system

Abstract

The system according to this embodiment aims to provide appropriate support tailored to the child's age and behavior. [Solution] The system according to the embodiment comprises a behavior analysis unit, a danger information provision unit, a play suggestion unit, and a dream support unit. The behavior analysis unit analyzes the child's behavior according to their age. The danger information provision unit provides danger information based on the results analyzed by the behavior analysis unit. The play suggestion unit suggests play based on the information provided by the danger information provision unit. The dream support unit suggests actions toward future dreams based on the results analyzed by the behavior analysis unit.

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, it is difficult to provide appropriate support according to the age and behavior of children, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate support according to the age and behavior of children.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a behavior analysis unit, a danger information provision unit, a play suggestion unit, and a dream support unit. The behavior analysis unit analyzes the child's behavior according to their age. The danger information provision unit provides danger information based on the results analyzed by the behavior analysis unit. The play suggestion unit suggests play based on the information provided by the danger information provision unit. The dream support unit suggests actions toward future dreams based on the results analyzed by the behavior analysis unit. [Effects of the Invention]

[0007] The system, as exemplified, can provide appropriate support tailored to the child's age and behavior. [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 applied 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 childcare support system according to an embodiment of the present invention is a system that analyzes a child's age-appropriate behavior, provides danger information, suggests play activities, and supports their dreams. This system collects information such as things a child should not do at their age, actions they should take, danger information inferred from location information, danger information obtained in cooperation with the police and local government, playmates when parents are doing housework, things that are difficult for parents to ask, and poor health, and a generating AI analyzes this information. Next, the generating AI provides the child with appropriate actions and precautions based on the analysis results. It also supports the child's dreams and introduces appropriate routes toward their future career aspirations. For example, the generating AI analyzes things a child should not do and actions they should take at their age and provides this information to the child. For example, it may present dangerous behaviors and recommended behaviors for a specific age. Next, based on the child's location information, the generating AI analyzes danger information and provides warnings. For example, if a child approaches a dangerous place, it warns them with pictures and sounds that are easy for children to understand, tailored to the terrain. It also collects local danger information in cooperation with the police and local government and provides it to the child. Furthermore, the generating AI suggests appropriate play activities to keep the child entertained while parents are doing housework. For example, it suggests suitable activities for when a child is playing alone. Furthermore, the AI ​​analyzes questions that children might find difficult to ask their parents or concerns about their health, providing appropriate advice. For example, if a child asks a question they might find difficult to ask their parents, the AI ​​will provide an appropriate answer. Finally, it supports children's dreams and introduces appropriate routes towards their desired future careers. For example, if a child wants to become a YouTuber, the AI ​​will suggest the actions and skills they should acquire. In this way, the system supports children's dreams and provides concrete advice for achieving their future goals. This enables the parenting support system to perform behavioral analysis, provide danger information, suggest play activities, and support children's dreams, all tailored to their age.

[0029] The childcare support system according to this embodiment comprises a behavior analysis unit, a danger information provision unit, a play suggestion unit, and a dream support unit. The behavior analysis unit analyzes the child's behavior according to their age. The behavior analysis unit analyzes the child's behavior according to their age, for example, using a generative AI. For example, the behavior analysis unit can analyze the behavior of toddlers, the behavior of elementary school children, etc. The behavior analysis unit can analyze the child's behavior patterns using a generative AI and suggest appropriate actions. The danger information provision unit provides danger information based on the results analyzed by the behavior analysis unit. The danger information provision unit provides danger information, for example, using a generative AI. For example, the danger information provision unit can provide information on the risk of traffic accidents, dangers within the home, etc. The danger information provision unit can provide danger information based on the child's location information using a generative AI. The play suggestion unit suggests play based on the information provided by the danger information provision unit. The play suggestion unit suggests play, for example, using a generative AI. For example, the play suggestion unit can suggest indoor play, outdoor play, educational play, etc. The play suggestion unit can use generative AI to suggest play activities tailored to the child's age and interests. The dream support unit suggests actions toward future dreams based on the results analyzed by the behavioral analysis unit. For example, the dream support unit can suggest actions toward future dreams using generative AI. For example, the dream support unit can suggest career experiences, skill acquisition, etc. The dream support unit can use generative AI to suggest specific actions toward the child's dreams. As a result, the childcare support system according to the embodiment can perform behavioral analysis, provide danger information, suggest play activities, and support dreams, all tailored to the child's age.

[0030] The Behavioral Analysis Unit analyzes children's behavior according to their age. For example, it uses generative AI to analyze age-appropriate behavior. Specifically, the generative AI collects children's behavioral data and learns age-specific behavioral patterns. For example, behaviors in early childhood include learning to walk, language development, and types of play. Behaviors in elementary school children include school learning activities, interaction with friends, and sports activities. The generative AI analyzes this behavioral data and can suggest appropriate behaviors according to the child's developmental stage. Furthermore, the Behavioral Analysis Unit can analyze individual children's behavioral patterns and evaluate the impact of specific behaviors. For example, it can analyze how a particular type of play contributes to a child's cognitive development, or how a particular behavior affects the development of social skills. This allows the Behavioral Analysis Unit to provide concrete behavioral guidelines to support children's growth.

[0031] The Hazard Information Provision Department provides hazard information based on the results analyzed by the Behavioral Analysis Department. The Hazard Information Provision Department uses, for example, generative AI to provide hazard information. Specifically, the generative AI analyzes children's behavioral data and location information to identify potential hazards. For example, it can detect locations with a risk of traffic accidents or dangerous situations within the home. The generative AI can issue real-time warnings when a child approaches a specific location or takes a specific action. For example, if a child approaches a road, it can send a warning to the parent's smartphone to prompt caution. Furthermore, for hazard information within the home, it can issue warnings when approaching dangerous areas such as stairs or the kitchen. This allows the Hazard Information Provision Department to provide crucial information to ensure children's safety and prevent accidents and injuries. In addition, the Hazard Information Provision Department can analyze hazard trends based on past data and propose long-term safety measures. For example, it can analyze the frequency of accidents at specific times or locations and implement preventative measures. This allows the Hazard Information Provision Department to comprehensively support children's safety.

[0032] The Play Suggestion Department suggests activities based on information provided by the Risk Information Provision Department. The Play Suggestion Department uses, for example, generative AI to suggest activities. Specifically, the generative AI suggests activities tailored to the child's age, interests, and current situation. For example, indoor activities might include building blocks, reading picture books, and puzzles. Outdoor activities might include playing in parks, sports, and nature observation. Educational activities might include learning games, science experiments, and art activities. The generative AI can select and suggest the most suitable activities to parents based on the child's interests and developmental stage. Furthermore, the Play Suggestion Department can record the child's play history and evaluate the effectiveness of past activities. For example, it can analyze how a particular activity contributed to the child's cognitive development and social skills and reflect this in future activity suggestions. This allows the Play Suggestion Department to provide effective activities that promote the child's growth. Additionally, the Play Suggestion Department can suggest activities suitable for various situations, such as activities that parents and children can enjoy together, or activities that friends can enjoy. This allows the Play Suggestion Department to improve the quality of children's play and provide enjoyable experiences.

[0033] The Dream Support Department proposes actions toward future dreams based on the results analyzed by the Behavioral Analysis Department. For example, the Dream Support Department uses generative AI to propose actions toward future dreams. Specifically, the generative AI analyzes the child's interests, talents, and current behavioral patterns and proposes specific actions toward future dreams. For example, if the child is interested in science, it can suggest science experiments, visits to science museums, and reading science-related books. For career experience, it can suggest programs that allow children to experience professions related to their dreams, such as doctors, engineers, and artists. For skill acquisition, it can suggest specific activities to acquire the skills necessary for the child's dreams, such as programming, playing musical instruments, and sports training. Furthermore, the Dream Support Department can create long-term plans toward the child's dreams and show parents and children specific steps. For example, it can support the child in steadily progressing toward their dreams through goal setting, progress management, and the provision of feedback. In this way, the Dream Support Department can provide specific action guidelines for realizing the child's future dreams and support the child's growth and development.

[0034] The location information acquisition unit can acquire location information. The location information acquisition unit can acquire location information using, for example, a generating AI. For example, the location information acquisition unit can acquire GPS information, Wi-Fi location information, etc. The location information acquisition unit can acquire the child's current location using a generating AI and provide appropriate information. This makes it possible to provide information based on the child's current location by acquiring location information. Some or all of the above processing in the location information acquisition unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the location information acquisition unit can input GPS information into a generating AI and have the generating AI perform location information analysis.

[0035] The Danger Information Provision Department can collect and provide local danger information in cooperation with the police and local governments. The Danger Information Provision Department can collect and provide local danger information using, for example, generative AI. For example, the Danger Information Provision Department can collect and provide crime information, disaster information, etc. The Danger Information Provision Department can analyze local danger information using generative AI and provide it to children. In this way, local danger information can be provided in cooperation with the police and local governments. Some or all of the above processing in the Danger Information Provision Department may be performed using generative AI, or may not be performed using generative AI. For example, the Danger Information Provision Department can input danger information obtained from the police and local governments into generative AI and have the generative AI perform the analysis of the danger information.

[0036] The play suggestion unit can suggest appropriate games for children to play alone while their parents are doing housework. The play suggestion unit can suggest appropriate games using, for example, generative AI. For example, the play suggestion unit can suggest age-appropriate games, safe games, etc. The play suggestion unit can suggest games that are appropriate for the child's age and interests using generative AI. This allows the unit to suggest appropriate games for children to play alone while their parents are doing housework. Some or all of the above processing in the play suggestion unit may be performed using generative AI or not. For example, the play suggestion unit can input data about the child's age and interests into the generative AI and have the generative AI suggest appropriate games.

[0037] The Dream Support Department can suggest actions and skills that will help children achieve their future career aspirations. For example, the Dream Support Department can use generative AI to suggest these actions and skills. For instance, it can suggest learning skills, social skills, and more. The Dream Support Department can use generative AI to suggest specific actions and skills that will help children achieve their dreams. This allows the Dream Support Department to suggest actions and skills that will help children achieve their future career aspirations. Some or all of the above-described processes in the Dream Support Department may be performed using generative AI, or they may not. For example, the Dream Support Department can input data about a child's dreams into a generative AI and have the AI ​​suggest appropriate actions and skills.

[0038] The behavioral analysis unit can analyze children's health issues and matters that parents may find difficult to discuss, and provide appropriate advice. For example, the behavioral analysis unit can use generative AI to analyze children's health issues and matters that parents may find difficult to discuss. For instance, the behavioral analysis unit can analyze health issues such as fever and stomachaches and provide appropriate advice. The behavioral analysis unit can also use generative AI to analyze matters that parents may find difficult to discuss, such as sex education and bullying, and provide appropriate advice. This allows for the provision of appropriate advice regarding children's health issues and matters that parents may find difficult to discuss. Some or all of the above-described processes in the behavioral analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the behavioral analysis unit can input data on children's health issues into a generative AI and have the generative AI provide appropriate advice.

[0039] The behavioral analysis unit can analyze a child's past behavioral history and predict future behavior. For example, the behavioral analysis unit can use generative AI to analyze past behavioral history. For instance, the behavioral analysis unit can use generative AI to analyze a child's frequently performed past behavioral patterns and predict future behavior. The behavioral analysis unit can use generative AI to analyze a child's past behaviors at specific time periods and predict potential future behaviors at the same time periods. The behavioral analysis unit can use generative AI to analyze a child's past behaviors at specific locations and predict potential future behaviors at the same locations. In this way, future behavior can be predicted by analyzing a child's past behavioral history. Some or all of the above-described processes in the behavioral analysis unit may be performed using generative AI, or they may not. For example, the behavioral analysis unit can input past behavioral history data into a generative AI and have the generative AI perform predictions of future behavior.

[0040] The behavioral analysis unit can adjust the analysis results while considering the child's current health condition during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to adjust the analysis results while considering the child's current health condition. For example, if the child has a cold, the generative AI can detect this health condition and adjust the analysis results while considering the health condition during behavioral analysis. If the child is tired, the generative AI can detect this health condition and adjust the analysis results while considering the fatigue level during behavioral analysis. If the child is injured, the generative AI can detect this health condition and adjust the analysis results while considering the injury level during behavioral analysis. By adjusting the analysis results while considering the child's current health condition, more accurate behavioral analysis becomes possible. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the behavioral analysis unit can input the child's health condition data into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0041] The behavioral analysis unit can provide analysis results that take into account a child's learning history during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to provide analysis results that take into account the learning history. For example, the behavioral analysis unit can have a generative AI analyze what a child has learned in the past and provide analysis results that take into account the learning history during behavioral analysis. The behavioral analysis unit can have a generative AI analyze what a child has learned in a specific subject and provide analysis results that take into account the learning history for that subject during behavioral analysis. The behavioral analysis unit can have a generative AI analyze learning events that a child has participated in in the past and provide analysis results that take into account the learning history for those events during behavioral analysis. This makes it possible to perform more appropriate behavioral analysis by providing analysis results that take into account the child's learning history. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI or not. For example, the behavioral analysis unit can input a child's learning history data into a generative AI and have the generative AI perform the provision of analysis results.

[0042] The behavioral analysis unit can provide analysis results that take into account the child's friendships during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to provide analysis results that take friendships into account. For example, the behavioral analysis unit can use a generative AI to analyze information about friends the child has played with in the past and provide analysis results that take friendships into account during behavioral analysis. The behavioral analysis unit can use a generative AI to analyze the amount of time the child has spent with a specific friend and provide analysis results that take that friendship into account during behavioral analysis. The behavioral analysis unit can use a generative AI to analyze activities the child has done with friends in the past and provide analysis results that take those activities into account during behavioral analysis. By providing analysis results that take the child's friendships into account, more appropriate behavioral analysis becomes possible. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the behavioral analysis unit can input the child's friendship data into a generative AI and have the generative AI perform the task of providing analysis results.

[0043] The hazard information provision unit can predict current hazards by referring to past hazard information data when providing hazard information. The hazard information provision unit can predict current hazards by referring to past hazard information data, for example, using a generating AI. For example, the hazard information provision unit can have a generating AI analyze past hazard information and predict current hazards. The hazard information provision unit can have a generating AI predict hazards at a specific location based on past hazard information data. The hazard information provision unit can have a generating AI predict hazards during a specific time period based on past hazard information data. In this way, current hazards can be predicted by referring to past hazard information data. Some or all of the above processing in the hazard information provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the hazard information provision unit can input past hazard information data into a generating AI and have the generating AI perform a prediction of current hazards.

[0044] The hazard information provision unit can provide warnings while considering the child's current activities when providing hazard information. The hazard information provision unit can, for example, use a generative AI to provide warnings while considering the current activities. For example, if the child is playing, the generative AI can detect that activity and provide hazard information related to play. If the child is moving, the generative AI can detect that activity and provide hazard information related to movement. If the child is studying, the generative AI can detect that activity and provide hazard information related to studying. This makes it possible to provide more appropriate information by providing warnings while considering the child's current activities. Some or all of the above processing in the hazard information provision unit may be performed using a generative AI or not. For example, the hazard information provision unit can input data on the child's activities into a generative AI and have the generative AI perform the task of providing warnings.

[0045] The risk information provision unit can prioritize providing highly relevant information by considering the child's geographical location when providing risk information. For example, the risk information provision unit can use a generating AI to prioritize providing highly relevant information by considering geographical location. For example, if the child is in a specific location, the generating AI can detect that geographical location and prioritize providing risk information related to that location. If the child is on the move, the generating AI can detect that geographical location and prioritize providing risk information related to the travel route. If the child is in a specific region, the generating AI can detect that geographical location and prioritize providing risk information related to that region. This makes it possible to provide more appropriate information by prioritizing the provision of highly relevant information by considering the child's geographical location. Some or all of the above processing in the risk information provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the risk information provision unit can input the child's geographical location data into a generating AI and have the generating AI perform the provision of highly relevant information.

[0046] The risk information provision unit can analyze a child's social media activity and provide relevant risk information when providing risk information. The risk information provision unit can, for example, use generative AI to analyze social media activity and provide relevant risk information. For example, if a child checks in to a specific location on social media, the generative AI can detect that information and provide risk information related to that location. If a child posts on social media that they are attending a specific event, the generative AI can detect that information and provide risk information related to that event. If a child posts on social media that they are with a specific friend, the generative AI can detect that information and provide risk information related to that friend. In this way, relevant risk information can be provided by analyzing a child's social media activity. Some or all of the above processing in the risk information provision unit may be performed using generative AI or not. For example, the risk information provision unit can input a child's social media activity data into a generative AI and have the generative AI perform the provision of relevant risk information.

[0047] The play suggestion unit can suggest the most suitable game by referring to the child's past play history when suggesting a game. For example, the play suggestion unit can use a generative AI to refer to the past play history and suggest the most suitable game. For example, the play suggestion unit can use a generative AI to analyze games the child has enjoyed in the past and suggest similar games. The play suggestion unit can use a generative AI to analyze play events the child has participated in in the past and suggest similar events. The play suggestion unit can use a generative AI to analyze places the child has played in the past and suggest games related to those places. In this way, the optimal game can be suggested by referring to the child's past play history. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input the child's past play history data into a generative AI and have the generative AI suggest the most suitable game.

[0048] The play suggestion unit can customize its suggestions by taking into account the child's current interests. For example, the play suggestion unit can use a generative AI to customize the suggestions by taking into account the child's current interests. For example, the generative AI can analyze the themes the child is currently interested in and suggest games related to those themes. The generative AI can analyze the characters the child is currently interested in and suggest games related to those characters. The generative AI can analyze the activities the child is currently interested in and suggest games related to those activities. By customizing the suggestions by taking into account the child's current interests, more appropriate games can be suggested. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input data on the child's current interests into a generative AI and have the generative AI customize the suggestions.

[0049] The play suggestion unit can suggest the most suitable play activity by considering the child's geographical location. For example, the play suggestion unit can use a generative AI to suggest the most suitable play activity by considering the geographical location. For example, if the child is in a park, the generative AI can detect that geographical location and suggest activities that can be done in the park. If the child is at home, the generative AI can detect that geographical location and suggest activities that can be done at home. If the child is at a friend's house, the generative AI can detect that geographical location and suggest activities that can be done at the friend's house. By suggesting the most suitable play activity by considering the child's geographical location, it becomes possible to provide more appropriate information. Some or all of the above processing in the play suggestion unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the play suggestion unit can input the child's geographical location data into a generative AI and have the generative AI perform the task of suggesting the most suitable play activity.

[0050] The play suggestion unit can suggest group games that take into account the child's friendships when suggesting games. For example, the play suggestion unit can use a generative AI to suggest group games that take friendships into account. For example, if the child is with friends, the generative AI can detect those friendships and suggest games that can be enjoyed as a group. If the child is spending time with a specific friend, the generative AI can detect those friendships and suggest games that can be enjoyed with that friend. If the child has plans to play with friends, the generative AI can detect those friendships and suggest games that can be enjoyed as a group in advance. This makes it possible to provide more appropriate information by suggesting group games that take the child's friendships into account. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input data on the child's friendships into a generative AI and have the generative AI suggest group games.

[0051] The Dream Support Department can suggest optimal actions and skills by referring to a child's past learning history during Dream Support. For example, the Dream Support Department can use a generative AI to suggest optimal actions and skills by referring to a child's past learning history. For example, the Dream Support Department can have the generative AI analyze what a child has learned in the past and suggest optimal actions and skills by considering the learning history during Dream Support. The Dream Support Department can have the generative AI analyze what a child has learned in a specific subject and suggest optimal actions and skills by considering the learning history for that subject during Dream Support. The Dream Support Department can have the generative AI analyze learning events a child has participated in in the past and suggest optimal actions and skills by considering the learning history for those events during Dream Support. In this way, optimal actions and skills can be suggested by referring to a child's past learning history. Some or all of the above processes in the Dream Support Department may be performed using a generative AI or not. For example, the Dream Support Department can input data on a child's past learning history into a generative AI and have the generative AI suggest optimal actions and skills.

[0052] The Dream Support Department can customize its suggestions during dream support sessions by taking into account the child's current interests. For example, the Dream Support Department can use a generative AI to customize suggestions based on the child's current interests. For instance, the generative AI can analyze the themes the child is currently interested in and suggest actions and skills related to those themes. The generative AI can analyze the characters the child is currently interested in and suggest actions and skills related to those characters. The generative AI can analyze the activities the child is currently interested in and suggest actions and skills related to those activities. By customizing suggestions based on the child's current interests, more appropriate information can be provided. Some or all of the above-described processes in the Dream Support Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Dream Support Department can input data on the child's current interests into a generative AI and have the generative AI customize the suggestions.

[0053] The Dream Support Department can suggest optimal actions and skills while considering the child's geographical location during dream support. For example, the Dream Support Department can use generative AI to suggest optimal actions and skills while considering geographical location. For example, if the child is in a specific location, the generative AI can detect that location and suggest actions and skills related to that location. If the child is on the move, the generative AI can detect that location and suggest actions and skills related to the travel route. If the child is in a specific region, the generative AI can detect that region and suggest actions and skills related to that region. This makes it possible to provide more appropriate information by suggesting optimal actions and skills while considering the child's geographical location. Some or all of the above processing in the Dream Support Department may be performed using generative AI, or it may be performed without using generative AI. For example, the Dream Support Department can input the child's geographical location data into the generative AI and have the generative AI suggest optimal actions and skills.

[0054] The Dream Support Department can suggest activities that children can engage in together, taking into account their friendships, when providing dream support. For example, the Dream Support Department can use a generative AI to suggest activities that children can engage in together, taking their friendships into consideration. For example, if a child is with a friend, the generative AI can detect that friendship and suggest activities that children can engage in together. If a child is spending time with a specific friend, the generative AI can detect that friendship and suggest activities that children can engage in together with that friend. If a child has plans to play with a friend, the generative AI can detect that friendship and suggest activities that children can engage in together in advance. This allows for the provision of more appropriate information by suggesting activities that children can engage in together, taking their friendships into consideration. Some or all of the above-described processes in the Dream Support Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Dream Support Department can input data on the child's friendships into a generative AI and have the generative AI suggest activities that children can engage in together.

[0055] The location information acquisition unit can select the optimal acquisition method by referring to the child's past movement history when acquiring location information. For example, the location information acquisition unit can use a generating AI to refer to past movement history and select the optimal acquisition method. For example, the generating AI can analyze places the child has frequently visited in the past and select the location information acquisition method for those locations. The generating AI can analyze the child's past movement history during specific time periods and select the location information acquisition method for those time periods. The generating AI can analyze the child's past travel history along specific routes and select the location information acquisition method for those routes. In this way, the optimal location information acquisition method can be selected by referring to the child's past movement history. Some or all of the above processing in the location information acquisition unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the location information acquisition unit can input the child's past movement history data into a generating AI and have the generating AI select the optimal acquisition method.

[0056] The location information acquisition unit can select the optimal acquisition method when acquiring location information, taking into account the child's current activities. For example, the location information acquisition unit can use a generative AI to select the optimal acquisition method, taking into account the child's current activities. For example, if the child is playing, the generative AI can detect that activity and select a location information acquisition method related to playing. If the child is moving, the generative AI can detect that activity and select a location information acquisition method related to movement. If the child is studying, the generative AI can detect that activity and select a location information acquisition method related to studying. By selecting the optimal location information acquisition method, taking into account the child's current activities, it becomes possible to provide more appropriate information. Some or all of the above processing in the location information acquisition unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the location information acquisition unit can input data on the child's current activities into a generative AI and have the generative AI select the optimal acquisition method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The behavioral analysis unit can consider a child's learning style when analyzing their behavioral patterns. For example, using generative AI, the behavioral analysis unit can provide behavioral suggestions that include a lot of visual information if the child is a visual learner. If the child is an auditory learner, it can provide behavioral suggestions that include a lot of audio. Furthermore, if the child is an experiential learner, it can provide behavioral suggestions that involve physical movement. This makes it possible to provide behavioral suggestions that are tailored to the child's learning style, enabling more effective support.

[0059] The location information acquisition unit can select the optimal acquisition method when acquiring a child's location information, taking into account the child's activity history. For example, the location information acquisition unit can use a generation AI to analyze places the child has frequently visited in the past and select the location information acquisition method for those locations. Furthermore, if the child tends to move around during specific time periods, the unit can select a location information acquisition method that suits those time periods. In addition, if the child frequently uses a specific route, the unit can select a location information acquisition method that suits that route. This makes it possible to acquire optimal location information while taking into account the child's activity history.

[0060] The behavioral analysis unit can analyze a child's past behavioral history and predict future behavior. For example, it can use generative AI to analyze a child's frequently occurring behavioral patterns in the past and predict future behavior. It can also analyze a child's behavior at specific times of day and predict behaviors they might exhibit at the same time of day in the future. Furthermore, it can analyze a child's behavior at specific locations and predict behaviors they might exhibit at the same locations in the future. In this way, by analyzing a child's past behavioral history, future behavior can be predicted.

[0061] The hazard information provision department can predict current hazards by referring to past hazard information data when providing hazard information. For example, the hazard information provision department can use generation AI to analyze past hazard information data and predict current hazards. It can also predict hazards in specific locations. Furthermore, it can predict hazards in specific time periods. In this way, current hazards can be predicted by referring to past hazard information data.

[0062] The play suggestion function can suggest the most suitable play activities by referring to the child's past play history. For example, it can use generative AI to analyze games the child has enjoyed in the past and suggest similar games. It can also analyze play events the child has participated in in the past and suggest similar events. Furthermore, it can analyze places the child has played in the past and suggest games related to those places. In this way, by referring to the child's past play history, it can suggest the most suitable play activities.

[0063] The Dream Support Department can suggest optimal actions and skills during dream support sessions by referencing a child's past learning history. For example, using generative AI, the Dream Support Department can analyze what a child has learned in the past and suggest optimal actions and skills during dream support sessions, taking their learning history into consideration. It can also analyze what a child has learned in a specific subject and suggest optimal actions and skills, taking their learning history in that subject into consideration. Furthermore, it can analyze past learning events the child has participated in and suggest optimal actions and skills, taking their learning history in those events into consideration. In this way, by referring to a child's past learning history, it can suggest optimal actions and skills.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The behavioral analysis unit analyzes the child's behavior according to their age. For example, it can use generative AI to analyze the behavior of toddlers and elementary school children, analyze the child's behavioral patterns, and suggest appropriate actions. Step 2: The risk information provision unit provides risk information based on the results analyzed by the behavioral analysis unit. For example, it can use a generation AI to provide information on the risk of traffic accidents and household hazards, and can also provide risk information based on the child's location information. Step 3: The play suggestion department suggests games based on the information provided by the risk information department. For example, it can use a generation AI to suggest indoor games, outdoor games, educational games, etc., and can suggest games that are appropriate for the child's age and interests. Step 4: The Dream Support Department proposes actions toward future dreams based on the results analyzed by the Behavioral Analysis Department. For example, using generative AI, it can suggest vocational experiences and skill acquisition, and propose specific actions toward the child's dreams.

[0066] (Example of form 2) The childcare support system according to an embodiment of the present invention is a system that analyzes a child's age-appropriate behavior, provides danger information, suggests play activities, and supports their dreams. This system collects information such as things a child should not do at their age, actions they should take, danger information inferred from location information, danger information obtained in cooperation with the police and local government, playmates when parents are doing housework, things that are difficult for parents to ask, and poor health, and a generating AI analyzes this information. Next, the generating AI provides the child with appropriate actions and precautions based on the analysis results. It also supports the child's dreams and introduces appropriate routes toward their future career aspirations. For example, the generating AI analyzes things a child should not do and actions they should take at their age and provides this information to the child. For example, it may present dangerous behaviors and recommended behaviors for a specific age. Next, based on the child's location information, the generating AI analyzes danger information and provides warnings. For example, if a child approaches a dangerous place, it warns them with pictures and sounds that are easy for children to understand, tailored to the terrain. It also collects local danger information in cooperation with the police and local government and provides it to the child. Furthermore, the generating AI suggests appropriate play activities to keep the child entertained while parents are doing housework. For example, it suggests suitable activities for when a child is playing alone. Furthermore, the AI ​​analyzes questions that children might find difficult to ask their parents or concerns about their health, providing appropriate advice. For example, if a child asks a question they might find difficult to ask their parents, the AI ​​will provide an appropriate answer. Finally, it supports children's dreams and introduces appropriate routes towards their desired future careers. For example, if a child wants to become a YouTuber, the AI ​​will suggest the actions and skills they should acquire. In this way, the system supports children's dreams and provides concrete advice for achieving their future goals. This enables the parenting support system to perform behavioral analysis, provide danger information, suggest play activities, and support children's dreams, all tailored to their age.

[0067] The childcare support system according to this embodiment comprises a behavior analysis unit, a danger information provision unit, a play suggestion unit, and a dream support unit. The behavior analysis unit analyzes the child's behavior according to their age. The behavior analysis unit analyzes the child's behavior according to their age, for example, using a generative AI. For example, the behavior analysis unit can analyze the behavior of toddlers, the behavior of elementary school children, etc. The behavior analysis unit can analyze the child's behavior patterns using a generative AI and suggest appropriate actions. The danger information provision unit provides danger information based on the results analyzed by the behavior analysis unit. The danger information provision unit provides danger information, for example, using a generative AI. For example, the danger information provision unit can provide information on the risk of traffic accidents, dangers within the home, etc. The danger information provision unit can provide danger information based on the child's location information using a generative AI. The play suggestion unit suggests play based on the information provided by the danger information provision unit. The play suggestion unit suggests play, for example, using a generative AI. For example, the play suggestion unit can suggest indoor play, outdoor play, educational play, etc. The play suggestion unit can use generative AI to suggest play activities tailored to the child's age and interests. The dream support unit suggests actions toward future dreams based on the results analyzed by the behavioral analysis unit. For example, the dream support unit can suggest actions toward future dreams using generative AI. For example, the dream support unit can suggest career experiences, skill acquisition, etc. The dream support unit can use generative AI to suggest specific actions toward the child's dreams. As a result, the childcare support system according to the embodiment can perform behavioral analysis, provide danger information, suggest play activities, and support dreams, all tailored to the child's age.

[0068] The Behavioral Analysis Unit analyzes children's behavior according to their age. For example, it uses generative AI to analyze age-appropriate behavior. Specifically, the generative AI collects children's behavioral data and learns age-specific behavioral patterns. For example, behaviors in early childhood include learning to walk, language development, and types of play. Behaviors in elementary school children include school learning activities, interaction with friends, and sports activities. The generative AI analyzes this behavioral data and can suggest appropriate behaviors according to the child's developmental stage. Furthermore, the Behavioral Analysis Unit can analyze individual children's behavioral patterns and evaluate the impact of specific behaviors. For example, it can analyze how a particular type of play contributes to a child's cognitive development, or how a particular behavior affects the development of social skills. This allows the Behavioral Analysis Unit to provide concrete behavioral guidelines to support children's growth.

[0069] The Hazard Information Provision Department provides hazard information based on the results analyzed by the Behavioral Analysis Department. The Hazard Information Provision Department uses, for example, generative AI to provide hazard information. Specifically, the generative AI analyzes children's behavioral data and location information to identify potential hazards. For example, it can detect locations with a risk of traffic accidents or dangerous situations within the home. The generative AI can issue real-time warnings when a child approaches a specific location or takes a specific action. For example, if a child approaches a road, it can send a warning to the parent's smartphone to prompt caution. Furthermore, for hazard information within the home, it can issue warnings when approaching dangerous areas such as stairs or the kitchen. This allows the Hazard Information Provision Department to provide crucial information to ensure children's safety and prevent accidents and injuries. In addition, the Hazard Information Provision Department can analyze hazard trends based on past data and propose long-term safety measures. For example, it can analyze the frequency of accidents at specific times or locations and implement preventative measures. This allows the Hazard Information Provision Department to comprehensively support children's safety.

[0070] The Play Suggestion Department suggests activities based on information provided by the Risk Information Provision Department. The Play Suggestion Department uses, for example, generative AI to suggest activities. Specifically, the generative AI suggests activities tailored to the child's age, interests, and current situation. For example, indoor activities might include building blocks, reading picture books, and puzzles. Outdoor activities might include playing in parks, sports, and nature observation. Educational activities might include learning games, science experiments, and art activities. The generative AI can select and suggest the most suitable activities to parents based on the child's interests and developmental stage. Furthermore, the Play Suggestion Department can record the child's play history and evaluate the effectiveness of past activities. For example, it can analyze how a particular activity contributed to the child's cognitive development and social skills and reflect this in future activity suggestions. This allows the Play Suggestion Department to provide effective activities that promote the child's growth. Additionally, the Play Suggestion Department can suggest activities suitable for various situations, such as activities that parents and children can enjoy together, or activities that friends can enjoy. This allows the Play Suggestion Department to improve the quality of children's play and provide enjoyable experiences.

[0071] The Dream Support Department proposes actions toward future dreams based on the results analyzed by the Behavioral Analysis Department. For example, the Dream Support Department uses generative AI to propose actions toward future dreams. Specifically, the generative AI analyzes the child's interests, talents, and current behavioral patterns and proposes specific actions toward future dreams. For example, if the child is interested in science, it can suggest science experiments, visits to science museums, and reading science-related books. For career experience, it can suggest programs that allow children to experience professions related to their dreams, such as doctors, engineers, and artists. For skill acquisition, it can suggest specific activities to acquire the skills necessary for the child's dreams, such as programming, playing musical instruments, and sports training. Furthermore, the Dream Support Department can create long-term plans toward the child's dreams and show parents and children specific steps. For example, it can support the child in steadily progressing toward their dreams through goal setting, progress management, and the provision of feedback. In this way, the Dream Support Department can provide specific action guidelines for realizing the child's future dreams and support the child's growth and development.

[0072] The location information acquisition unit can acquire location information. The location information acquisition unit can acquire location information using, for example, a generating AI. For example, the location information acquisition unit can acquire GPS information, Wi-Fi location information, etc. The location information acquisition unit can acquire the child's current location using a generating AI and provide appropriate information. This makes it possible to provide information based on the child's current location by acquiring location information. Some or all of the above processing in the location information acquisition unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the location information acquisition unit can input GPS information into a generating AI and have the generating AI perform location information analysis.

[0073] The Danger Information Provision Department can collect and provide local danger information in cooperation with the police and local governments. The Danger Information Provision Department can collect and provide local danger information using, for example, generative AI. For example, the Danger Information Provision Department can collect and provide crime information, disaster information, etc. The Danger Information Provision Department can analyze local danger information using generative AI and provide it to children. In this way, local danger information can be provided in cooperation with the police and local governments. Some or all of the above processing in the Danger Information Provision Department may be performed using generative AI, or may not be performed using generative AI. For example, the Danger Information Provision Department can input danger information obtained from the police and local governments into generative AI and have the generative AI perform the analysis of the danger information.

[0074] The play suggestion unit can suggest appropriate games for children to play alone while their parents are doing housework. The play suggestion unit can suggest appropriate games using, for example, generative AI. For example, the play suggestion unit can suggest age-appropriate games, safe games, etc. The play suggestion unit can suggest games that are appropriate for the child's age and interests using generative AI. This allows the unit to suggest appropriate games for children to play alone while their parents are doing housework. Some or all of the above processing in the play suggestion unit may be performed using generative AI or not. For example, the play suggestion unit can input data about the child's age and interests into the generative AI and have the generative AI suggest appropriate games.

[0075] The Dream Support Department can suggest actions and skills that will help children achieve their future career aspirations. For example, the Dream Support Department can use generative AI to suggest these actions and skills. For instance, it can suggest learning skills, social skills, and more. The Dream Support Department can use generative AI to suggest specific actions and skills that will help children achieve their dreams. This allows the Dream Support Department to suggest actions and skills that will help children achieve their future career aspirations. Some or all of the above-described processes in the Dream Support Department may be performed using generative AI, or they may not. For example, the Dream Support Department can input data about a child's dreams into a generative AI and have the AI ​​suggest appropriate actions and skills.

[0076] The behavioral analysis unit can analyze children's health issues and matters that parents may find difficult to discuss, and provide appropriate advice. For example, the behavioral analysis unit can use generative AI to analyze children's health issues and matters that parents may find difficult to discuss. For instance, the behavioral analysis unit can analyze health issues such as fever and stomachaches and provide appropriate advice. The behavioral analysis unit can also use generative AI to analyze matters that parents may find difficult to discuss, such as sex education and bullying, and provide appropriate advice. This allows for the provision of appropriate advice regarding children's health issues and matters that parents may find difficult to discuss. Some or all of the above-described processes in the behavioral analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the behavioral analysis unit can input data on children's health issues into a generative AI and have the generative AI provide appropriate advice.

[0077] The behavioral analysis unit can estimate a child's emotions and improve the accuracy of the behavioral analysis based on the estimated emotions. The behavioral analysis unit can estimate a child's emotions using, for example, a generative AI. For example, if a child is feeling anxious, the generative AI can detect that emotion and adjust the analysis results to account for the anxiety factor during the behavioral analysis. If a child is excited, the generative AI can detect that emotion and adjust the analysis results to account for the excitement factor during the behavioral analysis. If a child is tired, the generative AI can detect that emotion and adjust the analysis results to account for the fatigue factor during the behavioral analysis. This allows the accuracy of the behavioral analysis to be improved based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the behavioral analysis unit may be performed using a generative AI or not. For example, the behavioral analysis unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The behavioral analysis unit can analyze a child's past behavioral history and predict future behavior. For example, the behavioral analysis unit can use generative AI to analyze past behavioral history. For instance, the behavioral analysis unit can use generative AI to analyze a child's frequently performed past behavioral patterns and predict future behavior. The behavioral analysis unit can use generative AI to analyze a child's past behaviors at specific time periods and predict potential future behaviors at the same time periods. The behavioral analysis unit can use generative AI to analyze a child's past behaviors at specific locations and predict potential future behaviors at the same locations. In this way, future behavior can be predicted by analyzing a child's past behavioral history. Some or all of the above-described processes in the behavioral analysis unit may be performed using generative AI, or they may not. For example, the behavioral analysis unit can input past behavioral history data into a generative AI and have the generative AI perform predictions of future behavior.

[0079] The behavioral analysis unit can adjust the analysis results while considering the child's current health condition during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to adjust the analysis results while considering the child's current health condition. For example, if the child has a cold, the generative AI can detect this health condition and adjust the analysis results while considering the health condition during behavioral analysis. If the child is tired, the generative AI can detect this health condition and adjust the analysis results while considering the fatigue level during behavioral analysis. If the child is injured, the generative AI can detect this health condition and adjust the analysis results while considering the injury level during behavioral analysis. By adjusting the analysis results while considering the child's current health condition, more accurate behavioral analysis becomes possible. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the behavioral analysis unit can input the child's health condition data into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0080] The behavioral analysis unit can estimate a child's emotions and adjust the method of displaying the behavioral analysis results based on the estimated emotions. The behavioral analysis unit can estimate a child's emotions, for example, using generative AI. For example, if a child is feeling anxious, the generative AI can detect that emotion and provide the behavioral analysis results in a simple and reassuring display. If a child is excited, the generative AI can detect that emotion and provide the behavioral analysis results in a visually stimulating display. If a child is tired, the generative AI can detect that emotion and provide the behavioral analysis results in a highly visible display. By adjusting the method of displaying the behavioral analysis results based on the child's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the behavioral analysis unit may be performed using or without the generative AI. For example, the behavioral analysis unit can input children's emotional data into a generating AI and have the generating AI adjust the display method.

[0081] The behavioral analysis unit can provide analysis results that take into account a child's learning history during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to provide analysis results that take into account the learning history. For example, the behavioral analysis unit can have a generative AI analyze what a child has learned in the past and provide analysis results that take into account the learning history during behavioral analysis. The behavioral analysis unit can have a generative AI analyze what a child has learned in a specific subject and provide analysis results that take into account the learning history for that subject during behavioral analysis. The behavioral analysis unit can have a generative AI analyze learning events that a child has participated in in the past and provide analysis results that take into account the learning history for those events during behavioral analysis. This makes it possible to perform more appropriate behavioral analysis by providing analysis results that take into account the child's learning history. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI or not. For example, the behavioral analysis unit can input a child's learning history data into a generative AI and have the generative AI perform the provision of analysis results.

[0082] The behavioral analysis unit can provide analysis results that take into account the child's friendships during behavioral analysis. For example, the behavioral analysis unit can use a generative AI to provide analysis results that take friendships into account. For example, the behavioral analysis unit can use a generative AI to analyze information about friends the child has played with in the past and provide analysis results that take friendships into account during behavioral analysis. The behavioral analysis unit can use a generative AI to analyze the amount of time the child has spent with a specific friend and provide analysis results that take that friendship into account during behavioral analysis. The behavioral analysis unit can use a generative AI to analyze activities the child has done with friends in the past and provide analysis results that take those activities into account during behavioral analysis. By providing analysis results that take the child's friendships into account, more appropriate behavioral analysis becomes possible. Some or all of the above processing in the behavioral analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the behavioral analysis unit can input the child's friendship data into a generative AI and have the generative AI perform the task of providing analysis results.

[0083] The risk information provision unit can estimate a child's emotions and adjust the method of providing risk information based on the estimated emotions. The risk information provision unit can estimate a child's emotions, for example, using a generative AI. For example, if a child is feeling anxious, the generative AI can detect that emotion and provide risk information in a reassuring way. If a child is excited, the generative AI can detect that emotion and provide risk information in a visually stimulating way. If a child is tired, the generative AI can detect that emotion and provide risk information in a highly visible way. By adjusting the method of providing risk information based on a child's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the risk information provision unit may be performed using a generative AI or not. For example, the risk information provision department can input children's emotional data into a generating AI and have the AI ​​adjust the method of providing that information.

[0084] The hazard information provision unit can predict current hazards by referring to past hazard information data when providing hazard information. The hazard information provision unit can predict current hazards by referring to past hazard information data, for example, using a generating AI. For example, the hazard information provision unit can have a generating AI analyze past hazard information and predict current hazards. The hazard information provision unit can have a generating AI predict hazards at a specific location based on past hazard information data. The hazard information provision unit can have a generating AI predict hazards during a specific time period based on past hazard information data. In this way, current hazards can be predicted by referring to past hazard information data. Some or all of the above processing in the hazard information provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the hazard information provision unit can input past hazard information data into a generating AI and have the generating AI perform a prediction of current hazards.

[0085] The hazard information provision unit can provide warnings while considering the child's current activities when providing hazard information. The hazard information provision unit can, for example, use a generative AI to provide warnings while considering the current activities. For example, if the child is playing, the generative AI can detect that activity and provide hazard information related to play. If the child is moving, the generative AI can detect that activity and provide hazard information related to movement. If the child is studying, the generative AI can detect that activity and provide hazard information related to studying. This makes it possible to provide more appropriate information by providing warnings while considering the child's current activities. Some or all of the above processing in the hazard information provision unit may be performed using a generative AI or not. For example, the hazard information provision unit can input data on the child's activities into a generative AI and have the generative AI perform the task of providing warnings.

[0086] The risk information provision unit can estimate a child's emotions and determine the priority of risk information based on the estimated emotions. The risk information provision unit can estimate a child's emotions using, for example, a generative AI. For example, if the child is feeling anxious, the generative AI can detect that emotion and set the priority of the risk information higher. If the child is excited, the generative AI can detect that emotion and adjust the priority of the risk information. If the child is tired, the generative AI can detect that emotion and set the priority of the risk information lower. This makes it possible to provide more appropriate information by determining the priority of risk information based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the risk information provision unit may be performed using a generative AI or not. For example, the risk information provision unit can input child emotion data into a generative AI and have the generative AI perform the priority determination.

[0087] The risk information provision unit can prioritize providing highly relevant information by considering the child's geographical location when providing risk information. For example, the risk information provision unit can use a generating AI to prioritize providing highly relevant information by considering geographical location. For example, if the child is in a specific location, the generating AI can detect that geographical location and prioritize providing risk information related to that location. If the child is on the move, the generating AI can detect that geographical location and prioritize providing risk information related to the travel route. If the child is in a specific region, the generating AI can detect that geographical location and prioritize providing risk information related to that region. This makes it possible to provide more appropriate information by prioritizing the provision of highly relevant information by considering the child's geographical location. Some or all of the above processing in the risk information provision unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the risk information provision unit can input the child's geographical location data into a generating AI and have the generating AI perform the provision of highly relevant information.

[0088] The risk information provision unit can analyze a child's social media activity and provide relevant risk information when providing risk information. The risk information provision unit can, for example, use generative AI to analyze social media activity and provide relevant risk information. For example, if a child checks in to a specific location on social media, the generative AI can detect that information and provide risk information related to that location. If a child posts on social media that they are attending a specific event, the generative AI can detect that information and provide risk information related to that event. If a child posts on social media that they are with a specific friend, the generative AI can detect that information and provide risk information related to that friend. In this way, relevant risk information can be provided by analyzing a child's social media activity. Some or all of the above processing in the risk information provision unit may be performed using generative AI or not. For example, the risk information provision unit can input a child's social media activity data into a generative AI and have the generative AI perform the provision of relevant risk information.

[0089] The play suggestion unit can estimate a child's emotions and adjust its play suggestion method based on the estimated emotions. For example, the play suggestion unit can estimate a child's emotions using generative AI. For instance, if a child is feeling anxious, the generative AI can detect this emotion and suggest a comforting game. If a child is excited, the generative AI can detect this emotion and suggest a game that allows them to release energy. If a child is tired, the generative AI can detect this emotion and suggest a relaxing game. This allows for more appropriate information to be provided by adjusting the play suggestion method based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the play suggestion unit may be performed using or without generative AI. For example, the play suggestion unit can input child emotion data into the generative AI and have the generative AI adjust the suggestion method.

[0090] The play suggestion unit can suggest the most suitable game by referring to the child's past play history when suggesting a game. For example, the play suggestion unit can use a generative AI to refer to the past play history and suggest the most suitable game. For example, the play suggestion unit can use a generative AI to analyze games the child has enjoyed in the past and suggest similar games. The play suggestion unit can use a generative AI to analyze play events the child has participated in in the past and suggest similar events. The play suggestion unit can use a generative AI to analyze places the child has played in the past and suggest games related to those places. In this way, the optimal game can be suggested by referring to the child's past play history. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input the child's past play history data into a generative AI and have the generative AI suggest the most suitable game.

[0091] The play suggestion unit can customize its suggestions by taking into account the child's current interests. For example, the play suggestion unit can use a generative AI to customize the suggestions by taking into account the child's current interests. For example, the generative AI can analyze the themes the child is currently interested in and suggest games related to those themes. The generative AI can analyze the characters the child is currently interested in and suggest games related to those characters. The generative AI can analyze the activities the child is currently interested in and suggest games related to those activities. By customizing the suggestions by taking into account the child's current interests, more appropriate games can be suggested. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input data on the child's current interests into a generative AI and have the generative AI customize the suggestions.

[0092] The play suggestion unit can estimate a child's emotions and determine play priorities based on those estimated emotions. For example, the play suggestion unit can use generative AI to estimate a child's emotions. For instance, if a child is feeling anxious, the generative AI can detect this emotion and prioritize suggesting safe and comforting play activities. If a child is excited, the generative AI can detect this emotion and prioritize suggesting play activities that allow the child to release energy. If a child is tired, the generative AI can detect this emotion and prioritize suggesting relaxing play activities. This allows for more appropriate information to be provided by prioritizing play based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the play suggestion unit may be performed using or without generative AI. For example, the play suggestion unit can input child emotion data into a generative AI and have the generative AI determine the priorities.

[0093] The play suggestion unit can suggest the most suitable play activity by considering the child's geographical location. For example, the play suggestion unit can use a generative AI to suggest the most suitable play activity by considering the geographical location. For example, if the child is in a park, the generative AI can detect that geographical location and suggest activities that can be done in the park. If the child is at home, the generative AI can detect that geographical location and suggest activities that can be done at home. If the child is at a friend's house, the generative AI can detect that geographical location and suggest activities that can be done at the friend's house. By suggesting the most suitable play activity by considering the child's geographical location, it becomes possible to provide more appropriate information. Some or all of the above processing in the play suggestion unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the play suggestion unit can input the child's geographical location data into a generative AI and have the generative AI perform the task of suggesting the most suitable play activity.

[0094] The play suggestion unit can suggest group games that take into account the child's friendships when suggesting games. For example, the play suggestion unit can use a generative AI to suggest group games that take friendships into account. For example, if the child is with friends, the generative AI can detect those friendships and suggest games that can be enjoyed as a group. If the child is spending time with a specific friend, the generative AI can detect those friendships and suggest games that can be enjoyed with that friend. If the child has plans to play with friends, the generative AI can detect those friendships and suggest games that can be enjoyed as a group in advance. This makes it possible to provide more appropriate information by suggesting group games that take the child's friendships into account. Some or all of the above processing in the play suggestion unit may be performed using a generative AI or not. For example, the play suggestion unit can input data on the child's friendships into a generative AI and have the generative AI suggest group games.

[0095] The dream support unit can estimate a child's emotions and adjust the dream support method based on the estimated emotions. The dream support unit can estimate a child's emotions using, for example, a generative AI. For example, if a child is feeling anxious, the generative AI can detect that emotion and provide a reassuring dream support method. If a child is excited, the generative AI can detect that emotion and provide a dream support method that allows them to release their energy. If a child is tired, the generative AI can detect that emotion and provide a relaxing dream support method. By adjusting the dream support method based on the child's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the dream support unit may be performed using a generative AI or not. For example, the dream support unit can input child emotion data into a generative AI and have the generative AI adjust the support method.

[0096] The Dream Support Department can suggest optimal actions and skills by referring to a child's past learning history during Dream Support. For example, the Dream Support Department can use a generative AI to suggest optimal actions and skills by referring to a child's past learning history. For example, the Dream Support Department can have the generative AI analyze what a child has learned in the past and suggest optimal actions and skills by considering the learning history during Dream Support. The Dream Support Department can have the generative AI analyze what a child has learned in a specific subject and suggest optimal actions and skills by considering the learning history for that subject during Dream Support. The Dream Support Department can have the generative AI analyze learning events a child has participated in in the past and suggest optimal actions and skills by considering the learning history for those events during Dream Support. In this way, optimal actions and skills can be suggested by referring to a child's past learning history. Some or all of the above processes in the Dream Support Department may be performed using a generative AI or not. For example, the Dream Support Department can input data on a child's past learning history into a generative AI and have the generative AI suggest optimal actions and skills.

[0097] The Dream Support Department can customize its suggestions during dream support sessions by taking into account the child's current interests. For example, the Dream Support Department can use a generative AI to customize suggestions based on the child's current interests. For instance, the generative AI can analyze the themes the child is currently interested in and suggest actions and skills related to those themes. The generative AI can analyze the characters the child is currently interested in and suggest actions and skills related to those characters. The generative AI can analyze the activities the child is currently interested in and suggest actions and skills related to those activities. By customizing suggestions based on the child's current interests, more appropriate information can be provided. Some or all of the above-described processes in the Dream Support Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Dream Support Department can input data on the child's current interests into a generative AI and have the generative AI customize the suggestions.

[0098] The dream support unit can estimate a child's emotions and prioritize dream support based on those emotions. For example, the dream support unit can use generative AI to estimate a child's emotions. For instance, if a child is feeling anxious, the generative AI can detect this emotion and prioritize providing reassuring dream support. If a child is excited, the generative AI can detect this emotion and prioritize providing dream support that helps release energy. If a child is tired, the generative AI can detect this emotion and prioritize providing relaxing dream support. This allows for more appropriate information to be provided by prioritizing dream support based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the dream support unit may be performed using or without generative AI. For example, the Dream Support Department can input children's emotional data into a generating AI and have the AI ​​determine the priorities.

[0099] The Dream Support Department can suggest optimal actions and skills while considering the child's geographical location during dream support. For example, the Dream Support Department can use generative AI to suggest optimal actions and skills while considering geographical location. For example, if the child is in a specific location, the generative AI can detect that location and suggest actions and skills related to that location. If the child is on the move, the generative AI can detect that location and suggest actions and skills related to the travel route. If the child is in a specific region, the generative AI can detect that region and suggest actions and skills related to that region. This makes it possible to provide more appropriate information by suggesting optimal actions and skills while considering the child's geographical location. Some or all of the above processing in the Dream Support Department may be performed using generative AI, or it may be performed without using generative AI. For example, the Dream Support Department can input the child's geographical location data into the generative AI and have the generative AI suggest optimal actions and skills.

[0100] The Dream Support Department can suggest activities that children can engage in together, taking into account their friendships, when providing dream support. For example, the Dream Support Department can use a generative AI to suggest activities that children can engage in together, taking their friendships into consideration. For example, if a child is with a friend, the generative AI can detect that friendship and suggest activities that children can engage in together. If a child is spending time with a specific friend, the generative AI can detect that friendship and suggest activities that children can engage in together with that friend. If a child has plans to play with a friend, the generative AI can detect that friendship and suggest activities that children can engage in together in advance. This allows for the provision of more appropriate information by suggesting activities that children can engage in together, taking their friendships into consideration. Some or all of the above-described processes in the Dream Support Department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the Dream Support Department can input data on the child's friendships into a generative AI and have the generative AI suggest activities that children can engage in together.

[0101] The location information acquisition unit can estimate a child's emotions and adjust the timing of location information acquisition based on the estimated emotions. The location information acquisition unit can estimate a child's emotions using, for example, a generative AI. For example, if a child is feeling anxious, the generative AI can detect that emotion and acquire location information more frequently. If a child is excited, the generative AI can detect that emotion and adjust the timing of location information acquisition. If a child is tired, the generative AI can detect that emotion and reduce the frequency of location information acquisition. By adjusting the timing of location information acquisition based on the child's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the location information acquisition unit may be performed using a generative AI or not. For example, the location information acquisition unit can input child emotion data into a generative AI and have the generative AI adjust the acquisition timing.

[0102] The location information acquisition unit can select the optimal acquisition method by referring to the child's past movement history when acquiring location information. For example, the location information acquisition unit can use a generating AI to refer to past movement history and select the optimal acquisition method. For example, the generating AI can analyze places the child has frequently visited in the past and select the location information acquisition method for those locations. The generating AI can analyze the child's past movement history during specific time periods and select the location information acquisition method for those time periods. The generating AI can analyze the child's past travel history along specific routes and select the location information acquisition method for those routes. In this way, the optimal location information acquisition method can be selected by referring to the child's past movement history. Some or all of the above processing in the location information acquisition unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the location information acquisition unit can input the child's past movement history data into a generating AI and have the generating AI select the optimal acquisition method.

[0103] The location information acquisition unit can estimate a child's emotions and determine the priority of location information based on the estimated emotions. The location information acquisition unit can estimate a child's emotions using, for example, a generative AI. For example, if the child is feeling anxious, the generative AI can detect that emotion and set the priority of the location information higher. If the child is excited, the generative AI can detect that emotion and adjust the priority of the location information. If the child is tired, the generative AI can detect that emotion and set the priority of the location information lower. This makes it possible to provide more appropriate information by determining the priority of location information based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the location information acquisition unit may be performed using a generative AI or not. For example, the location information acquisition unit can input child emotion data into a generative AI and have the generative AI perform the priority determination.

[0104] The location information acquisition unit can select the optimal acquisition method when acquiring location information, taking into account the child's current activities. For example, the location information acquisition unit can use a generative AI to select the optimal acquisition method, taking into account the child's current activities. For example, if the child is playing, the generative AI can detect that activity and select a location information acquisition method related to playing. If the child is moving, the generative AI can detect that activity and select a location information acquisition method related to movement. If the child is studying, the generative AI can detect that activity and select a location information acquisition method related to studying. By selecting the optimal location information acquisition method, taking into account the child's current activities, it becomes possible to provide more appropriate information. Some or all of the above processing in the location information acquisition unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the location information acquisition unit can input data on the child's current activities into a generative AI and have the generative AI select the optimal acquisition method.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The behavioral analysis unit can consider a child's learning style when analyzing their behavioral patterns. For example, using generative AI, the behavioral analysis unit can provide behavioral suggestions that include a lot of visual information if the child is a visual learner. If the child is an auditory learner, it can provide behavioral suggestions that include a lot of audio. Furthermore, if the child is an experiential learner, it can provide behavioral suggestions that involve physical movement. This makes it possible to provide behavioral suggestions that are tailored to the child's learning style, enabling more effective support.

[0107] The location information acquisition unit can select the optimal acquisition method when acquiring a child's location information, taking into account the child's activity history. For example, the location information acquisition unit can use a generation AI to analyze places the child has frequently visited in the past and select the location information acquisition method for those locations. Furthermore, if the child tends to move around during specific time periods, the unit can select a location information acquisition method that suits those time periods. In addition, if the child frequently uses a specific route, the unit can select a location information acquisition method that suits that route. This makes it possible to acquire optimal location information while taking into account the child's activity history.

[0108] The danger information provision unit can estimate a child's emotions and adjust the method of providing danger information based on those emotions. For example, using generative AI, the unit can provide danger information in a reassuring way if the child is feeling anxious. If the child is excited, it can provide danger information in a visually stimulating way. Furthermore, if the child is tired, it can provide danger information in a highly visible way. By adjusting the method of providing danger information based on the child's emotions, it becomes possible to provide more appropriate information.

[0109] The play suggestion unit can estimate a child's emotions and adjust its play suggestion methods based on those estimates. For example, using generative AI, the play suggestion unit can suggest comforting play if a child is feeling anxious. It can also suggest play that allows a child to release energy if they are excited. Furthermore, it can suggest relaxing play if a child is tired. By adjusting play suggestion methods based on a child's emotions, it becomes possible to provide more appropriate information.

[0110] The Dream Support Department can estimate a child's emotions and adjust its dream support methods based on those estimates. For example, using a generative AI, the Dream Support Department can provide a reassuring dream support method if the child is feeling anxious. It can also provide a dream support method that allows the child to release energy if they are excited. Furthermore, it can provide a relaxing dream support method if the child is tired. By adjusting the dream support method based on the child's emotions, it becomes possible to provide more appropriate information.

[0111] The behavioral analysis unit can analyze a child's past behavioral history and predict future behavior. For example, it can use generative AI to analyze a child's frequently occurring behavioral patterns in the past and predict future behavior. It can also analyze a child's behavior at specific times of day and predict behaviors they might exhibit at the same time of day in the future. Furthermore, it can analyze a child's behavior at specific locations and predict behaviors they might exhibit at the same locations in the future. In this way, by analyzing a child's past behavioral history, future behavior can be predicted.

[0112] The hazard information provision department can predict current hazards by referring to past hazard information data when providing hazard information. For example, the hazard information provision department can use generation AI to analyze past hazard information data and predict current hazards. It can also predict hazards in specific locations. Furthermore, it can predict hazards in specific time periods. In this way, current hazards can be predicted by referring to past hazard information data.

[0113] The play suggestion function can suggest the most suitable play activities by referring to the child's past play history. For example, it can use generative AI to analyze games the child has enjoyed in the past and suggest similar games. It can also analyze play events the child has participated in in the past and suggest similar events. Furthermore, it can analyze places the child has played in the past and suggest games related to those places. In this way, by referring to the child's past play history, it can suggest the most suitable play activities.

[0114] The Dream Support Department can suggest optimal actions and skills during dream support sessions by referencing a child's past learning history. For example, using generative AI, the Dream Support Department can analyze what a child has learned in the past and suggest optimal actions and skills during dream support sessions, taking their learning history into consideration. It can also analyze what a child has learned in a specific subject and suggest optimal actions and skills, taking their learning history in that subject into consideration. Furthermore, it can analyze past learning events the child has participated in and suggest optimal actions and skills, taking their learning history in those events into consideration. In this way, by referring to a child's past learning history, it can suggest optimal actions and skills.

[0115] The location information acquisition unit can estimate the child's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, using a generative AI, the location information acquisition unit can acquire location information more frequently if the child is feeling anxious. It can also adjust the timing of location information acquisition if the child is excited. Furthermore, it can reduce the frequency of location information acquisition if the child is tired. By adjusting the timing of location information acquisition based on the child's emotions, it becomes possible to provide more appropriate information.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The behavioral analysis unit analyzes the child's behavior according to their age. For example, it can use generative AI to analyze the behavior of toddlers and elementary school children, analyze the child's behavioral patterns, and suggest appropriate actions. Step 2: The risk information provision unit provides risk information based on the results analyzed by the behavioral analysis unit. For example, it can use a generation AI to provide information on the risk of traffic accidents and household hazards, and can also provide risk information based on the child's location information. Step 3: The play suggestion department suggests games based on the information provided by the risk information department. For example, it can use a generation AI to suggest indoor games, outdoor games, educational games, etc., and can suggest games that are appropriate for the child's age and interests. Step 4: The Dream Support Department proposes actions toward future dreams based on the results analyzed by the Behavioral Analysis Department. For example, using generative AI, it can suggest vocational experiences and skill acquisition, and propose specific actions toward the child's dreams.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the behavior analysis unit, risk information provision unit, play suggestion unit, dream support unit, and location information acquisition unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the behavior analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The risk information provision unit is implemented by the specific processing unit 290 of the data processing device 12. The play suggestion unit is implemented by the control unit 46A of the smart device 14. The dream support unit is implemented by the specific processing unit 290 of the data processing device 12. The location information acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. 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.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the behavior analysis unit, risk information provision unit, play suggestion unit, dream support unit, and location information acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the behavior analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The risk information provision unit is implemented by the specific processing unit 290 of the data processing device 12. The play suggestion unit is implemented by the control unit 46A of the smart glasses 214. The dream support unit is implemented by the specific processing unit 290 of the data processing device 12. The location information acquisition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the behavior analysis unit, risk information provision unit, play suggestion unit, dream support unit, and location information acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the behavior analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The risk information provision unit is implemented by the specific processing unit 290 of the data processing unit 12. The play suggestion unit is implemented by the control unit 46A of the headset terminal 314. The dream support unit is implemented by the specific processing unit 290 of the data processing unit 12. The location information acquisition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the behavior analysis unit, danger information provision unit, play suggestion unit, dream support unit, and location information acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the behavior analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The danger information provision unit is implemented by the specific processing unit 290 of the data processing unit 12. The play suggestion unit is implemented by the control unit 46A of the robot 414. The dream support unit is implemented by the specific processing unit 290 of the data processing unit 12. The location information acquisition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) The behavioral analysis unit analyzes children's behavior according to their age, A risk information provision unit provides risk information based on the results of the analysis performed by the aforementioned behavioral analysis unit, Based on the information provided by the aforementioned risk information provision department, the play suggestion department proposes games, The system includes a dream support unit that proposes actions toward future dreams based on the results of analysis by the aforementioned behavioral analysis unit. A system characterized by the following features. (Note 2) It includes a location information acquisition unit that acquires location information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned risk information provision department, We collect and provide information on local dangers in cooperation with the police and local governments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned play suggestion unit is, Suggesting appropriate games for children to play alone while their parents are doing housework. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Dream Support Department Suggesting actions and skills that will help children achieve their future career aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned behavioral analysis unit, We analyze children's health issues and other topics that parents may find difficult to discuss, and provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned behavioral analysis unit, This system estimates children's emotions and improves the accuracy of behavioral analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned behavioral analysis unit, Analyzing a child's past behavioral history to predict future behavior The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned behavioral analysis unit, When performing behavioral analysis, the analysis results are adjusted to take into account the child's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned behavioral analysis unit, Adjust the method for estimating a child's emotions and displaying the results of behavioral analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned behavioral analysis unit, When analyzing behavior, the analysis results are provided taking into account the child's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned behavioral analysis unit, When analyzing behavior, we provide analysis results that take into account the child's friendships. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned risk information provision department, The system estimates the child's emotions and adjusts how risk information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned risk information provision department, When providing hazard information, we refer to past hazard information data to predict the current risk. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned risk information provision department, When providing information about potential dangers, we will provide warnings that take into account the child's current activities. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned risk information provision department, The system estimates the child's emotions and prioritizes risk information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned risk information provision department, When providing danger information, we prioritize providing highly relevant information by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned risk information provision department, When providing risk information, we analyze children's social media activity and provide relevant risk information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned play suggestion unit is, The system estimates the child's emotions and adjusts the suggested play activities based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned play suggestion unit is, When suggesting play activities, we refer to the child's past play history to suggest the most suitable activity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned play suggestion unit is, When suggesting activities, customize the suggestions to take into account the child's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned play suggestion unit is, It estimates the child's emotions and determines play priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned play suggestion unit is, When suggesting activities, we take the child's geographical location into consideration to suggest the most suitable activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned play suggestion unit is, When suggesting games, consider the children's friendships and suggest games that can be played in groups. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Dream Support Department We estimate the child's emotions and adjust the dream support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Dream Support Department When providing dream support, we refer to the child's past learning history to suggest the most suitable actions and skills. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Dream Support Department When providing dream support, we customize the suggestions to take into account the child's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Dream Support Department The system estimates the child's emotions and prioritizes dream support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Dream Support Department When providing dream support, we suggest optimal actions and skills while considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Dream Support Department When providing dream support, we suggest activities that children can participate in together, taking into account their friendships. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned location information acquisition unit, The system estimates the child's emotions and adjusts the timing of location data acquisition based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned location information acquisition unit, When acquiring location information, the system selects the optimal acquisition method by referring to the child's past movement history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned location information acquisition unit, The system estimates the child's emotions and prioritizes location information based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned location information acquisition unit, When acquiring location information, the optimal acquisition method is selected considering the child's current activities. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0190] 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. The behavioral analysis unit analyzes children's behavior according to their age, A risk information provision unit provides risk information based on the results of the analysis performed by the aforementioned behavioral analysis unit, Based on the information provided by the aforementioned risk information provision department, the play suggestion department proposes games, The system includes a dream support unit that proposes actions toward future dreams based on the results of analysis by the aforementioned behavioral analysis unit. A system characterized by the following features.

2. It includes a location information acquisition unit that acquires location information. The system according to feature 1.

3. The aforementioned risk information provision department, We collect and provide information on local dangers in cooperation with the police and local governments. The system according to feature 1.

4. The aforementioned play suggestion unit is, Suggesting appropriate games for children to play alone while their parents are doing housework. The system according to feature 1.

5. The aforementioned Dream Support Department Suggesting actions and skills that will help children achieve their future career aspirations. The system according to feature 1.

6. The aforementioned behavioral analysis unit, We analyze children's health issues and other topics that parents may find difficult to discuss, and provide appropriate advice. The system according to feature 1.

7. The aforementioned behavioral analysis unit, This system estimates children's emotions and improves the accuracy of behavioral analysis based on those estimated emotions. The system according to feature 1.

8. The aforementioned behavioral analysis unit, Analyzing a child's past behavioral history to predict future behavior The system according to feature 1.

9. The aforementioned behavioral analysis unit, When performing behavioral analysis, the analysis results are adjusted to take into account the child's current health status. The system according to feature 1.

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