System

Abstract

An object of the system according to the embodiment is to convey a task to a child who is not good at understanding words in a visually easy-to-understand manner.SOLUTION: A system includes an task management unit, a visualization unit, and a completion notifying unit. The task management unit manages a task list. The visualization unit visually displays the tasks managed by the task management unit. The visualization unit generates an image according to the content of the task using the generation AI. The completion notification unit visually expresses the result of the task when the child completes the task.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to explain tasks in an easy-to-understand manner to children who have difficulty understanding words.

[0005] The system according to the embodiment aims to convey tasks visually in an easy-to-understand manner to children who have difficulty understanding words. [Means for solving the problem]

[0006] The system according to the embodiment includes a task management unit, a visualization unit, and a completion notification unit. The task management unit manages a list of tasks. The visualization unit visually displays tasks managed by the task management unit. The visualization unit uses a generation AI to generate an image according to the content of the task. The completion notification unit visually displays the result when a child completes a task. [Effects of the Invention]

[0007] The system according to the embodiment can visually convey tasks to children who have difficulty understanding words in an easy-to-understand manner. [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 task management system according to an embodiment of the present invention is a system that visually communicates the tasks that need to be done to children who have difficulty understanding words. This system uses image generation AI to visually display tasks based on a list of tasks, and visually displays the results when a child completes a task. This allows the task management system to visually communicate the tasks that need to be done to children who have difficulty understanding words.

[0029] A task management system according to an embodiment includes a task management unit, a visualization unit, and a completion notification unit. The task management unit manages a list of tasks. For example, it can manage household chores, study tasks, daily routine tasks, and the like. The visualization unit visually displays tasks managed by the task management unit. For example, it uses a generation AI to generate an image according to the content of the task and display it as 2D graphics, 3D models, animations, or the like. The completion notification unit visually represents the outcome when a child completes a task. For example, it uses a generation AI to generate an image or animation according to the task completion status and display it as a progress bar, achievement medal, animation, or the like. This allows the task management system to visually and clearly communicate the current task to children who have difficulty understanding words.

[0030] The visualization unit uses the generation AI to generate animations according to the content of the task, providing dynamic visual effects as the child progresses through the task. For example, for the task "brush your teeth," the visualization unit generates an animation of a toothbrush moving to brush the teeth, and displays this animation as the child progresses through the task. For the task "do your homework," the visualization unit generates an animation of a pencil moving to write in a notebook, and displays this animation as the child progresses through the task. For the task "tidy up your room," the visualization unit generates an animation of a toy going into a box, and displays this animation as the child progresses through the task. This provides dynamic visual effects as the task progresses, attracting the child's interest and encouraging them to complete the task.

[0031] The visualization unit allows the generation AI to generate related stories and characters based on the content of the task, allowing children to enjoy the task as part of a story. For example, for the task of "brushing teeth," the visualization unit generates a story that teaches the importance of brushing teeth and displays a scene in which a character from that story brushes their teeth. For the task of "doing homework," the visualization unit generates a story that conveys the joy of learning and displays a scene in which a character from that story does their homework. For the task of "tidying up your room," the visualization unit generates a story that teaches the importance of tidying up and displays a scene in which a character from that story tidies up their room. This increases children's motivation by allowing them to enjoy the task as part of a story.

[0032] The visualization unit generates the list of tasks as a 3D model, and the 3D model can evolve each time the child completes a task. For example, the visualization unit evolves the 3D model character to have healthy teeth each time the child completes the task "brush their teeth." The visualization unit also evolves the 3D model character to hold a book as a symbol of knowledge each time the child completes the task "do their homework." The visualization unit also displays how the 3D model's room becomes more tidy each time the child completes the task "tidy their room." This increases the child's sense of accomplishment as the 3D model evolves each time the child completes a task.

[0033] The visualization unit can visualize tasks based on different themes, allowing children to select a theme that interests them. For example, the visualization unit visualizes the task of "brushing teeth" with a space exploration theme, displaying a scene of an astronaut brushing his teeth. The visualization unit also visualizes the task of "doing homework" with an underwater exploration theme, displaying a scene of an underwater explorer writing in a notebook. The visualization unit also visualizes the task of "tidying up your room" with a jungle exploration theme, displaying a scene of an explorer organizing his tools. Thus, visualizing tasks based on different themes attracts children's interest and promotes task completion.

[0034] The completion notification unit allows the generation AI to recognize the child's voice when the task is completed, and generate and display a specific animation in response to that voice. For example, when a child reports verbally that they "brushed their teeth," the generation AI generates and displays an animation of a toothbrush moving to brush their teeth. In addition, when a child reports verbally that they "finished their homework," the generation AI generates and displays an animation of a pencil moving to write in a notebook. In addition, when a child reports verbally that they "tidyed their room," the generation AI generates and displays an animation of a toy being put into a box. In this way, generating animations in response to the child's voice enhances the sense of accomplishment when a task is completed.

[0035] The completion notification unit allows the generation AI to recognize the child's actions when the task is completed and generate a visual effect according to that action. For example, when the child claps, the generation AI generates and displays a visual effect of stars scattering. When the child jumps, the generation AI generates and displays a visual effect of fireworks exploding. When the child pumps their fist, the generation AI generates and displays a visual effect of a rainbow. In this way, generating visual effects according to the child's actions enhances the sense of accomplishment when the task is completed.

[0036] The completion notification unit allows the generation AI to display a child's favorite character when the task is completed, and to generate an animation in which the character celebrates the completion of the task. For example, when a child reports that they have "brushed their teeth," the generation AI generates and displays an animation in which the child's favorite anime character brushes their teeth. Furthermore, when a child reports that they have "finished their homework," the generation AI generates and displays an animation in which the child's favorite game character does their homework. Furthermore, when a child reports that they have "tidied up their room," the generation AI generates and displays an animation in which the child's favorite movie character tidies up their room. In this way, displaying a child's favorite character enhances the sense of accomplishment when the task is completed.

[0037] The completion notification unit allows the generation AI to generate badges or medals according to the child's progress when a task is completed, and can add a collection element. For example, when a child reports that they "brushed their teeth," the generation AI generates a toothbrushing badge that the child can collect. Also, when a child reports that they "finished their homework," the generation AI generates a learning medal that the child can collect. Also, when a child reports that they "tidyed their room," the generation AI generates an organization badge that the child can collect. In this way, generating badges and medals increases the child's sense of accomplishment and adds a collection element.

[0038] The completion notification unit allows the generation AI to generate a storybook based on the child's progress when a task is completed, and a new page can be added each time a task is completed. For example, when a child reports that they have "brushed their teeth," the generation AI generates a new page in the toothbrushing storybook so that the child can continue reading. Also, when a child reports that they have "finished their homework," the generation AI generates a new page in the learning storybook so that the child can continue reading. Also, when a child reports that they have "tidied up their room," the generation AI generates a new page in the organization storybook so that the child can continue reading. In this way, generating a storybook and adding a new page each time a task is completed increases the child's sense of accomplishment.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The task management system can further include an audio guide unit. The audio guide unit provides audio guidance according to the progress of the task. For example, for the task "brush your teeth," the audio guide unit may instruct, "Next, try brushing your molars." For the task "do your homework," the audio guide unit may instruct, "Let's move on to the next problem." For the task "tidy up your room," the audio guide unit may instruct, "Next, put your toys away." In this way, the audio guide can support the progress of the task and help the child understand.

[0041] The task management system may further include a reward system unit. The reward system unit provides a reward to a child each time the child completes a task. For example, the reward system unit provides a digital sticker each time the child completes a task such as "brushing their teeth." The reward system unit also provides points each time the child completes a task such as "doing their homework." The reward system unit also provides a badge each time the child completes a task such as "tidying their room." This makes it possible to increase the child's motivation through rewards and encourage them to complete tasks.

[0042] The task management system may further include a customization unit. The customization unit customizes the visualization of tasks and rewards according to the child's preferences. For example, for the task "brush your teeth," the customization unit displays the child's favorite character. For the task "do your homework," the customization unit uses the child's favorite color. For the task "tidy up your room," the customization unit plays the child's favorite music. In this way, customization according to the child's preferences can make completing tasks more enjoyable.

[0043] The task management system may further include a progress sharing unit. The progress sharing unit shares the child's task progress with family and friends. For example, when the task "brush teeth" is completed, the progress sharing unit sends a notification to family members. Also, when the task "do homework" is completed, the progress sharing unit sends a notification to friends. Also, when the task "tidy up the room" is completed, the progress sharing unit sends a notification to family members. In this way, sharing task progress can increase the child's sense of accomplishment.

[0044] The task management system may further include a progress display unit that displays the progress of a task in real time. The progress display unit visually displays the progress of a task. For example, for the task "brush teeth," the progress display unit displays the progress of a toothbrush icon. For the task "do homework," the progress display unit displays the progress of a pencil icon. For the task "tidy up your room," the progress display unit displays the progress of a toy icon. In this way, displaying the progress of tasks in real time can enhance a child's sense of accomplishment.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The task management section manages a list of tasks, such as household chores, study tasks, and daily routine tasks. Step 2: The visualization unit visually displays the tasks managed by the task management unit. For example, it uses generation AI to generate images based on the content of the tasks and displays them as 2D graphics, 3D models, animations, etc. Step 3: The completion notification component visually represents the child's achievement as they complete the task. For example, it uses generative AI to generate images and animations based on the task completion status, and displays them as progress bars, achievement medals, animations, etc.

[0047] (Example 2) A task management system according to an embodiment of the present invention is a system that visually communicates the tasks that need to be done to children who have difficulty understanding words. This system uses image generation AI to visually display tasks based on a list of tasks, and visually displays the results when a child completes a task. This allows the task management system to visually communicate the tasks that need to be done to children who have difficulty understanding words.

[0048] A task management system according to an embodiment includes a task management unit, a visualization unit, and a completion notification unit. The task management unit manages a list of tasks. For example, it can manage household chores, study tasks, daily routine tasks, and the like. The visualization unit visually displays tasks managed by the task management unit. For example, it uses a generation AI to generate an image according to the content of the task and display it as 2D graphics, 3D models, animations, or the like. The completion notification unit visually represents the outcome when a child completes a task. For example, it uses a generation AI to generate an image or animation according to the task completion status and display it as a progress bar, achievement medal, animation, or the like. This allows the task management system to visually and clearly communicate the current task to children who have difficulty understanding words.

[0049] The visualization unit uses the generation AI to generate animations according to the content of the task, providing dynamic visual effects as the child progresses through the task. For example, for the task "brush your teeth," the visualization unit generates an animation of a toothbrush moving to brush the teeth, and displays this animation as the child progresses through the task. For the task "do your homework," the visualization unit generates an animation of a pencil moving to write in a notebook, and displays this animation as the child progresses through the task. For the task "tidy up your room," the visualization unit generates an animation of a toy going into a box, and displays this animation as the child progresses through the task. This provides dynamic visual effects as the task progresses, attracting the child's interest and encouraging them to complete the task.

[0050] The visualization unit allows the generation AI to generate related stories and characters based on the content of the task, allowing children to enjoy the task as part of a story. For example, for the task of "brushing teeth," the visualization unit generates a story that teaches the importance of brushing teeth and displays a scene in which a character from that story brushes their teeth. For the task of "doing homework," the visualization unit generates a story that conveys the joy of learning and displays a scene in which a character from that story does their homework. For the task of "tidying up your room," the visualization unit generates a story that teaches the importance of tidying up and displays a scene in which a character from that story tidies up their room. This increases children's motivation by allowing them to enjoy the task as part of a story.

[0051] The visualization unit uses the emotion estimation function to visualize tasks according to the child's current emotional state and can generate images that elicit positive emotions. For example, if the child is tired, the visualization unit's generation AI generates images of relaxing scenery or characters and displays them as the task progresses. If the child is excited, the visualization unit's generation AI generates images in calming colors and displays them as the task progresses. If the child is feeling anxious, the visualization unit's generation AI generates images of characters or scenes that give a sense of security and displays them as the task progresses. In this way, visualization according to the child's emotional state elicits positive emotions and promotes task completion.

[0052] The visualization unit generates the list of tasks as a 3D model, and the 3D model can evolve each time the child completes a task. For example, the visualization unit evolves the 3D model character to have healthy teeth each time the child completes the task "brush their teeth." The visualization unit also evolves the 3D model character to hold a book as a symbol of knowledge each time the child completes the task "do their homework." The visualization unit also displays how the 3D model's room becomes more tidy each time the child completes the task "tidy their room." This increases the child's sense of accomplishment as the 3D model evolves each time the child completes a task.

[0053] The visualization unit can visualize tasks based on different themes, allowing children to select a theme that interests them. For example, the visualization unit visualizes the task of "brushing teeth" with a space exploration theme, displaying a scene of an astronaut brushing his teeth. The visualization unit also visualizes the task of "doing homework" with an underwater exploration theme, displaying a scene of an underwater explorer writing in a notebook. The visualization unit also visualizes the task of "tidying up your room" with a jungle exploration theme, displaying a scene of an explorer organizing his tools. Thus, visualizing tasks based on different themes attracts children's interest and promotes task completion.

[0054] The visualization unit uses the emotion estimation function to record the emotions a child feels when completing a task and can optimize the next task visualization based on that data. For example, the visualization unit records the emotions a child feels when completing the task of "brushing their teeth" and reflects them in the next task visualization. If there are a lot of positive emotions, the visualization unit performs a similar visualization. The visualization unit also records the emotions a child feels when completing the task of "doing homework" and reflects them in the next task visualization. If there are a lot of negative emotions, the visualization is changed. The visualization unit also records the emotions a child feels when completing the task of "tidying up their room" and reflects them in the next task visualization. The optimal visualization is provided based on the emotion data. This makes it easier to elicit positive emotions by optimizing the next task visualization based on the child's emotion data.

[0055] The completion notification unit allows the generation AI to recognize the child's voice when the task is completed, and generate and display a specific animation in response to that voice. For example, when a child reports verbally that they "brushed their teeth," the generation AI generates and displays an animation of a toothbrush moving to brush their teeth. In addition, when a child reports verbally that they "finished their homework," the generation AI generates and displays an animation of a pencil moving to write in a notebook. In addition, when a child reports verbally that they "tidyed their room," the generation AI generates and displays an animation of a toy being put into a box. In this way, generating animations in response to the child's voice enhances the sense of accomplishment when a task is completed.

[0056] The completion notification unit allows the generation AI to recognize the child's actions when the task is completed and generate a visual effect according to that action. For example, when the child claps, the generation AI generates and displays a visual effect of stars scattering. When the child jumps, the generation AI generates and displays a visual effect of fireworks exploding. When the child pumps their fist, the generation AI generates and displays a visual effect of a rainbow. In this way, generating visual effects according to the child's actions enhances the sense of accomplishment when the task is completed.

[0057] The completion notification unit can use the emotion estimation function to analyze the emotion a child expresses when completing a task and generate visual effects to reinforce positive emotions. For example, the completion notification unit analyzes the emotion a child expresses when they report that they "brushed their teeth," and if the positive emotion is strong, the generation AI displays a smiling character. The completion notification unit also analyzes the emotion a child expresses when they report that they "finished their homework," and if the positive emotion is strong, the generation AI displays a character clapping. The completion notification unit also analyzes the emotion a child expresses when they report that they "tidyed their room," and if the positive emotion is strong, the generation AI displays a character giving a high five. In this way, analyzing the child's emotion and generating visual effects that reinforce positive emotions increases the child's sense of accomplishment when completing a task.

[0058] The completion notification unit allows the generation AI to display a child's favorite character when the task is completed, and to generate an animation in which the character celebrates the completion of the task. For example, when a child reports that they have "brushed their teeth," the generation AI generates and displays an animation in which the child's favorite anime character brushes their teeth. Furthermore, when a child reports that they have "finished their homework," the generation AI generates and displays an animation in which the child's favorite game character does their homework. Furthermore, when a child reports that they have "tidied up their room," the generation AI generates and displays an animation in which the child's favorite movie character tidies up their room. In this way, displaying a child's favorite character enhances the sense of accomplishment when the task is completed.

[0059] The completion notification unit uses the emotion estimation function to record the emotion a child feels when they complete a task, and can optimize the visual effect for the next task completion based on that data. For example, the completion notification unit records the emotion a child feels when they report that they "brushed their teeth," and generates a visual effect that elicits positive emotion when the next task is completed. The completion notification unit also records the emotion a child feels when they report that they "finished their homework," and generates a visual effect that elicits positive emotion when the next task is completed. The completion notification unit also records the emotion a child feels when they report that they "tidyed their room," and generates a visual effect that elicits positive emotion when the next task is completed. In this way, optimizing the visual effect for the next task completion based on the child's emotion data makes it easier to elicit positive emotion.

[0060] The completion notification unit allows the generation AI to generate badges or medals according to the child's progress when a task is completed, and can add a collection element. For example, when a child reports that they "brushed their teeth," the generation AI generates a toothbrushing badge that the child can collect. Also, when a child reports that they "finished their homework," the generation AI generates a learning medal that the child can collect. Also, when a child reports that they "tidyed their room," the generation AI generates an organization badge that the child can collect. In this way, generating badges and medals increases the child's sense of accomplishment and adds a collection element.

[0061] The completion notification unit allows the generation AI to generate a storybook based on the child's progress when a task is completed, and a new page can be added each time a task is completed. For example, when a child reports that they have "brushed their teeth," the generation AI generates a new page in the toothbrushing storybook so that the child can continue reading. Also, when a child reports that they have "finished their homework," the generation AI generates a new page in the learning storybook so that the child can continue reading. Also, when a child reports that they have "tidied up their room," the generation AI generates a new page in the organization storybook so that the child can continue reading. In this way, generating a storybook and adding a new page each time a task is completed increases the child's sense of accomplishment.

[0062] The completion notification unit uses the emotion estimation function to record the emotion a child feels when they complete a task, and can optimize the next visual outcome based on that data. For example, the completion notification unit records the emotion a child feels when they report that they "brushed their teeth," and generates a visual outcome that will elicit positive emotion when the next task is completed. The completion notification unit also records the emotion a child feels when they report that they "finished their homework," and generates a visual outcome that will elicit positive emotion when the next task is completed. The completion notification unit also records the emotion a child feels when they report that they "tidyed their room," and generates a visual outcome that will elicit positive emotion when the next task is completed. In this way, optimizing the next visual outcome based on the child's emotion data makes it easier to elicit positive emotion.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The task management system can further include an audio guide unit. The audio guide unit provides audio guidance according to the progress of the task. For example, for the task "brush your teeth," the audio guide unit may instruct, "Next, try brushing your molars." For the task "do your homework," the audio guide unit may instruct, "Let's move on to the next problem." For the task "tidy up your room," the audio guide unit may instruct, "Next, put your toys away." In this way, the audio guide can support the progress of the task and help the child understand.

[0065] The task management system may further include a reward system unit. The reward system unit provides a reward to a child each time the child completes a task. For example, the reward system unit provides a digital sticker each time the child completes a task such as "brushing their teeth." The reward system unit also provides points each time the child completes a task such as "doing their homework." The reward system unit also provides a badge each time the child completes a task such as "tidying their room." This makes it possible to increase the child's motivation through rewards and encourage them to complete tasks.

[0066] The task management system may further include a customization unit. The customization unit customizes the visualization of tasks and rewards according to the child's preferences. For example, for the task "brush your teeth," the customization unit displays the child's favorite character. For the task "do your homework," the customization unit uses the child's favorite color. For the task "tidy up your room," the customization unit plays the child's favorite music. In this way, customization according to the child's preferences can make completing tasks more enjoyable.

[0067] The task management system may further include a progress sharing unit. The progress sharing unit shares the child's task progress with family and friends. For example, when the task "brush teeth" is completed, the progress sharing unit sends a notification to family members. Also, when the task "do homework" is completed, the progress sharing unit sends a notification to friends. Also, when the task "tidy up the room" is completed, the progress sharing unit sends a notification to family members. In this way, sharing task progress can increase the child's sense of accomplishment.

[0068] The task management system can further use the emotion estimation function to adjust the difficulty of the task based on the child's emotions. For example, if the child is tired, the difficulty of the task can be lowered. If the child is excited, the difficulty of the task can be increased. If the child is feeling anxious, the difficulty of the task can be adjusted. In this way, adjusting the difficulty of the task according to the child's emotions can make the task completion smoother.

[0069] The task management system can further use the emotion estimation function to optimize the order of tasks based on the child's emotions. For example, if the child is tired, it can start with an easy task. If the child is excited, it can start with a task that requires concentration. If the child is anxious, it can start with a task that provides comfort. In this way, optimizing the order of tasks according to the child's emotions can make task completion more effective.

[0070] The task management system can further use the emotion estimation function to customize task feedback based on the child's emotions. For example, if the child has positive emotions, it can provide praising feedback. If the child has negative emotions, it can provide encouraging feedback. If the child has neutral emotions, it can provide feedback instructing the next step. This allows for more effective support in task completion by providing feedback according to the child's emotions.

[0071] The task management system can further use the emotion estimation function to customize task rewards based on the child's emotions. For example, if a child has positive emotions, it can provide a special reward. If a child has negative emotions, it can provide an encouraging reward. If a child has neutral emotions, it can provide a regular reward. This makes it possible to more effectively promote task completion by providing rewards according to the child's emotions.

[0072] The task management system can further use the emotion estimation function to adjust the progress of the task based on the child's emotions. For example, if the child is tired, the task progress can be slowed down. If the child is excited, the task progress can be speeded up. If the child is feeling anxious, the task progress can be adjusted. In this way, adjusting the task progress according to the child's emotions can make the task completion smoother.

[0073] The task management system may further include a progress display unit that displays the progress of a task in real time. The progress display unit visually displays the progress of a task. For example, for the task "brush teeth," the progress display unit displays the progress of a toothbrush icon. For the task "do homework," the progress display unit displays the progress of a pencil icon. For the task "tidy up your room," the progress display unit displays the progress of a toy icon. In this way, displaying the progress of tasks in real time can enhance a child's sense of accomplishment.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The task management section manages a list of tasks, such as household chores, study tasks, and daily routine tasks. Step 2: The visualization unit visually displays the tasks managed by the task management unit. For example, it uses generation AI to generate images based on the content of the tasks and displays them as 2D graphics, 3D models, animations, etc. Step 3: The completion notification component visually represents the child's achievement as they complete the task. For example, it uses generative AI to generate images and animations based on the task completion status, and displays them as progress bars, achievement medals, animations, etc.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 task management section that manages a list of tasks; a visualization unit that visually displays the tasks managed by the task management unit; The visualization unit generates an image according to the content of the task using a generation AI, A completion notification section that visually displays the child's achievement when the child completes a task. A system characterized by:

2. The visualization unit Using the generation AI, generate an animation according to the content of the task; Provide dynamic visual effects as the child progresses through the task 2. The system of claim 1.

3. The visualization unit The generation AI generates related stories and characters based on the content of the task, Allow the child to enjoy the task as part of a story 2. The system of claim 1.

4. The visualization unit providing a visualization of said task according to the child's current emotional state; Generate images that elicit positive emotions 2. The system of claim 1.

5. The visualization unit generating the task list as a 3D model; The 3D model evolves as the child completes the task.

2. The system of claim 1.

6. The visualization unit Visualizing the task based on different themes; Allow your child to choose a topic that interests them 2. The system of claim 1.

7. The visualization unit recording the child's emotions as they complete the task; Use that data to optimize your next task visualization 2. The system of claim 1.

8. The completion notification section is When the task is completed, the AI ​​recognizes the child's voice and generates and displays a specific animation based on that voice.

2. The system of claim 1.

Images