant during a specific time period based on past data, it will focus on collecting data during that time period. Furthermore, if the data collection unit finds that emotional fluctuations are significant during a specific activity based on past data, it can focus on collecting data during that activity. Additionally, if the data collection unit finds that emotional fluctuations are significant under a specific environment based on past data, it can focus on collecting data under that environment. For example, the data collection unit selects the optimal data collection method based on past biometric information. This allows for the selection of the optimal data collection method based on past biometric information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input past biometric information into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter biometric data based on the child's current activity level and environment. For example, if the child is exercising, the unit can prioritize collecting heart rate and body movement data and filter out other data. Similarly, if the child is quietly reading, the unit can prioritize collecting facial expression and voice tone data and filter out other data. Furthermore, if the child is outdoors, the unit can filter data considering ambient sounds and surrounding conditions. For example, the unit collects appropriate biometric data according to the child's activity level and environment. This allows for the collection of appropriate biometric data based on the child's activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the child's activity level and environment into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant information by considering the child's geographical location when collecting biometric data. For example, if the child is at school, the data collection unit can prioritize the collection of data on stress and concentration during learning. Similarly, if the child is in a park, the data collection unit can prioritize the collection of data on excitement and enjoyment during play. Furthermore, if the child is at home, the data collection unit can prioritize the collection of data on relaxation and emotions within the home. For example, the data collection unit can collect highly relevant biometric data based on the child's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the child's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0037] The data collection unit can analyze a child's social media activity and collect relevant information when collecting biometric data. For example, the data collection unit can estimate a child's emotions from the content of their social media posts and collect relevant biometric data. The data collection unit can also estimate a child's emotions from their interactions with friends on social media and collect relevant biometric data. Furthermore, the data collection unit can estimate a child's emotions based on the time of day they are active on social media and collect relevant biometric data. For example, the data collection unit can collect relevant biometric data based on a child's social media activity. This allows the data collection unit to collect relevant biometric data based on a child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's social media activity into a generating AI and have the generating AI collect relevant information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information during the analysis. For example, if there is a large fluctuation in heart rate, the analysis unit can perform a detailed analysis to identify the cause of the emotional fluctuation. The analysis unit can also perform a detailed analysis to identify the type of emotion if there is a significant change in facial expression. Furthermore, if there is a large change in voice tone, the analysis unit can perform a detailed analysis to identify the intensity of the emotion. For example, the analysis unit optimizes the level of detail of the analysis based on the importance of the biometric information. This allows the level of detail of the analysis to be optimized according to the importance of the biometric information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the biometric information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis methods depending on the category of biometric information during analysis. For example, the analysis unit can apply a method that analyzes temporal variations to heart rate data. It can also apply an analysis method using image recognition technology to facial expression data. Furthermore, it can apply an analysis method using speech recognition technology to voice tone data. For example, the analysis unit applies an appropriate analysis method depending on the category of biometric information. This allows for the application of an appropriate analysis method depending on the category of biometric information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of biometric information into a generating AI and have the generating AI execute the application of different analysis methods.

[0040] The analysis unit can determine the priority of analysis based on the timing of biometric data collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected data to understand the latest emotional state. It can also prioritize the analysis of data before and after a specific event to understand emotional fluctuations. Furthermore, the analysis unit can analyze data over a long period to understand emotional trends. For example, the analysis unit can optimize the priority of analysis based on the timing of biometric data collection. This allows for the optimization of analysis priority based on the timing of biometric data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of biometric data collection into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relationships between biometric data during the analysis process. For example, if heart rate and changes in facial expression are related, the analysis unit will prioritize analyzing these data. Similarly, if voice tone and body movements are related, the analysis unit can prioritize analyzing these data. Furthermore, if facial expressions and changes in heart rate are related, the analysis unit can prioritize analyzing these data. For example, the analysis unit optimizes the order of analysis based on the relationships between biometric data. This allows for the optimization of the analysis order based on the relationships between biometric data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between biometric data into a generating AI and have the generating AI adjust the order of analysis.

[0042] The service provider can adjust the level of detail in a proposal based on the importance of the analysis results. For example, the service provider can provide a detailed proposal for important analysis results. Conversely, it can provide a concise proposal for general analysis results. Furthermore, it can provide a concise proposal for urgent analysis results to allow for a quick response. For example, the service provider can optimize the level of detail in a proposal based on the importance of the analysis results. This allows for the optimization of proposal detail according to the importance of the analysis results. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the analysis results into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0043] The service provider can apply different suggestion algorithms depending on the category of the analysis results when making suggestions. For example, the service provider can apply an algorithm that suggests relaxation methods to analysis results related to stress. It can also apply an algorithm that suggests positive activities to analysis results related to joy. Furthermore, it can apply an algorithm that suggests ways to calm down to analysis results related to anger. For example, the service provider applies an appropriate suggestion algorithm depending on the category of the analysis results. This ensures that an appropriate suggestion algorithm is applied according to the category of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the analysis results into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0044] The service provider can determine the priority of proposals based on the timing of the submission of analysis results. For example, the service provider may prioritize proposals based on recent analysis results. It may also prioritize proposals based on analysis results before and after a specific event. Furthermore, it may prioritize proposals based on analysis results over a long period. For example, the service provider can optimize the priority of proposals based on the timing of the submission of analysis results. This allows for the optimization of proposal priority based on the timing of the submission of analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the timing of the submission of analysis results into a generating AI and have the generating AI determine the priority of proposals.

[0045] The service provider can adjust the order of suggestions based on the relevance of the analysis results when making suggestions. For example, the service provider may prioritize suggesting analysis results related to stress. It can also prioritize suggesting analysis results related to joy. Furthermore, it can prioritize suggesting analysis results related to anger. For example, the service provider can optimize the order of suggestions based on the relevance of the analysis results. This allows for the optimization of the suggestion order based on the relevance of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0046] The dialogue unit can select the optimal dialogue method by referring to the child's past dialogue history during a conversation. For example, the dialogue unit can prioritize topics the child likes from past dialogue history. It can also select a speaking style that makes the child feel relaxed from past dialogue history. Furthermore, it can select topics that the child is interested in from past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on past dialogue history. This allows the dialogue unit to select the optimal dialogue method based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0047] The dialogue unit can select the optimal dialogue method while considering the child's geographical location information. For example, if the child is at school, the dialogue unit can engage in dialogue on topics related to learning. If the child is in a park, the dialogue unit can engage in dialogue on topics related to play. Furthermore, if the child is at home, the dialogue unit can engage in dialogue on topics related to home. For example, the dialogue unit selects the optimal dialogue method based on the child's geographical location information. This allows the dialogue unit to select the optimal dialogue method based on the child's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's geographical location information into a generating AI and have the generating AI select the optimal dialogue method.

[0048] The dialogue unit can analyze a child's social media activity during a conversation and suggest content for that conversation. For example, the dialogue unit can suggest topics that the child might be interested in based on their social media posts. It can also suggest content based on the child's interactions with friends on social media. Furthermore, the dialogue unit can suggest content based on the time of day the child is active on social media. For example, the dialogue unit can optimize the content of the conversation based on the child's social media activity. This allows for the optimization of the conversation content based on the child's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's social media activity into a generating AI and have the generating AI suggest content for the conversation.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The plush toy system can also be equipped with a learning support unit that monitors the child's learning progress and provides feedback tailored to their learning situation. For example, when a child is doing homework, the learning support unit can evaluate the accuracy and understanding of their answers and provide appropriate advice. Furthermore, if a child is learning a new concept, the learning support unit can evaluate their understanding in real time and provide additional explanations or practice problems as needed. In addition, the learning support unit can analyze the child's learning patterns and suggest an optimal learning schedule. This allows the plush toy system to support the child's learning and maximize learning effectiveness.

[0051] The plush toy system can also include a health management unit that monitors the child's health and provides information useful for health management. For example, the health management unit can record the child's diet and evaluate its nutritional balance. It can also monitor the child's exercise level and suggest appropriate exercise plans. Furthermore, it can analyze the child's sleep patterns and provide advice to promote quality sleep. In this way, the plush toy system can comprehensively support the child's health and promote healthy growth.

[0052] The plush toy system can also include a creative support department to further foster children's creativity through creative activities. For example, the creative support department can offer advice on color selection and composition when children are drawing. It can also provide hints for story development and character design when children are creating stories. Furthermore, it can offer ideas for melody and rhythm when children are creating music. In this way, the plush toy system can draw out children's creativity and cultivate their rich expressive abilities.

[0053] The plush toy system can also be equipped with a social skills support section to further foster children's social skills. For example, the social skills support section can advise children on appropriate communication methods when playing with friends. It can also provide support to help children develop cooperation and leadership skills when participating in group activities. Furthermore, the social skills support section can suggest problem-solving methods when children face difficult situations. In this way, the plush toy system can cultivate children's social skills and develop their ability to build smooth interpersonal relationships.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The data collection unit collects biometric information such as the child's facial expressions, voice tone, body movements, and heart rate. For example, the data collection unit can capture the child's facial expressions with a camera and save them as image data. It can also record the child's voice tone with a microphone and save it as audio data. Furthermore, it can detect the child's body movements with a motion sensor and collect movement data. It measures the child's heart rate with a heart rate sensor and saves it as heart rate data. Step 2: The analysis unit analyzes the biometric information collected by the collection unit to understand the child's emotional state in real time. For example, it can use image recognition technology to analyze the child's facial expressions and estimate their emotional state. It can also use speech recognition technology to analyze the tone of the child's voice and estimate their emotional state. Furthermore, it can analyze motion sensor data to estimate the emotional state from the child's body movements. It can also analyze heart rate data to estimate the emotional state from the fluctuations in the child's heart rate. Step 3: The service provider makes specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, it can notify parents of the child's emotional state based on the analysis results and suggest appropriate responses. It can also notify teachers of the child's emotional state based on the analysis results and suggest individually optimized instruction. Furthermore, it can generate a detailed emotional state report and make specific response suggestions to parents and teachers. For example, it can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. Step 4: The dialogue department uses an AI chatbot to engage in natural conversations with children and build trust. For example, through dialogue with children, it can understand their emotional state and respond appropriately. It can also facilitate children's emotional expression and deepen their self-understanding. Furthermore, through dialogue with children, it can notify parents and teachers of the children's emotional state and suggest appropriate responses. For example, it can understand the children's emotional state in real time through dialogue and build trust.

[0056] (Example of form 2) The plush toy system according to an embodiment of the present invention is a system that grasps a child's emotional state in real time by collecting biometric information such as a child's facial expressions, tone of voice, body movements, and heart rate, and analyzing it with a multimodal AI. For example, the plush toy system collects biometric information such as a child's facial expressions, tone of voice, body movements, and heart rate, and analyzes it with a multimodal AI to grasp the child's emotional state in real time. Next, the multimodal AI analyzes the collected biometric information to grasp the child's emotional state in real time. Furthermore, an AI chatbot engages in natural dialogue with the child to build a relationship of trust. This provides a safe environment for children to express their emotions and promotes self-understanding. A detailed emotional state report is also generated, and specific suggestions for action are provided to parents and teachers. For example, parents can improve their understanding of their child's emotions and communicate more effectively. Teachers can grasp the emotional trends of the entire class and provide individually optimized instruction. Furthermore, the multimodal AI utilizes technologies such as image recognition, speech recognition, natural language processing, and biometric data analysis, and is equipped with advanced privacy protection technology. This allows for 24-hour monitoring and continuous analysis of a child's emotional state. For example, if a child is being bullied at school, the AI ​​can detect early signs and suggest appropriate responses to parents and teachers. This is expected to significantly reduce bullying and school absenteeism. It also contributes to the individualization of education, strengthening of family relationships, and the democratization of mental health care. For instance, if a child is stressed, the AI ​​can identify the cause and provide advice on how to relax. This allows children to deepen their self-understanding and improve their mental health. In this way, the plush toy system can grasp a child's emotional state in real time and suggest appropriate responses.

[0057] The stuffed animal system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a dialogue unit. The collection unit collects biometric information such as a child's facial expressions, voice tone, body movements, and heart rate. For example, the collection unit can capture a child's facial expressions with a camera and save them as image data. The collection unit can also record a child's voice tone with a microphone and save it as audio data. Furthermore, the collection unit can detect a child's body movements with a motion sensor and collect movement data. For example, the collection unit can measure a child's heart rate with a heart rate sensor and save it as heart rate data. The analysis unit analyzes the biometric information collected by the collection unit to grasp the child's emotional state in real time. For example, the analysis unit can analyze a child's facial expressions using image recognition technology and estimate their emotional state. The analysis unit can also analyze a child's voice tone using voice recognition technology and estimate their emotional state. Furthermore, the analysis unit can analyze motion sensor data and estimate the emotional state from the child's body movements. For example, the analysis unit analyzes heart rate data and estimates the child's emotional state from the fluctuations in their heart rate. The service unit provides specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, the service unit can notify parents of the child's emotional state based on the analysis results and suggest appropriate responses. The service unit can also notify teachers of the child's emotional state based on the analysis results and suggest individually optimized instruction. Furthermore, the service unit can generate a detailed emotional state report and provide specific response suggestions to parents and teachers. For example, the service unit can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. The dialogue unit uses an AI chatbot to engage in natural conversations with children and build trust. For example, the dialogue unit can understand the child's emotional state through conversations and take appropriate responses. The dialogue unit can also promote the child's emotional expression and deepen their self-understanding through conversations. Furthermore, the dialogue unit can notify parents and teachers of the child's emotional state through conversations and suggest appropriate responses. For example, the dialogue department uses dialogue with children to understand their emotional state in real time and build a relationship of trust.As a result, the plush toy system according to this embodiment can grasp the child's emotional state in real time and suggest appropriate responses.

[0058] The data collection unit collects biometric information such as a child's facial expressions, voice tone, body movements, and heart rate. For example, the unit can capture a child's facial expressions with a camera and save them as image data. The camera has high resolution and can capture even subtle changes in facial expressions. The unit can also record a child's voice tone with a microphone and save it as audio data. The microphone is highly sensitive and can accurately capture subtle changes in a child's voice and nuances of emotion. Furthermore, the unit can detect a child's body movements with a motion sensor and collect movement data. The motion sensor records the speed, direction, and intensity of the child's movements in detail, providing data for analyzing movement patterns. For example, the unit can measure a child's heart rate with a heart rate sensor and save it as heart rate data. The heart rate sensor can accurately measure heart rate in real time by making direct contact with the child's skin. This allows the unit to collect biometric information from multiple angles and provide detailed data. Furthermore, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0059] The analysis unit analyzes biometric information collected by the data collection unit to understand the child's emotional state in real time. For example, the analysis unit uses image recognition technology to analyze the child's facial expressions and estimate their emotional state. Image recognition technology can detect facial feature points and analyze subtle changes in facial expressions to estimate emotions such as joy, sadness, anger, and surprise with high accuracy. The analysis unit can also use speech recognition technology to analyze the tone of the child's voice and estimate their emotional state. Speech recognition technology can analyze features such as pitch, volume, and rhythm of the voice to capture nuances of emotion. Furthermore, the analysis unit can analyze motion sensor data to estimate the child's emotional state from their body movements. Motion sensor data can estimate emotions such as excitement and relaxation by analyzing movement patterns and intensity. For example, the analysis unit analyzes heart rate data to estimate the child's emotional state from fluctuations in their heart rate. Fluctuations in heart rate reflect emotional states such as stress, excitement, and relaxation, and by analyzing this, the child's emotional state can be understood in real time. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term emotional trends and patterns. This allows the analysis unit to quickly and accurately analyze the collected data and understand the child's emotional state in real time.

[0060] The service provider makes specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, the service provider can notify parents of their child's emotional state based on the analysis results and suggest appropriate responses. Notifications are made via smartphone apps, email, SMS, etc., allowing parents to understand their child's emotional state in real time. The service provider can also notify teachers of their child's emotional state based on the analysis results and suggest individually optimized instruction. Teachers can select instruction methods that match the child's emotional state and provide more effective education. Furthermore, the service provider can generate detailed emotional state reports and make specific response suggestions to parents and teachers. The reports include changes and trends in the child's emotional state and specific response methods, which parents and teachers can use as a reference to take appropriate action. For example, the service provider can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. In this way, the service provider can provide parents and teachers with specific and practical information and support appropriate responses according to the child's emotional state. Furthermore, the service provider can collect feedback from parents and teachers and continuously improve the accuracy and effectiveness of the suggestions. This allows the service provider to offer highly accurate solutions based on the latest information, supporting the growth and development of children.

[0061] The dialogue unit uses an AI chatbot to engage in natural conversations with children and build trust. For example, the dialogue unit can understand children's emotional states through conversations and respond appropriately. The AI ​​chatbot uses natural language processing technology to understand children's statements and generate appropriate responses. The dialogue unit can also facilitate children's emotional expression and deepen their self-understanding through conversations. The AI ​​chatbot provides an environment where children can easily express their emotions by offering empathetic and encouraging words in response to what they say. Furthermore, the dialogue unit can notify parents and teachers of children's emotional states through conversations and suggest appropriate responses. For example, the dialogue unit can understand children's emotional states in real time through conversations and build trust. The AI ​​chatbot can analyze children's statements and actions to estimate their emotional states and take appropriate responses. This allows the dialogue unit to build trust with children and understand their emotional states in real time. Furthermore, the dialogue unit can continuously monitor children's emotional states through conversations and notify parents and teachers as needed. This allows the dialogue unit to understand the child's emotional state in real time and respond appropriately.

[0062] The data collection unit can estimate a child's emotions and adjust the frequency of biometric data collection based on the estimated emotions. For example, if a child is excited, the data collection unit can collect heart rate and body movement data at a high frequency to understand detailed emotional changes. Conversely, if a child is relaxed, the data collection unit can set a lower collection frequency to collect only the minimum necessary data. Furthermore, if a child is stressed, the data collection unit can collect facial expression and voice tone data at a high frequency to identify the cause of stress. For example, the data collection unit can optimize the frequency of biometric data collection according to the child's emotional state. This allows for the optimization of biometric data collection frequency according to the child's emotional state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, to estimate a child's emotional state, the data collection unit can input the collected biometric data into a generating AI and have the generating AI perform the emotional state estimation.

[0063] The data collection unit can analyze a child's past biometric information and select the optimal data collection method. For example, if the data collection unit finds that emotional fluctuations are significant during a specific time period based on past data, it will focus on collecting data during that time period. Furthermore, if the data collection unit finds that emotional fluctuations are significant during a specific activity based on past data, it can focus on collecting data during that activity. Additionally, if the data collection unit finds that emotional fluctuations are significant under a specific environment based on past data, it can focus on collecting data under that environment. For example, the data collection unit selects the optimal data collection method based on past biometric information. This allows for the selection of the optimal data collection method based on past biometric information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input past biometric information into a generating AI and have the generating AI select the optimal data collection method.

[0064] The data collection unit can filter biometric data based on the child's current activity level and environment. For example, if the child is exercising, the unit can prioritize collecting heart rate and body movement data and filter out other data. Similarly, if the child is quietly reading, the unit can prioritize collecting facial expression and voice tone data and filter out other data. Furthermore, if the child is outdoors, the unit can filter data considering ambient sounds and surrounding conditions. For example, the unit collects appropriate biometric data according to the child's activity level and environment. This allows for the collection of appropriate biometric data based on the child's activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the child's activity level and environment into a generating AI and have the generating AI perform the filtering.

[0065] The data collection unit can estimate a child's emotions and determine the priority of biometric data to collect based on the estimated emotions. For example, if a child is sad, the data collection unit will prioritize collecting data on facial expressions and tone of voice. If a child is happy, the data collection unit can also prioritize collecting data on body movements and heart rate. Furthermore, if a child is angry, the data collection unit can also prioritize collecting data on tone of voice and body movements. For example, the data collection unit can optimize the priority of biometric data to collect according to the child's emotional state. This allows for the optimization of the priority of biometric data collected according to the child's emotional state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to estimate the child's emotional state, the data collection unit can input the collected biometric data into a generating AI and have the generating AI perform the estimation of the emotional state.

[0066] The data collection unit can prioritize the collection of highly relevant information by considering the child's geographical location when collecting biometric data. For example, if the child is at school, the data collection unit can prioritize the collection of data on stress and concentration during learning. Similarly, if the child is in a park, the data collection unit can prioritize the collection of data on excitement and enjoyment during play. Furthermore, if the child is at home, the data collection unit can prioritize the collection of data on relaxation and emotions within the home. For example, the data collection unit can collect highly relevant biometric data based on the child's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the child's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0067] The data collection unit can analyze a child's social media activity and collect relevant information when collecting biometric data. For example, the data collection unit can estimate a child's emotions from the content of their social media posts and collect relevant biometric data. The data collection unit can also estimate a child's emotions from their interactions with friends on social media and collect relevant biometric data. Furthermore, the data collection unit can estimate a child's emotions based on the time of day they are active on social media and collect relevant biometric data. For example, the data collection unit can collect relevant biometric data based on a child's social media activity. This allows the data collection unit to collect relevant biometric data based on a child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's social media activity into a generating AI and have the generating AI collect relevant information.

[0068] The analysis unit can estimate the child's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the child is excited, the analysis unit can apply an algorithm that performs a detailed analysis of emotional fluctuations. If the child is relaxed, the analysis unit can also apply an analysis algorithm that prioritizes emotional stability. Furthermore, if the child is stressed, the analysis unit can apply an analysis algorithm to identify the cause of the stress. For example, the analysis unit optimizes the analysis algorithm according to the child's emotional state. This allows the analysis algorithm to be optimized according to the child's emotional state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, in order to estimate the child's emotional state, the analysis unit can input collected biometric information into a generating AI and have the generating AI perform the estimation of the emotional state.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information during the analysis. For example, if there is a large fluctuation in heart rate, the analysis unit can perform a detailed analysis to identify the cause of the emotional fluctuation. The analysis unit can also perform a detailed analysis to identify the type of emotion if there is a significant change in facial expression. Furthermore, if there is a large change in voice tone, the analysis unit can perform a detailed analysis to identify the intensity of the emotion. For example, the analysis unit optimizes the level of detail of the analysis based on the importance of the biometric information. This allows the level of detail of the analysis to be optimized according to the importance of the biometric information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the biometric information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0070] The analysis unit can apply different analysis methods depending on the category of biometric information during analysis. For example, the analysis unit can apply a method that analyzes temporal variations to heart rate data. It can also apply an analysis method using image recognition technology to facial expression data. Furthermore, it can apply an analysis method using speech recognition technology to voice tone data. For example, the analysis unit applies an appropriate analysis method depending on the category of biometric information. This allows for the application of an appropriate analysis method depending on the category of biometric information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of biometric information into a generating AI and have the generating AI execute the application of different analysis methods.

[0071] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is tense, the analysis unit can provide a simple and highly visible display method. If the child is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the child is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit optimizes the display method of the analysis results according to the child's emotional state. This allows for optimization of the display method of the analysis results according to the child's emotional state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, in order to estimate the child's emotional state, the analysis unit can input collected biometric information into a generating AI and have the generating AI perform the estimation of the emotional state.

[0072] The analysis unit can determine the priority of analysis based on the timing of biometric data collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected data to understand the latest emotional state. It can also prioritize the analysis of data before and after a specific event to understand emotional fluctuations. Furthermore, the analysis unit can analyze data over a long period to understand emotional trends. For example, the analysis unit can optimize the priority of analysis based on the timing of biometric data collection. This allows for the optimization of analysis priority based on the timing of biometric data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of biometric data collection into a generating AI and have the generating AI determine the priority of analysis.

[0073] The analysis unit can adjust the order of analysis based on the relationships between biometric data during the analysis process. For example, if heart rate and changes in facial expression are related, the analysis unit will prioritize analyzing these data. Similarly, if voice tone and body movements are related, the analysis unit can prioritize analyzing these data. Furthermore, if facial expressions and changes in heart rate are related, the analysis unit can prioritize analyzing these data. For example, the analysis unit optimizes the order of analysis based on the relationships between biometric data. This allows for the optimization of the analysis order based on the relationships between biometric data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between biometric data into a generating AI and have the generating AI adjust the order of analysis.

[0074] The service provider can estimate a child's emotions and adjust the expression of response suggestions based on the estimated emotions. For example, if the child is nervous, the service provider will offer response suggestions in a calm manner. If the child is relaxed, the service provider can also offer response suggestions in a manner that includes detailed information. Furthermore, if the child is in a hurry, the service provider can offer response suggestions in a manner that gets straight to the point. For example, the service provider optimizes the expression of response suggestions according to the child's emotional state. This allows for the optimization of the expression of response suggestions according to the child's emotional state. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, to estimate the child's emotional state, the service provider can input collected biometric information into a generating AI and have the generating AI perform the estimation of the emotional state.

[0075] The service provider can adjust the level of detail in a proposal based on the importance of the analysis results. For example, the service provider can provide a detailed proposal for important analysis results. Conversely, it can provide a concise proposal for general analysis results. Furthermore, it can provide a concise proposal for urgent analysis results to allow for a quick response. For example, the service provider can optimize the level of detail in a proposal based on the importance of the analysis results. This allows for the optimization of proposal detail according to the importance of the analysis results. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the analysis results into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0076] The service provider can apply different suggestion algorithms depending on the category of the analysis results when making suggestions. For example, the service provider can apply an algorithm that suggests relaxation methods to analysis results related to stress. It can also apply an algorithm that suggests positive activities to analysis results related to joy. Furthermore, it can apply an algorithm that suggests ways to calm down to analysis results related to anger. For example, the service provider applies an appropriate suggestion algorithm depending on the category of the analysis results. This ensures that an appropriate suggestion algorithm is applied according to the category of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the analysis results into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0077] The service provider can estimate the child's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the child is nervous, the service provider will provide short, concise suggestions. If the child is relaxed, the service provider can also provide longer suggestions with more detailed explanations. Furthermore, if the child is in a hurry, the service provider can provide short suggestions to allow for a quick response. For example, the service provider optimizes the length of suggestions according to the child's emotional state. This allows for the optimization of suggestion length according to the child's emotional state. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, to estimate the child's emotional state, the service provider can input collected biometric information into a generating AI and have the generating AI perform the emotional state estimation.

[0078] The service provider can determine the priority of proposals based on the timing of the submission of analysis results. For example, the service provider may prioritize proposals based on recent analysis results. It may also prioritize proposals based on analysis results before and after a specific event. Furthermore, it may prioritize proposals based on analysis results over a long period. For example, the service provider can optimize the priority of proposals based on the timing of the submission of analysis results. This allows for the optimization of proposal priority based on the timing of the submission of analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the timing of the submission of analysis results into a generating AI and have the generating AI determine the priority of proposals.

[0079] The service provider can adjust the order of suggestions based on the relevance of the analysis results when making suggestions. For example, the service provider may prioritize suggesting analysis results related to stress. It can also prioritize suggesting analysis results related to joy. Furthermore, it can prioritize suggesting analysis results related to anger. For example, the service provider can optimize the order of suggestions based on the relevance of the analysis results. This allows for the optimization of the suggestion order based on the relevance of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0080] The dialogue unit can estimate the child's emotions and adjust the way it expresses the dialogue based on the estimated emotions. For example, if the child is nervous, the dialogue unit will speak in a calm voice. If the child is relaxed, the dialogue unit can speak in a cheerful voice. Furthermore, if the child is excited, the dialogue unit can speak in an energetic voice. For example, the dialogue unit optimizes the way it expresses the dialogue according to the child's emotional state. This allows the dialogue to be optimized according to the child's emotional state. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, in order to estimate the child's emotional state, the dialogue unit can input collected biometric information into a generating AI and have the generating AI perform the estimation of the emotional state.

[0081] The dialogue unit can select the optimal dialogue method by referring to the child's past dialogue history during a conversation. For example, the dialogue unit can prioritize topics the child likes from past dialogue history. It can also select a speaking style that makes the child feel relaxed from past dialogue history. Furthermore, it can select topics that the child is interested in from past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on past dialogue history. This allows the dialogue unit to select the optimal dialogue method based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past dialogue history into a generating AI and have the generating AI select the optimal dialogue method.

[0082] The dialogue unit can customize the content of the dialogue based on the child's current emotional state during the conversation. For example, if the child is sad, the dialogue unit can offer comforting words. If the child is happy, the dialogue unit can offer empathetic words. Furthermore, if the child is angry, the dialogue unit can offer words that encourage them to calm down. For example, the dialogue unit optimizes the content of the dialogue according to the child's current emotional state. This allows the dialogue content to be optimized according to the child's current emotional state. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input collected biometric information into a generating AI to estimate the child's emotional state, and have the generating AI perform the estimation of the emotional state.

[0083] The dialogue unit can estimate the child's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the child is tense, the dialogue unit will prioritize dialogue that helps the child relax. If the child is relaxed, the dialogue unit can also prioritize dialogue on enjoyable topics. Furthermore, if the child is excited, the dialogue unit can also prioritize dialogue that helps the child calm down. For example, the dialogue unit optimizes the priority of the dialogue according to the child's emotional state. This allows for the optimization of the priority of the dialogue according to the child's emotional state. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, in order to estimate the child's emotional state, the dialogue unit can input collected biometric information into a generating AI and have the generating AI perform the estimation of the emotional state.

[0084] The dialogue unit can select the optimal dialogue method while considering the child's geographical location information. For example, if the child is at school, the dialogue unit can engage in dialogue on topics related to learning. If the child is in a park, the dialogue unit can engage in dialogue on topics related to play. Furthermore, if the child is at home, the dialogue unit can engage in dialogue on topics related to home. For example, the dialogue unit selects the optimal dialogue method based on the child's geographical location information. This allows the dialogue unit to select the optimal dialogue method based on the child's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's geographical location information into a generating AI and have the generating AI select the optimal dialogue method.

[0085] The dialogue unit can analyze a child's social media activity during a conversation and suggest content for that conversation. For example, the dialogue unit can suggest topics that the child might be interested in based on their social media posts. It can also suggest content based on the child's interactions with friends on social media. Furthermore, the dialogue unit can suggest content based on the time of day the child is active on social media. For example, the dialogue unit can optimize the content of the conversation based on the child's social media activity. This allows for the optimization of the conversation content based on the child's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's social media activity into a generating AI and have the generating AI suggest content for the conversation.

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

[0087] The plush toy system can also be equipped with a learning support unit that monitors the child's learning progress and provides feedback tailored to their learning situation. For example, when a child is doing homework, the learning support unit can evaluate the accuracy and understanding of their answers and provide appropriate advice. Furthermore, if a child is learning a new concept, the learning support unit can evaluate their understanding in real time and provide additional explanations or practice problems as needed. In addition, the learning support unit can analyze the child's learning patterns and suggest an optimal learning schedule. This allows the plush toy system to support the child's learning and maximize learning effectiveness.

[0088] The plush toy system can also include a health management unit that monitors the child's health and provides information useful for health management. For example, the health management unit can record the child's diet and evaluate its nutritional balance. It can also monitor the child's exercise level and suggest appropriate exercise plans. Furthermore, it can analyze the child's sleep patterns and provide advice to promote quality sleep. In this way, the plush toy system can comprehensively support the child's health and promote healthy growth.

[0089] The plush toy system can also include a creative support department to further foster children's creativity through creative activities. For example, the creative support department can offer advice on color selection and composition when children are drawing. It can also provide hints for story development and character design when children are creating stories. Furthermore, it can offer ideas for melody and rhythm when children are creating music. In this way, the plush toy system can draw out children's creativity and cultivate their rich expressive abilities.

[0090] The plush toy system can also be equipped with a social skills support section to further foster children's social skills. For example, the social skills support section can advise children on appropriate communication methods when playing with friends. It can also provide support to help children develop cooperation and leadership skills when participating in group activities. Furthermore, the social skills support section can suggest problem-solving methods when children face difficult situations. In this way, the plush toy system can cultivate children's social skills and develop their ability to build smooth interpersonal relationships.

[0091] The plush toy system can also be equipped with a music provider that estimates the child's emotions and plays appropriate music based on those emotions. For example, if the child is sad, the music provider can play relaxing music. If the child is happy, it can play cheerful music. Furthermore, if the child is angry, it can play calming music. In this way, the plush toy system can provide appropriate music according to the child's emotional state and support emotional stability.

[0092] The plush toy system can also include an aromatherapy unit that estimates a child's emotions and provides an appropriate aroma based on those emotions. For example, if a child is tense, the aromatherapy unit can provide a relaxing aroma. If the child is relaxed, the aromatherapy unit can also provide an aroma that enhances concentration. Furthermore, if the child is stressed, the aromatherapy unit can provide an aroma that reduces stress. In this way, the plush toy system can provide an appropriate aroma according to the child's emotional state and support emotional stability.

[0093] The plush toy system can also include an exercise suggestion unit that estimates a child's emotions and suggests appropriate exercises based on those emotions. For example, if a child is excited, the exercise suggestion unit might suggest exercises to release energy. If a child is relaxed, the exercise suggestion unit might suggest light stretching. Furthermore, if a child is stressed, the exercise suggestion unit might suggest relaxing yoga. In this way, the plush toy system can suggest appropriate exercises according to the child's emotional state and support their physical and mental health.

[0094] The plush toy system can also include a reading suggestion unit that estimates a child's emotions and suggests appropriate reading based on those emotions. For example, if a child is sad, the reading suggestion unit can suggest a book that will cheer them up. If a child is happy, the reading suggestion unit can suggest a book that will be even more enjoyable. Furthermore, if a child is angry, the reading suggestion unit can suggest a book that will calm them down. In this way, the plush toy system can suggest appropriate reading according to the child's emotional state and support emotional stability.

[0095] The plush toy system can also include a game suggestion unit that estimates a child's emotions and suggests an appropriate game based on those emotions. For example, if a child is sad, the game suggestion unit can suggest a game to cheer them up. If a child is happy, the game suggestion unit can suggest a game that will make the child even happier. Furthermore, if a child is angry, the game suggestion unit can suggest a game to calm them down. In this way, the plush toy system can suggest an appropriate game according to the child's emotional state and support emotional stability.

[0096] The plush toy system can also include a meal suggestion unit that estimates a child's emotions and suggests appropriate meals based on those emotions. For example, if a child is tense, the meal suggestion unit can suggest a meal with a relaxing effect. If a child is relaxed, the meal suggestion unit can also suggest a meal that enhances concentration. Furthermore, if a child is stressed, the meal suggestion unit can suggest a meal that reduces stress. In this way, the plush toy system can suggest appropriate meals according to the child's emotional state and support emotional stability.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects biometric information such as the child's facial expressions, voice tone, body movements, and heart rate. For example, the data collection unit can capture the child's facial expressions with a camera and save them as image data. It can also record the child's voice tone with a microphone and save it as audio data. Furthermore, it can detect the child's body movements with a motion sensor and collect movement data. It measures the child's heart rate with a heart rate sensor and saves it as heart rate data. Step 2: The analysis unit analyzes the biometric information collected by the collection unit to understand the child's emotional state in real time. For example, it can use image recognition technology to analyze the child's facial expressions and estimate their emotional state. It can also use speech recognition technology to analyze the tone of the child's voice and estimate their emotional state. Furthermore, it can analyze motion sensor data to estimate the emotional state from the child's body movements. It can also analyze heart rate data to estimate the emotional state from the fluctuations in the child's heart rate. Step 3: The service provider makes specific response suggestions to parents and teachers based on the analysis results obtained by the analysis unit. For example, it can notify parents of the child's emotional state based on the analysis results and suggest appropriate responses. It can also notify teachers of the child's emotional state based on the analysis results and suggest individually optimized instruction. Furthermore, it can generate a detailed emotional state report and make specific response suggestions to parents and teachers. For example, it can suggest specific responses to parents, such as praising, scolding, or encouraging, depending on the child's emotional state. Step 4: The dialogue department uses an AI chatbot to engage in natural conversations with children and build trust. For example, through dialogue with children, it can understand their emotional state and respond appropriately. It can also facilitate children's emotional expression and deepen their self-understanding. Furthermore, through dialogue with children, it can notify parents and teachers of the children's emotional state and suggest appropriate responses. For example, it can understand the children's emotional state in real time through dialogue and build trust.

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

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the child's facial expressions and voice tone using the camera 42 and microphone 38B of the smart device 14, and collects body movements and heart rate using motion sensors and heart rate sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected biometric information to grasp the child's emotional state in real time. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and makes specific response suggestions to parents and teachers based on the analysis results. The dialogue unit is implemented in the control unit 46A of the smart device 14, for example, and uses an AI chatbot to engage in natural dialogue with the child and build a relationship of trust. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the child's facial expressions and voice tone, and motion sensors and heart rate sensors to collect body movements and heart rate. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected biometric information and grasp the child's emotional state in real time. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to make specific response suggestions to parents and teachers based on the analysis results. The dialogue unit is implemented in the control unit 46A of the smart glasses 214, for example, to engage in natural dialogue with the child using an AI chatbot and build a relationship of trust. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and dialogue unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the child's facial expressions and voice tone, and motion sensors and heart rate sensors to collect body movements and heart rate. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected biometric information and grasp the child's emotional state in real time. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to make specific response suggestions to parents and teachers based on the analysis results. The dialogue unit is implemented in the control unit 46A of the headset terminal 314, for example, to engage in natural dialogue with the child using an AI chatbot and build a relationship of trust. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and dialogue unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the child's facial expressions and voice tone, and motion sensors and heart rate sensors to collect body movements and heart rate. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected biometric information and grasp the child's emotional state in real time. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to make specific response suggestions to parents and teachers based on the analysis results. The dialogue unit is implemented in the control unit 46A of the robot 414, for example, to engage in natural dialogue with the child using an AI chatbot and build a relationship of trust. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A collection unit that collects biometric information such as the child's facial expressions, voice tone, body movements, and heart rate, The analysis unit analyzes the biological information collected by the aforementioned collection unit to grasp the child's emotional state in real time, Based on the analysis results obtained by the aforementioned analysis unit, the provision unit provides specific suggestions for action to parents and teachers. It includes a dialogue section for engaging in natural conversations with children and building trusting relationships. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the child's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the child's past biometric data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting biometric data, filtering is performed based on the child's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the biometric data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting biometric information, the system prioritizes the collection of highly relevant information, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting biometric data, analyze the child's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the biological information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the category of biological information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the biological information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the biological information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, We estimate the child's emotions and adjust the way we express our response suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When making a proposal, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When making a proposal, different proposal algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When submitting a proposal, the priority of the proposal will be determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When making proposals, adjust the order of proposals based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, The system estimates the child's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During the conversation, refer to the child's past conversation history to select the most appropriate method of communication. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, During the conversation, customize the content of the dialogue based on the child's current emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, Estimate the child's emotions and determine the priority of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During the conversation, the optimal method of communication is selected, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, During the conversation, we analyze the child's social media activity and suggest content for the conversation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects biometric information such as the child's facial expressions, voice tone, body movements, and heart rate, The analysis unit analyzes the biological information collected by the aforementioned collection unit to grasp the child's emotional state in real time, Based on the analysis results obtained by the aforementioned analysis unit, the provision unit provides specific suggestions for action to parents and teachers. It includes a dialogue section for engaging in natural conversations with children and building trusting relationships. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the child's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the child's past biometric data and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting biometric data, filtering is performed based on the child's current activity level and environment. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the biometric data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting biometric information, the system prioritizes the collection of highly relevant information, taking into account the child's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting biometric data, analyze the child's social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the child's emotions and adjusts the analysis algorithm based on the estimated emotions. The system according to feature 1.

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