system

Abstract

An object of the system according to the embodiment is to secure safety of a small child at the time of going to a school or a school room.SOLUTION: A system includes a position monitoring unit, a voice instruction unit, a help function unit, and an evaluation unit. A position monitoring part registers a school route in advance and monitors the position of the child by using a GPS function together. The voice instruction unit issues a voice instruction based on the position information monitored by the position monitoring unit. The help function unit transmits a siren and a voice by pressing a help button in an emergency, and performs guidance to the nearest "house for helping children". The evaluation unit displays a simple map on a screen, and gives an evaluation point when the vehicle can safely pass through an intersection or a crosswalk.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient support to ensure the safety of young children when they commute to kindergarten or school, and there is room for improvement.

[0005] The system according to the embodiment aims to ensure the safety of small children when they go to kindergarten or school. [Means for solving the problem]

[0006] The system according to the embodiment comprises a location monitoring unit, a voice instruction unit, a help function unit, and an evaluation unit. The location monitoring unit registers the child's route to school in advance and monitors the child's location using a GPS function. The voice instruction unit issues voice instructions based on the location information monitored by the location monitoring unit. In an emergency, the help function unit emits a siren and a voice when the help button is pressed, and provides guidance to the nearest "child help home." The evaluation unit displays a simple map on the screen and awards evaluation points if the child safely passes through intersections and crosswalks. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the safety of small children when they go to kindergarten or school. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A batch-type communication tool according to an embodiment of the present invention is a system that supports the commute of young children who have just started kindergarten or elementary school. This system allows users to register their school route in advance, monitors the child's location using a GPS function, and generates voice instructions using AI. This allows the batch-type communication tool to support children's safe and enjoyable commute to kindergarten or school.

[0029] A batch-type communication tool according to an embodiment includes a location monitoring unit, a voice instruction unit, a help function unit, and an evaluation unit. The location monitoring unit registers a child's school route in advance and monitors the child's location using a GPS function. For example, a parent may set a school route using a smartphone app and register the route information in the batch-type communication tool. The voice instruction unit issues voice instructions based on the location information monitored by the location monitoring unit. For example, if a child strays far from the school route or stops moving from a certain point, the generation AI issues voice instructions such as "What's wrong?" or "That's not the way!". In an emergency, the help function unit activates a siren and a voice message saying "Help me!" when the help button is pressed, and provides guidance to the nearest "child rescuer." For example, if a child gets lost, the generation AI identifies the nearest safe location and provides voice guidance there. The evaluation unit displays a simple map on the screen and awards evaluation points if the child safely passes through intersections and crosswalks. For example, if a child takes the correct route to school and crosses the crosswalk safely, a star or point will be displayed on the screen. This allows the batch-based communication tool according to the embodiment to support children's safe and enjoyable commute to kindergarten or school. For example, even if a child strays from the school route, the AI ​​can respond immediately and guide the child back to the correct path. It can also respond quickly in emergencies to ensure the safety of children. Furthermore, incorporating game elements allows children to learn safe behaviors on their own initiative.

[0030] When registering a school route, parents take photos of the route, and the AI ​​analyzes the image data to automatically identify dangerous areas and display warnings. For example, parents take photos of the route with their smartphones and upload the image data to a batch communication tool. The AI ​​analyzes the images, evaluates road conditions and traffic volume, and identifies dangerous areas. For example, it detects intersections and crosswalks without traffic lights and displays warnings. When analyzing photos of the route, the AI ​​recognizes the presence or absence of road signs and sidewalks and evaluates the level of danger. For example, it identifies areas without sidewalks or areas with heavy vehicle traffic and displays warnings to parents. The AI ​​also uses image analysis technology to detect obstacles and construction sites in photos of the route and marks them as dangerous areas. For example, it identifies roads under construction or areas with a lot of parked vehicles and displays warnings. This improves safety by identifying dangerous areas on the route in advance and displaying warnings.

[0031] The location monitoring unit allows the generation AI to obtain weather information in real time based on the setting information of the school route and suggest an alternative route in the event of bad weather. For example, the location monitoring unit allows the generation AI to obtain weather information in real time based on the setting information of the school route and send a notification to parents in the event of bad weather. For example, if heavy rain or strong winds are forecast, the generation AI will suggest an alternative route. The location monitoring unit also allows the generation AI to evaluate the safety of the school route based on weather information and suggest an alternative route to parents if a dangerous situation is predicted. For example, if snow or ice is forecast, the generation AI will suggest a route that avoids slippery roads. The location monitoring unit also allows the generation AI to analyze weather information and evaluate the safety of each point on the school route. For example, it will identify areas at risk of flooding or areas where trees may be fallen due to strong winds and suggest an alternative route. This improves safety by suggesting alternative routes in bad weather.

[0032] The location monitoring unit can share registered school route information with other parents, building safe school routes throughout the community. For example, the location monitoring unit builds a platform for sharing registered school route information with other parents, building safe school routes throughout the community. For example, parents upload school route information and share it with other parents. The location monitoring unit also uses a generative AI to analyze registered school route information and suggest safe school routes to share with other parents. For example, if multiple parents use the same school route, it will suggest the safest route. The location monitoring unit also develops an app for sharing registered school route information, making it easy for parents to share school route information. For example, dangerous areas and safe spots on the school route can be shared with other parents. In this way, sharing school route information improves safety throughout the community.

[0033] The location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route and guide the user to places where the child can stop safely. For example, the location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route. For example, it displays information on convenience stores and parks where the child can stop safely. The location monitoring unit also allows the generation AI to analyze the set information of the school route and guide the user to safe places to stop along the school route. For example, it suggests places where the child can stop if they get lost. The location monitoring unit also allows the generation AI to provide information on stores and facilities along the school route in real time based on the set information of the school route. For example, it displays places where the child can stop safely on a map. This improves safety by guiding the user to safe places to stop along the school route.

[0034] The voice instruction unit allows the generation AI to analyze the tone and speed of a child's voice, determine the level of urgency, and issue appropriate instructions. For example, the generation AI analyzes the tone and speed of a child's voice in real time to determine the level of urgency. For example, if a child's voice is high-pitched and fast, it will determine the level of urgency and immediately issue appropriate instructions. The voice instruction unit also collects voice data to analyze the tone and speed of a child's voice and evaluates the level of urgency. For example, if a child's voice is trembling, it will determine the level of urgency and respond quickly. The voice instruction unit also develops an algorithm that analyzes the tone and speed of a child's voice and determines the level of urgency. For example, if a child's voice suddenly changes, it will determine the level of urgency and issue appropriate instructions. This enables a quick response by analyzing the tone and speed of a child's voice to determine the level of urgency and issuing appropriate instructions.

[0035] The voice instruction unit allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to get lost in a particular place, the generation AI will issue a warning instruction when the child approaches that place. The voice instruction unit also analyzes past data to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to be late during a particular time period, the generation AI will instruct the child to leave earlier during that time period. The voice instruction unit also develops an algorithm that learns a child's behavioral patterns and provides individually customized instructions. For example, if a child prefers a particular route, the generation AI will prioritize guidance along that route. This allows for more effective support by learning a child's behavioral patterns and providing individually customized instructions.

[0036] The voice instruction unit can add a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. The voice instruction unit, for example, adds a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. For example, instructions are issued in multiple languages, such as English, Spanish, and Chinese. To achieve multilingual support, the voice instruction unit has the generation AI learn voice data in each language and generate appropriate voice instructions. For example, if a child speaks English, instructions are issued in English. The voice instruction unit also adds a function that enables the generation AI to issue voice instructions in multiple languages, thereby developing an algorithm that achieves multilingual support. For example, if a child speaks a different language, instructions are issued in that language. This allows voice instructions to be issued in multiple languages, thereby achieving multilingual support and accommodating a wider range of users.

[0037] The voice instruction unit can add a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, the voice instruction unit adds a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, an animation can be displayed on the screen to give visual instructions to the child. In addition, the voice instruction unit allows the generation AI to generate animation data and link it to the voice instructions in order to provide visual instructions. For example, if a child gets lost, an animation showing directions can be displayed on the screen. In addition, the voice instruction unit develops an algorithm that allows the generation AI to provide a combination of voice instructions and visual instructions. For example, when a child approaches an intersection, an animation showing how to cross safely can be displayed on the screen. This allows for more effective support by providing not only voice instructions but also visual instructions.

[0038] The help function unit allows the generation AI to analyze surrounding environmental sounds in an emergency and propose the optimal response depending on the situation. For example, the help function unit allows the generation AI to analyze surrounding environmental sounds in real time in an emergency and propose the optimal response depending on the situation. For example, it can detect car horns or people screaming and give appropriate instructions. The help function unit also collects audio data to analyze surrounding environmental sounds and evaluates the emergency situation. For example, it can give instructions to evacuate in noisy places. The help function unit also develops an algorithm that allows the generation AI to analyze surrounding environmental sounds in an emergency and propose the optimal response. For example, it can detect the sound of a fire alarm and guide users to the evacuation route. This makes it possible to analyze surrounding environmental sounds in an emergency and propose the optimal response depending on the situation, enabling quick and appropriate response.

[0039] The help function unit enables the generating AI to send real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. The help function unit, for example, builds a system in which the generating AI sends real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. For example, if a child presses the help button, a notification is sent to the parent's smartphone. The help function unit also manages the contact information of parents or guardians to send emergency notifications quickly. For example, if a child gets lost, a notification including location information is sent to the parent. The help function unit also develops an algorithm in which the generating AI sends real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. For example, if a child is in a dangerous situation, an emergency notification is sent to the parent. This ensures the safety of children by sending real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly.

[0040] The help function unit will enable the generating AI to automatically notify nearby police and fire departments in the event of an emergency. The help function unit will add a function that enables the generating AI to automatically notify nearby police and fire departments in the event of an emergency. For example, if a child presses the help button, the generating AI will automatically notify the police and fire department. To achieve this automatic notification function, the generating AI will manage contact information for the police and fire departments and quickly notify them in the event of an emergency. For example, if a child is in danger, the generating AI will send a report including location information to the police. The help function unit will also develop an algorithm that will add a function that enables the generating AI to automatically notify nearby police and fire departments in the event of an emergency. For example, if a fire or accident occurs, the system will automatically notify the fire department. This will enable a quick response by automatically notifying nearby police and fire departments in the event of an emergency.

[0041] The help function unit enables the generation AI to send voice messages asking for help from nearby adults in an emergency. The help function unit adds a function that enables the generation AI to send voice messages asking for help from nearby adults in an emergency. For example, if a child presses the help button, a voice message yelling "Help me!" is sent. The help function unit also builds a system that enables the generation AI to send voice messages to ask for help from nearby adults. For example, if a child gets lost, a voice message saying "Please help this child" is sent. The help function unit also develops an algorithm that enables the generation AI to send voice messages asking for help from nearby adults in an emergency. For example, if a child is in a dangerous situation, the voice message saying "Help me" is sent repeatedly. This allows for quick assistance to be obtained by sending a voice message asking for help from nearby adults in an emergency.

[0042] The evaluation unit allows the generation AI to record the child's progress in completing the school route and set long-term goals, giving them a sense of accomplishment. For example, the evaluation unit allows the generation AI to record the child's progress in completing the school route and set long-term goals. For example, it could count the number of days a child is able to safely commute to school each day and give a reward if that goal is achieved for a certain period of time. In addition, the evaluation unit allows the generation AI to analyze the child's location information and evaluate the progress in completing the school route. For example, it could award points if the child takes the correct route. In addition, the evaluation unit develops an algorithm that allows the generation AI to record the child's progress in completing the school route and set long-term goals. For example, it could provide a special reward if the child commutes safely for one month. In this way, the generation AI records the child's progress in completing the school route and sets long-term goals, giving them a sense of accomplishment.

[0043] The evaluation unit allows the generation AI to evaluate a child's behavior on the way to school and send praising messages to increase motivation. For example, the evaluation unit allows the generation AI to evaluate a child's behavior on the way to school and send praising messages. For example, if the child crosses the crosswalk correctly, it sends a praising message saying, "Well done!". In addition, to evaluate behavior on the way to school, the generation AI analyzes the child's location information and behavioral data and sends praising messages at appropriate times. For example, if the child avoids a dangerous area, it may praise the child by saying, "That's great!". The evaluation unit also develops an algorithm that allows the generation AI to evaluate a child's behavior on the way to school and send praising messages. For example, if the child gets to school safely every day, it may send a praising message saying, "You're working hard every day!". In this way, evaluating a child's behavior on the way to school and sending praising messages increases motivation.

[0044] The evaluation unit can add a ranking function that allows the generation AI to compete with other children, thereby promoting social connections. For example, the evaluation unit can display the completion rate of the commuting route to school in a ranking format, and reward those who rank highly. To realize the ranking function, the evaluation unit also has the generation AI collect children's commuting data and generate rankings. For example, the evaluation unit can tally daily commuting points and update the rankings. The evaluation unit also develops an algorithm that adds a ranking function that allows the generation AI to compete with other children and promotes social connections. For example, the evaluation unit can add a function that increases points by commuting to school with friends. By adding a ranking function that allows children to compete with other children, social connections can be promoted.

[0045] The evaluation unit allows the generation AI to provide learning content while the child is commuting to school, incorporating educational elements. For example, the evaluation unit may provide learning content while the generation AI is commuting to school, incorporating educational elements. For example, the evaluation unit may pose English vocabulary or arithmetic questions while the child is commuting to school, awarding points for correct answers. In addition, in order to provide learning content, the evaluation unit has the generation AI collect educational data and pose questions at appropriate times while the child is commuting to school. For example, it may pose quizzes while the child is waiting at traffic lights. The evaluation unit may also develop algorithms that allow the generation AI to provide learning content while the child is commuting to school, incorporating educational elements. For example, it may add a function that adjusts the difficulty level according to the child's learning progress. This allows learning content to be provided while the child is commuting to school, incorporating educational elements.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The location monitoring unit allows the generation AI to obtain weather information in real time based on the set information of the school route and suggest alternative routes in the event of bad weather. For example, the generation AI obtains weather information in real time based on the set information of the school route and sends a notification to parents in the event of bad weather. For example, if heavy rain or strong winds are forecast, an alternative route will be suggested. The location monitoring unit also allows the generation AI to evaluate the safety of the school route based on weather information and suggest alternative routes to parents if dangerous conditions are predicted. For example, if snow or ice is forecast, a route that avoids slippery roads will be suggested. The location monitoring unit also allows the generation AI to analyze weather information and evaluate the safety of each point on the school route. For example, it can identify areas at risk of flooding or areas where trees may be fallen due to strong winds and suggest alternative routes. This improves safety by suggesting alternative routes in bad weather.

[0048] The location monitoring unit can share registered school route information with other parents, building safe school routes throughout the community. For example, a platform for sharing registered school route information with other parents can be built, building safe school routes throughout the community. For example, parents can upload school route information and share it with other parents. The location monitoring unit also uses a generative AI to analyze the registered school route information and suggest safe school routes to share with other parents. For example, if multiple parents use the same school route, it will suggest the safest route. The location monitoring unit also develops an app for sharing registered school route information, making it easy for parents to share school route information. For example, dangerous areas and safe spots on the school route can be shared with other parents. In this way, sharing school route information improves safety throughout the community.

[0049] The location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route and guide children to places where they can stop safely. For example, the generation AI provides information on stores and facilities along the school route based on the set information of the school route. For example, it displays information on convenience stores and parks where children can stop safely. The location monitoring unit also allows the generation AI to analyze the set information of the school route and guide children to safe places to stop along the school route. For example, it suggests places where children can stop if they get lost. The location monitoring unit also allows the generation AI to provide information on stores and facilities along the school route in real time based on the set information of the school route. For example, it displays places where children can stop safely on a map. This improves safety by guiding children to safe places to stop along the school route.

[0050] The voice instruction unit can add a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. For example, the generation AI can issue instructions in multiple languages, such as English, Spanish, and Chinese. To achieve multilingual support, the voice instruction unit has the generation AI learn voice data in each language and generate appropriate voice instructions. For example, if a child speaks English, it will issue instructions in English. The voice instruction unit can also add a function that enables the generation AI to issue voice instructions in multiple languages, thereby developing an algorithm that achieves multilingual support. For example, if a child speaks a different language, it will issue instructions in that language. This allows voice instructions to be issued in multiple languages, achieving multilingual support and accommodating a wider range of users.

[0051] The voice instruction unit can add a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, a function is added that allows the generation AI to provide not only voice instructions but also visual instructions. For example, an animation is displayed on the screen to give visual instructions to the child. In addition, the voice instruction unit allows the generation AI to generate animation data and link it to the voice instructions in order to provide visual instructions. For example, if a child gets lost, an animation guiding the way is displayed on the screen. In addition, the voice instruction unit develops an algorithm that allows the generation AI to provide a combination of voice instructions and visual instructions. For example, when a child approaches an intersection, an animation showing how to cross safely is displayed on the screen. This allows for more effective support by providing not only voice instructions but also visual instructions.

[0052] The voice instruction unit allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to get lost in a particular place, the generation AI will issue instructions to warn the child when approaching that place. The voice instruction unit also analyzes past data to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to be late during a certain time of day, the generation AI will instruct the child to leave earlier during that time. The voice instruction unit also develops an algorithm that allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child prefers a particular route, the generation AI will prioritize guidance along that route. This allows for more effective support by learning a child's behavioral patterns and providing individually customized instructions.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The location monitoring unit registers the child's school route in advance and monitors the child's location using the GPS function. For example, a parent can set the school route using a smartphone app and register that information in a batch communication tool. Step 2: The voice instruction unit issues voice instructions based on the location information monitored by the location monitoring unit. For example, if a child strays far from the school route or stops moving from a certain point, the generation AI will issue voice instructions such as "What's wrong?" or "That's not the way!" Step 3: In an emergency, the help function unit activates a siren and a voice message saying "Help me" when the help button is pressed, and provides guidance to the nearest "home that will help children." For example, if a child gets lost, the AI ​​generator will identify the nearest safe place and provide voice guidance to get there. Step 4: The evaluation unit displays a simple map on the screen and gives points when the child safely passes through intersections and crosswalks. For example, if a child takes the correct route to school and crosses the crosswalk safely, a star or point will be displayed on the screen.

[0055] (Example 2) A batch-type communication tool according to an embodiment of the present invention is a system that supports the commute of young children who have just started kindergarten or elementary school. This system allows users to register their school route in advance, monitors the child's location using a GPS function, and generates voice instructions using AI. This allows the batch-type communication tool to support children's safe and enjoyable commute to kindergarten or school.

[0056] A batch-type communication tool according to an embodiment includes a location monitoring unit, a voice instruction unit, a help function unit, and an evaluation unit. The location monitoring unit registers a child's school route in advance and monitors the child's location using a GPS function. For example, a parent may set a school route using a smartphone app and register the route information in the batch-type communication tool. The voice instruction unit issues voice instructions based on the location information monitored by the location monitoring unit. For example, if a child strays far from the school route or stops moving from a certain point, the generation AI issues voice instructions such as "What's wrong?" or "That's not the way!". In an emergency, the help function unit activates a siren and a voice message saying "Help me!" when the help button is pressed, and provides guidance to the nearest "child rescuer." For example, if a child gets lost, the generation AI identifies the nearest safe location and provides voice guidance there. The evaluation unit displays a simple map on the screen and awards evaluation points if the child safely passes through intersections and crosswalks. For example, if a child takes the correct route to school and crosses the crosswalk safely, a star or point will be displayed on the screen. This allows the batch-based communication tool according to the embodiment to support children's safe and enjoyable commute to kindergarten or school. For example, even if a child strays from the school route, the AI ​​can respond immediately and guide the child back to the correct path. It can also respond quickly in emergencies to ensure the safety of children. Furthermore, incorporating game elements allows children to learn safe behaviors on their own initiative.

[0057] When registering a school route, parents take photos of the route, and the AI ​​analyzes the image data to automatically identify dangerous areas and display warnings. For example, parents take photos of the route with their smartphones and upload the image data to a batch communication tool. The AI ​​analyzes the images, evaluates road conditions and traffic volume, and identifies dangerous areas. For example, it detects intersections and crosswalks without traffic lights and displays warnings. When analyzing photos of the route, the AI ​​recognizes the presence or absence of road signs and sidewalks and evaluates the level of danger. For example, it identifies areas without sidewalks or areas with heavy vehicle traffic and displays warnings to parents. The AI ​​also uses image analysis technology to detect obstacles and construction sites in photos of the route and marks them as dangerous areas. For example, it identifies roads under construction or areas with a lot of parked vehicles and displays warnings. This improves safety by identifying dangerous areas on the route in advance and displaying warnings.

[0058] The location monitoring unit allows the generation AI to obtain weather information in real time based on the setting information of the school route and suggest an alternative route in the event of bad weather. For example, the location monitoring unit allows the generation AI to obtain weather information in real time based on the setting information of the school route and send a notification to parents in the event of bad weather. For example, if heavy rain or strong winds are forecast, the generation AI will suggest an alternative route. The location monitoring unit also allows the generation AI to evaluate the safety of the school route based on weather information and suggest an alternative route to parents if a dangerous situation is predicted. For example, if snow or ice is forecast, the generation AI will suggest a route that avoids slippery roads. The location monitoring unit also allows the generation AI to analyze weather information and evaluate the safety of each point on the school route. For example, it will identify areas at risk of flooding or areas where trees may be fallen due to strong winds and suggest an alternative route. This improves safety by suggesting alternative routes in bad weather.

[0059] The location monitoring unit uses the emotion estimation function to measure the parent's anxiety level when setting up the school route, and can suggest additional safety measures if the anxiety is high. For example, when setting up the school route, the location monitoring unit analyzes the parent's facial expressions and voice and uses the emotion estimation function to measure the anxiety level. For example, if the parent looks anxious, the location monitoring unit suggests additional safety measures. The location monitoring unit also uses the emotion estimation function to measure the parent's anxiety level in real time when setting up the school route, and if the anxiety is high, the generation AI suggests additional safety measures. For example, the location monitoring unit provides a detailed description of the school route and emphasizes safety points. The location monitoring unit also uses the emotion estimation function to analyze the parent's heart rate and voice tone when setting up the school route to measure the parent's anxiety level. If the anxiety is high, the generation AI suggests an alternative route or additional safety measures. This provides a sense of security by measuring the parent's anxiety level and suggesting additional safety measures.

[0060] The location monitoring unit can share registered school route information with other parents, building safe school routes throughout the community. For example, the location monitoring unit builds a platform for sharing registered school route information with other parents, building safe school routes throughout the community. For example, parents upload school route information and share it with other parents. The location monitoring unit also uses a generative AI to analyze registered school route information and suggest safe school routes to share with other parents. For example, if multiple parents use the same school route, it will suggest the safest route. The location monitoring unit also develops an app for sharing registered school route information, making it easy for parents to share school route information. For example, dangerous areas and safe spots on the school route can be shared with other parents. In this way, sharing school route information improves safety throughout the community.

[0061] The location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route and guide the user to places where the child can stop safely. For example, the location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route. For example, it displays information on convenience stores and parks where the child can stop safely. The location monitoring unit also allows the generation AI to analyze the set information of the school route and guide the user to safe places to stop along the school route. For example, it suggests places where the child can stop if they get lost. The location monitoring unit also allows the generation AI to provide information on stores and facilities along the school route in real time based on the set information of the school route. For example, it displays places where the child can stop safely on a map. This improves safety by guiding the user to safe places to stop along the school route.

[0062] The location monitoring unit can use the emotion estimation function to measure a child's emotions when setting a school route and suggest a route that the child will enjoy. For example, when setting a school route, the location monitoring unit analyzes the child's facial expressions and voice and uses the emotion estimation function to measure how much fun the child is having. For example, if the child looks like they are having fun, that route is preferentially suggested. The location monitoring unit also uses the emotion estimation function to measure in real time how much fun the child feels when setting a school route and suggests an enjoyable route. For example, a route that passes through places that the child is likely to be interested in is suggested. In addition, to measure the child's enjoyment, the location monitoring unit uses the emotion estimation function to analyze the child's heart rate and voice tone when setting a school route. If the enjoyment is high, that route is preferentially suggested. In this way, suggesting an enjoyable route that takes the child's emotions into consideration improves their motivation to go to school.

[0063] The voice instruction unit allows the generation AI to analyze the tone and speed of a child's voice, determine the level of urgency, and issue appropriate instructions. For example, the generation AI analyzes the tone and speed of a child's voice in real time to determine the level of urgency. For example, if a child's voice is high-pitched and fast, it will determine the level of urgency and immediately issue appropriate instructions. The voice instruction unit also collects voice data to analyze the tone and speed of a child's voice and evaluates the level of urgency. For example, if a child's voice is trembling, it will determine the level of urgency and respond quickly. The voice instruction unit also develops an algorithm that analyzes the tone and speed of a child's voice and determines the level of urgency. For example, if a child's voice suddenly changes, it will determine the level of urgency and issue appropriate instructions. This enables a quick response by analyzing the tone and speed of a child's voice to determine the level of urgency and issuing appropriate instructions.

[0064] The voice instruction unit allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to get lost in a particular place, the generation AI will issue a warning instruction when the child approaches that place. The voice instruction unit also analyzes past data to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to be late during a particular time period, the generation AI will instruct the child to leave earlier during that time period. The voice instruction unit also develops an algorithm that learns a child's behavioral patterns and provides individually customized instructions. For example, if a child prefers a particular route, the generation AI will prioritize guidance along that route. This allows for more effective support by learning a child's behavioral patterns and providing individually customized instructions.

[0065] The voice instruction unit can use the emotion estimation function to analyze a child's emotional state in real time and generate voice instructions that provide a sense of security. The voice instruction unit, for example, uses the emotion estimation function to analyze a child's emotional state in real time and generate voice instructions that provide a sense of security. For example, if a child seems anxious, it might say in a gentle voice, "It's okay." The voice instruction unit also uses the emotion estimation function to analyze the child's facial expressions and voice tone in real time to analyze the child's emotional state. To provide a sense of security, the generation AI generates appropriate voice instructions. The voice instruction unit also uses the emotion estimation function to develop an algorithm that analyzes a child's emotional state and generates voice instructions that provide a sense of security. For example, if a child is crying, it might issue voice instructions to calm them down. In this way, the child's emotional state is analyzed in real time and voice instructions that provide a sense of security are generated, thereby reducing the child's anxiety.

[0066] The voice instruction unit can add a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. The voice instruction unit, for example, adds a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. For example, instructions are issued in multiple languages, such as English, Spanish, and Chinese. To achieve multilingual support, the voice instruction unit has the generation AI learn voice data in each language and generate appropriate voice instructions. For example, if a child speaks English, instructions are issued in English. The voice instruction unit also adds a function that enables the generation AI to issue voice instructions in multiple languages, thereby developing an algorithm that achieves multilingual support. For example, if a child speaks a different language, instructions are issued in that language. This allows voice instructions to be issued in multiple languages, thereby achieving multilingual support and accommodating a wider range of users.

[0067] The voice instruction unit can add a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, the voice instruction unit adds a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, an animation can be displayed on the screen to give visual instructions to the child. In addition, the voice instruction unit allows the generation AI to generate animation data and link it to the voice instructions in order to provide visual instructions. For example, if a child gets lost, an animation showing directions can be displayed on the screen. In addition, the voice instruction unit develops an algorithm that allows the generation AI to provide a combination of voice instructions and visual instructions. For example, when a child approaches an intersection, an animation showing how to cross safely can be displayed on the screen. This allows for more effective support by providing not only voice instructions but also visual instructions.

[0068] The audio instruction unit uses the emotion estimation function to play music and sound effects that correspond to the child's emotions to help them relax. The audio instruction unit, for example, uses the emotion estimation function to play music and sound effects that correspond to the child's emotions to help them relax. For example, if the child seems anxious, calm music is played. The audio instruction unit also uses the emotion estimation function to analyze the child's facial expressions and vocal tone in real time to analyze the child's emotional state. To help the child relax, the generation AI plays appropriate music and sound effects. The audio instruction unit also uses the emotion estimation function to develop an algorithm that plays music and sound effects that correspond to the child's emotions. For example, if the child is excited, calm music is played to help them relax. In this way, music and sound effects that correspond to the child's emotions can be played to help them relax.

[0069] The help function unit allows the generation AI to analyze surrounding environmental sounds in an emergency and propose the optimal response depending on the situation. For example, the help function unit allows the generation AI to analyze surrounding environmental sounds in real time in an emergency and propose the optimal response depending on the situation. For example, it can detect car horns or people screaming and give appropriate instructions. The help function unit also collects audio data to analyze surrounding environmental sounds and evaluates the emergency situation. For example, it can give instructions to evacuate in noisy places. The help function unit also develops an algorithm that allows the generation AI to analyze surrounding environmental sounds in an emergency and propose the optimal response. For example, it can detect the sound of a fire alarm and guide users to the evacuation route. This makes it possible to analyze surrounding environmental sounds in an emergency and propose the optimal response depending on the situation, enabling quick and appropriate response.

[0070] The help function unit enables the generating AI to send real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. The help function unit, for example, builds a system in which the generating AI sends real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. For example, if a child presses the help button, a notification is sent to the parent's smartphone. The help function unit also manages the contact information of parents or guardians to send emergency notifications quickly. For example, if a child gets lost, a notification including location information is sent to the parent. The help function unit also develops an algorithm in which the generating AI sends real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly. For example, if a child is in a dangerous situation, an emergency notification is sent to the parent. This ensures the safety of children by sending real-time notifications to parents or guardians in the event of an emergency, encouraging them to respond quickly.

[0071] The help function unit can use the emotion estimation function to analyze a child's emotional state in an emergency and send a reassuring voice message. For example, the help function unit uses the emotion estimation function to analyze a child's emotional state in an emergency in real time and send a reassuring voice message. For example, if a child is panicking, a message to calm them down is sent. The help function unit also uses the emotion estimation function to analyze a child's facial expressions and voice tone in real time to analyze a child's emotional state in an emergency. A generation AI sends an appropriate voice message to provide a sense of security. The help function unit also uses the emotion estimation function to develop an algorithm that analyzes a child's emotional state in an emergency and sends a reassuring voice message. For example, if a child is crying, the help function unit can say in a gentle voice, "It's okay." This allows the system to analyze a child's emotional state in an emergency and send a reassuring voice message, thereby reducing the child's anxiety.

[0072] The help function unit will enable the generating AI to automatically notify nearby police and fire departments in the event of an emergency. The help function unit will add a function that enables the generating AI to automatically notify nearby police and fire departments in the event of an emergency. For example, if a child presses the help button, the generating AI will automatically notify the police and fire department. To achieve this automatic notification function, the generating AI will manage contact information for the police and fire departments and quickly notify them in the event of an emergency. For example, if a child is in danger, the generating AI will send a report including location information to the police. The help function unit will also develop an algorithm that will add a function that enables the generating AI to automatically notify nearby police and fire departments in the event of an emergency. For example, if a fire or accident occurs, the system will automatically notify the fire department. This will enable a quick response by automatically notifying nearby police and fire departments in the event of an emergency.

[0073] The help function unit enables the generation AI to send voice messages asking for help from nearby adults in an emergency. The help function unit adds a function that enables the generation AI to send voice messages asking for help from nearby adults in an emergency. For example, if a child presses the help button, a voice message yelling "Help me!" is sent. The help function unit also builds a system that enables the generation AI to send voice messages to ask for help from nearby adults. For example, if a child gets lost, a voice message saying "Please help this child" is sent. The help function unit also develops an algorithm that enables the generation AI to send voice messages asking for help from nearby adults in an emergency. For example, if a child is in a dangerous situation, the voice message saying "Help me" is sent repeatedly. This allows for quick assistance to be obtained by sending a voice message asking for help from nearby adults in an emergency.

[0074] The help function unit can use the emotion estimation function to provide a relaxation guide to stabilize a child's emotions in an emergency. The help function unit, for example, uses the emotion estimation function to provide a relaxation guide to stabilize a child's emotions in an emergency. For example, if a child is in a panic state, it provides an audio guide instructing them on how to take deep breaths. The help function unit also uses the emotion estimation function to analyze the child's facial expressions and voice tone in real time to analyze the child's emotional state. To provide the relaxation guide, the generation AI issues appropriate audio instructions. The help function unit also uses the emotion estimation function to develop an algorithm that provides a relaxation guide to stabilize a child's emotions in an emergency. For example, if a child is crying, it provides an audio guide to calm them down. This provides a relaxation guide to stabilize a child's emotions in an emergency, thereby reducing the child's anxiety.

[0075] The evaluation unit allows the generation AI to record the child's progress in completing the school route and set long-term goals, giving them a sense of accomplishment. For example, the evaluation unit allows the generation AI to record the child's progress in completing the school route and set long-term goals. For example, it could count the number of days a child is able to safely commute to school each day and give a reward if that goal is achieved for a certain period of time. In addition, the evaluation unit allows the generation AI to analyze the child's location information and evaluate the progress in completing the school route. For example, it could award points if the child takes the correct route. In addition, the evaluation unit develops an algorithm that allows the generation AI to record the child's progress in completing the school route and set long-term goals. For example, it could provide a special reward if the child commutes safely for one month. In this way, the generation AI records the child's progress in completing the school route and sets long-term goals, giving them a sense of accomplishment.

[0076] The evaluation unit allows the generation AI to evaluate a child's behavior on the way to school and send praising messages to increase motivation. For example, the evaluation unit allows the generation AI to evaluate a child's behavior on the way to school and send praising messages. For example, if the child crosses the crosswalk correctly, it sends a praising message saying, "Well done!". In addition, to evaluate behavior on the way to school, the generation AI analyzes the child's location information and behavioral data and sends praising messages at appropriate times. For example, if the child avoids a dangerous area, it may praise the child by saying, "That's great!". The evaluation unit also develops an algorithm that allows the generation AI to evaluate a child's behavior on the way to school and send praising messages. For example, if the child gets to school safely every day, it may send a praising message saying, "You're working hard every day!". In this way, evaluating a child's behavior on the way to school and sending praising messages increases motivation.

[0077] The evaluation unit can use the emotion estimation function to adjust game elements according to the child's emotional state to maintain enjoyment. The evaluation unit, for example, uses the emotion estimation function to adjust game elements according to the child's emotional state. For example, if the child seems to be having fun, it adds more challenging tasks. The evaluation unit also uses the emotion estimation function to analyze the child's facial expressions and vocal tone in real time to analyze the child's emotional state. The generation AI adjusts game elements to maintain enjoyment. The evaluation unit also uses the emotion estimation function to develop an algorithm that adjusts game elements according to the child's emotional state. For example, if the child is bored, it adds new game elements to pique their interest. In this way, adjusting game elements according to the child's emotional state maintains enjoyment and increases motivation to go to school.

[0078] The evaluation unit can add a ranking function that allows the generation AI to compete with other children, thereby promoting social connections. For example, the evaluation unit can display the completion rate of the commuting route to school in a ranking format, and reward those who rank highly. To realize the ranking function, the evaluation unit also has the generation AI collect children's commuting data and generate rankings. For example, the evaluation unit can tally daily commuting points and update the rankings. The evaluation unit also develops an algorithm that adds a ranking function that allows the generation AI to compete with other children and promotes social connections. For example, the evaluation unit can add a function that increases points by commuting to school with friends. By adding a ranking function that allows children to compete with other children, social connections can be promoted.

[0079] The evaluation unit allows the generation AI to provide learning content while the child is commuting to school, incorporating educational elements. For example, the evaluation unit may provide learning content while the generation AI is commuting to school, incorporating educational elements. For example, the evaluation unit may pose English vocabulary or arithmetic questions while the child is commuting to school, awarding points for correct answers. In addition, in order to provide learning content, the evaluation unit has the generation AI collect educational data and pose questions at appropriate times while the child is commuting to school. For example, it may pose quizzes while the child is waiting at traffic lights. The evaluation unit may also develop algorithms that allow the generation AI to provide learning content while the child is commuting to school, incorporating educational elements. For example, it may add a function that adjusts the difficulty level according to the child's learning progress. This allows learning content to be provided while the child is commuting to school, incorporating educational elements.

[0080] The evaluation unit can use the emotion estimation function to introduce a reward system that corresponds to the child's emotions and reinforce positive behavior. For example, the evaluation unit uses the emotion estimation function to introduce a reward system that corresponds to the child's emotions. For example, if the child seems to be having fun, it provides additional points or a special reward. The evaluation unit also uses the emotion estimation function to analyze the child's facial expressions and vocal tone in real time to analyze the child's emotional state. The generation AI provides appropriate rewards to reinforce positive behavior. The evaluation unit also uses the emotion estimation function to develop an algorithm that introduces a reward system that corresponds to the child's emotions. For example, it provides a special item or title if the child works hard. In this way, positive behavior is reinforced by introducing a reward system that corresponds to the child's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The location monitoring unit allows the generation AI to obtain weather information in real time based on the set information of the school route and suggest alternative routes in the event of bad weather. For example, the generation AI obtains weather information in real time based on the set information of the school route and sends a notification to parents in the event of bad weather. For example, if heavy rain or strong winds are forecast, an alternative route will be suggested. The location monitoring unit also allows the generation AI to evaluate the safety of the school route based on weather information and suggest alternative routes to parents if dangerous conditions are predicted. For example, if snow or ice is forecast, a route that avoids slippery roads will be suggested. The location monitoring unit also allows the generation AI to analyze weather information and evaluate the safety of each point on the school route. For example, it can identify areas at risk of flooding or areas where trees may be fallen due to strong winds and suggest alternative routes. This improves safety by suggesting alternative routes in bad weather.

[0083] The location monitoring unit can share registered school route information with other parents, building safe school routes throughout the community. For example, a platform for sharing registered school route information with other parents can be built, building safe school routes throughout the community. For example, parents can upload school route information and share it with other parents. The location monitoring unit also uses a generative AI to analyze the registered school route information and suggest safe school routes to share with other parents. For example, if multiple parents use the same school route, it will suggest the safest route. The location monitoring unit also develops an app for sharing registered school route information, making it easy for parents to share school route information. For example, dangerous areas and safe spots on the school route can be shared with other parents. In this way, sharing school route information improves safety throughout the community.

[0084] The location monitoring unit allows the generation AI to provide information on stores and facilities along the school route based on the set information of the school route and guide children to places where they can stop safely. For example, the generation AI provides information on stores and facilities along the school route based on the set information of the school route. For example, it displays information on convenience stores and parks where children can stop safely. The location monitoring unit also allows the generation AI to analyze the set information of the school route and guide children to safe places to stop along the school route. For example, it suggests places where children can stop if they get lost. The location monitoring unit also allows the generation AI to provide information on stores and facilities along the school route in real time based on the set information of the school route. For example, it displays places where children can stop safely on a map. This improves safety by guiding children to safe places to stop along the school route.

[0085] The location monitoring unit uses the emotion estimation function to measure the parent's anxiety level when setting up the school route, and if the anxiety is high, it can suggest additional safety measures. For example, when setting up the school route, it analyzes the parent's facial expressions and voice and uses the emotion estimation function to measure the anxiety level. For example, if the parent looks anxious, it suggests additional safety measures. The location monitoring unit also uses the emotion estimation function to measure the parent's anxiety level in real time when setting up the school route, and if the anxiety is high, the generation AI suggests additional safety measures. For example, it provides a detailed description of the school route or emphasizes safety points. The location monitoring unit also uses the emotion estimation function to analyze the parent's heart rate and voice tone when setting up the school route to measure the parent's anxiety level. If the anxiety is high, the generation AI suggests an alternative route or additional safety measures. This provides a sense of security by measuring the parent's anxiety level and suggesting additional safety measures.

[0086] The location monitoring unit can use the emotion estimation function to measure a child's emotions when setting up a school route and suggest a route that the child will enjoy. For example, when setting up a school route, the unit analyzes the child's facial expressions and voice and uses the emotion estimation function to measure how much fun the child is having. For example, if the child looks like they are having fun, that route is preferentially suggested. The location monitoring unit also uses the emotion estimation function to measure how much fun the child feels when setting up a school route in real time and suggests an enjoyable route. For example, a route that passes through places that the child is likely to be interested in is suggested. The location monitoring unit also uses the emotion estimation function to analyze the child's heart rate and voice tone when setting up a school route to measure the child's enjoyment. If the enjoyment is high, that route is preferentially suggested. In this way, suggesting an enjoyable route that takes the child's emotions into consideration improves their motivation to go to school.

[0087] The voice instruction unit can add a function that enables the generation AI to issue voice instructions in multiple languages, thereby achieving multilingual support. For example, the generation AI can issue instructions in multiple languages, such as English, Spanish, and Chinese. To achieve multilingual support, the voice instruction unit has the generation AI learn voice data in each language and generate appropriate voice instructions. For example, if a child speaks English, it will issue instructions in English. The voice instruction unit can also add a function that enables the generation AI to issue voice instructions in multiple languages, thereby developing an algorithm that achieves multilingual support. For example, if a child speaks a different language, it will issue instructions in that language. This allows voice instructions to be issued in multiple languages, achieving multilingual support and accommodating a wider range of users.

[0088] The voice instruction unit can add a function that allows the generation AI to provide not only voice instructions but also visual instructions. For example, a function is added that allows the generation AI to provide not only voice instructions but also visual instructions. For example, an animation is displayed on the screen to give visual instructions to the child. In addition, the voice instruction unit allows the generation AI to generate animation data and link it to the voice instructions in order to provide visual instructions. For example, if a child gets lost, an animation guiding the way is displayed on the screen. In addition, the voice instruction unit develops an algorithm that allows the generation AI to provide a combination of voice instructions and visual instructions. For example, when a child approaches an intersection, an animation showing how to cross safely is displayed on the screen. This allows for more effective support by providing not only voice instructions but also visual instructions.

[0089] The voice instruction unit can use the emotion estimation function to analyze a child's emotional state in real time and generate voice instructions that provide a sense of security. For example, the emotion estimation function can be used to analyze a child's emotional state in real time and generate voice instructions that provide a sense of security. For example, if a child seems anxious, the voice instruction unit can say in a gentle voice, "It's okay." To analyze a child's emotional state, the voice instruction unit can also use the emotion estimation function to analyze the child's facial expressions and voice tone in real time. To provide a sense of security, the generation AI generates appropriate voice instructions. The voice instruction unit can also use the emotion estimation function to develop an algorithm that analyzes a child's emotional state and generates voice instructions that provide a sense of security. For example, if a child is crying, voice instructions to calm them down can be issued. In this way, the child's emotional state can be analyzed in real time and voice instructions that provide a sense of security can be generated, thereby reducing the child's anxiety.

[0090] The voice instruction unit allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to get lost in a particular place, the generation AI will issue instructions to warn the child when approaching that place. The voice instruction unit also analyzes past data to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child tends to be late during a certain time of day, the generation AI will instruct the child to leave earlier during that time. The voice instruction unit also develops an algorithm that allows the generation AI to learn a child's behavioral patterns and provide individually customized instructions. For example, if a child prefers a particular route, the generation AI will prioritize guidance along that route. This allows for more effective support by learning a child's behavioral patterns and providing individually customized instructions.

[0091] The audio instruction unit uses the emotion estimation function to play music and sound effects that correspond to the child's emotions to help them relax. For example, the emotion estimation function can be used to play music and sound effects that correspond to the child's emotions to help them relax. For example, if the child seems anxious, calm music can be played. The audio instruction unit also uses the emotion estimation function to analyze the child's facial expressions and vocal tone in real time to analyze the child's emotional state. To help the child relax, the generation AI plays appropriate music and sound effects. The audio instruction unit also uses the emotion estimation function to develop an algorithm that plays music and sound effects that correspond to the child's emotions. For example, if the child is excited, calm music can be played to help them relax. In this way, music and sound effects that correspond to the child's emotions can be played to help them relax.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The location monitoring unit registers the child's school route in advance and monitors the child's location using the GPS function. For example, a parent can set the school route using a smartphone app and register that information in a batch communication tool. Step 2: The voice instruction unit issues voice instructions based on the location information monitored by the location monitoring unit. For example, if a child strays far from the school route or stops moving from a certain point, the generation AI will issue voice instructions such as "What's wrong?" or "That's not the way!" Step 3: In an emergency, the help function unit activates a siren and a voice message saying "Help me" when the help button is pressed, and provides guidance to the nearest "home that will help children." For example, if a child gets lost, the AI ​​generator will identify the nearest safe place and provide voice guidance to get there. Step 4: The evaluation unit displays a simple map on the screen and gives points when the child safely passes through intersections and crosswalks. For example, if a child takes the correct route to school and crosses the crosswalk safely, a star or point will be displayed on the screen.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A location monitoring unit that registers the child's route to school in advance and monitors the child's location using the GPS function. a voice instruction unit that issues voice instructions based on the location information monitored by the location monitoring unit; In an emergency, pressing the help button will activate a siren and sound, and guide you to the nearest "Child Help Home." An evaluation unit that displays a simple map on the screen and gives evaluation points if the vehicle can safely pass through an intersection or a crosswalk. A system characterized by:

2. The position monitoring unit When registering a school route, parents take a photo of the route, and the image data is generated and analyzed by AI, which automatically identifies dangerous areas and displays warnings.

2. The system of claim 1.

3. The position monitoring unit Based on the set information of the school route, the AI ​​generator obtains weather information in real time and suggests alternative routes in the event of bad weather.

2. The system of claim 1.

4. The position monitoring unit When setting up the school route, measure parents' anxiety levels and suggest additional safety measures if anxiety levels are high.

2. The system of claim 1.

5. The position monitoring unit Share the registered route information with other parents to build safe routes to school throughout the community 2. The system of claim 1.

6. The position monitoring unit Based on the set information of the school route, the generation AI provides information on stores and facilities on the school route and guides the child to safe places to stop.

2. The system of claim 1.

7. The position monitoring unit Measuring the child's emotions when setting the school route, and suggesting the route that the child can enjoy 2. The system of claim 1.

8. The voice instruction unit The generating AI analyzes the tone and speed of the child's voice, determines the level of urgency, and issues appropriate instructions.

2. The system of claim 1.

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