System

Abstract

An object of the system according to the embodiment is to appropriately manage a time period during which a child plays alone and to allow the child to continue playing without getting bored.SOLUTION: A system includes a play proposal unit, a time management unit, and a variation provision unit. The play suggestion unit suggests an appropriate play based on the age and interest of the child. The time management part manages time so that the child plays only for the time designated by the parent. The variation providing unit provides a play variation so that the child does not get bored.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] With conventional technology, it was difficult to properly manage the time children spent playing alone and to keep them playing without getting bored.

[0005] The system according to the embodiment aims to appropriately manage the time that children spend playing alone so that they can continue playing without getting bored. [Means for solving the problem]

[0006] The system according to the embodiment includes a play suggestion unit, a time management unit, and a variation provision unit. The play suggestion unit suggests appropriate play based on the child's age and interests. The time management unit manages time so that the child plays for only the time specified by the parent. The variation provision unit provides a variety of play options to keep the child from getting bored. [Effects of the Invention]

[0007] The system according to the embodiment appropriately manages the time that a child spends playing alone, allowing the child to continue playing without getting bored. [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 support system according to an embodiment of the present invention is a system that supports dual-income households or households with small children in balancing childcare and housework. In this system, a generation AI provides a method for parents to let their children play alone for a specific period of time. This enables the support system to provide a method for parents to let their children play alone for a specific period of time.

[0029] The support system according to the embodiment includes a play suggestion unit, a time management unit, and a variation provision unit. The play suggestion unit suggests appropriate play based on the child's age and interests. For example, the play suggestion unit suggests simple puzzles or block games for a three-year-old child. The play suggestion unit also suggests picture book reading or simple crafts for a five-year-old child. The play suggestion unit also suggests animal- or vehicle-related play based on the child's interests. The time management unit manages time so that the child plays for only the time specified by the parent. For example, when a parent instructs the child to "play only while I go to the bathroom," the time management unit measures the time and manages the child to play only during that time. The time management unit also notifies the child when time has passed, saying, "It's almost over." The time management unit also provides a method for setting a timer and criteria for extending or shortening the time. The variation provision unit provides a variety of play options to prevent the child from getting bored. For example, if a child gets bored with one play, the variation provision unit suggests a new play. The variation providing unit first suggests playing with blocks, and then suggests reading a picture book to the child if the child gets bored. The variation providing unit also provides variations such as indoor games, outdoor games, digital games, etc. As a result, the support system according to the embodiment suggests appropriate games based on the child's age and interests, manages time, and provides variations in games, allowing the child to continue playing without getting bored.

[0030] The play suggestion unit learns a child's past play history and can prioritize suggesting games that the child particularly enjoyed. For example, the generation AI stores the child's past play history in a database and analyzes the games that the child particularly enjoyed. For example, it prioritizes suggesting games that the child played for a long time or that the child frequently choses. The play suggestion unit also extracts patterns of games that the child particularly enjoyed based on the child's play history. For example, it prioritizes suggesting games that include specific themes or characters. The play suggestion unit also learns a child's play history and suggests games that the child particularly enjoyed in real time. For example, it suggests new games based on games that the child enjoyed in the past. In this way, by learning a child's past play history and prioritizing suggesting games that the child particularly enjoyed, the child can enjoy themselves more.

[0031] The play suggestion unit can monitor a child's physical activity level and suggest games that encourage moderate exercise. For example, the generation AI in the play suggestion unit monitors a child's physical activity level and suggests games that encourage moderate exercise. For example, if a child has been sitting for a long time, it suggests games that get the child moving. The play suggestion unit also analyzes a child's physical activity data and suggests games to help resolve a lack of exercise. For example, if a child has not achieved their daily exercise amount, it suggests games that encourage exercise. The play suggestion unit also monitors a child's physical activity level in real time and suggests games that encourage moderate exercise. For example, if a child is tired, it suggests light exercise, and if they are energetic, it suggests active exercise. In this way, by monitoring a child's physical activity level and suggesting games that encourage moderate exercise, children's health can be maintained.

[0032] The game suggestion unit can work with a child's friends and siblings to suggest games that can be played together. For example, the generation AI of the game suggestion unit collects information about a child's friends and siblings to suggest games that can be played together. For example, it suggests games and activities that can be enjoyed by multiple people. The game suggestion unit also works with a child's friends and siblings to suggest games that can be played together online. For example, it suggests games that can be played together via video calls. The game suggestion unit also takes into account the interests of a child's friends and siblings to suggest games that can be enjoyed together. For example, it suggests games based on common hobbies and interests. In this way, the game suggestion unit can work with a child's friends and siblings to suggest games that can be played together, allowing the child to enjoy themselves more.

[0033] The play suggestion unit is able to suggest games that include educational elements in conjunction with a child's learning content. For example, the generation AI analyzes a child's learning content and suggests games that include educational elements. For example, it may suggest games for solving math problems or science experiments. The generation AI also considers a child's learning progress and suggests games that are linked to the learning content. For example, it may suggest games related to the theme the child is studying. The generation AI also suggests games that include educational elements based on a child's learning content. For example, it may suggest games for learning about historical events or games for memorizing English vocabulary. In this way, by suggesting games that include educational elements in conjunction with a child's learning content, the child's learning effectiveness is improved.

[0034] The time management unit can monitor a child's concentration and suggest appropriate breaks when their concentration wanes. In the time management unit, for example, the generation AI monitors a child's concentration and suggests appropriate breaks when their concentration wanes. For example, it suggests a short break if the child is tired. In addition, the time management unit analyzes a child's concentration data and suggests breaks when their concentration wanes. For example, it suggests relaxing activities that will help the child regain their concentration. In addition, the time management unit monitors a child's concentration in real time and suggests breaks when their concentration wanes. For example, it suggests light exercise that will refresh the child. In this way, by suggesting appropriate breaks when a child's concentration wanes, the child's concentration can be restored.

[0035] The time management unit works in conjunction with the parent's schedule, adjusting playtime to suit the parent's schedule. For example, the generation AI analyzes the parent's schedule and adjusts the child's playtime to suit the parent's schedule. For example, it sets the child to play during times when the parent is busy. The time management unit also works in conjunction with the parent's calendar and adjusts the child's playtime to suit the parent's schedule. For example, it sets the child to play quietly while the parent is in a meeting. The time management unit also adjusts the child's playtime based on the parent's schedule. For example, it sets the child to play while the parent is doing housework. This adjusts playtime to suit the parent's schedule, reducing the burden on the parent.

[0036] The time management unit can gradually extend a child's play time, gradually increasing the amount of independent play time. In the time management unit, for example, the generation AI gradually extends a child's play time, gradually increasing the amount of independent play time. For example, it starts with a short time and gradually extends the play time. The time management unit also provides a program that allows the generation AI to gradually extend a child's play time, increasing the amount of independent play time. For example, it gradually increases the amount of time a child plays on their own. The time management unit also allows the generation AI to gradually extend a child's play time, increasing the amount of time a child plays independently. For example, it gradually lengthens the amount of time a child plays on their own. In this way, the child's play time is gradually extended and the amount of independent play time is gradually increased, thereby fostering a sense of independence.

[0037] The time management unit can synchronize a child's playtime with children in other households and provide time to play together online. For example, the generation AI in the time management unit synchronizes a child's playtime with children in other households and provides time to play together online. For example, the generation AI suggests games that can be played together via video calls. The time management unit also synchronizes a child's playtime with children in other households and suggests online activities that can be played together. For example, the generation AI suggests games to draw pictures together online. The time management unit also synchronizes a child's playtime with children in other households and provides games that can be enjoyed together online. For example, the generation AI suggests games to sing songs together online. In this way, the child's playtime can be synchronized with children in other households and provided with time to play together online, making the child more enjoyable.

[0038] The time management unit can notify the parent's smartphone or smartwatch of the amount of time the child is playing, allowing the parent to check in real time. For example, the generation AI in the time management unit notifies the parent's smartphone of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the time the child starts playing and the expected time to finish. The generation AI in the time management unit can also notify the parent's smartwatch of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the progress while the child is playing. The generation AI in the time management unit can also notify the parent's smart device of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the parent of the time when the child finishes playing. This increases the parent's sense of security by notifying the parent's smartphone or smartwatch of the amount of time the child is playing, allowing the parent to check in real time.

[0039] The variation providing unit can analyze children's play patterns and switch games at the optimal timing. For example, the generation AI in the variation providing unit analyzes children's play patterns and switches games at the optimal timing. For example, it suggests the next game before the child gets bored. In addition, the generation AI in the variation providing unit analyzes children's play data, understands play patterns, and switches games at the optimal timing. For example, it suggests the next game before the child loses concentration. In addition, the generation AI in the variation providing unit analyzes children's play patterns in real time and switches games at the optimal timing. For example, it suggests the next game if it detects signs that the child is getting bored. In this way, by analyzing children's play patterns and switching games at the optimal timing, children can continue playing without getting bored.

[0040] The variation providing unit can detect changes in a child's interests in real time and suggest games based on the new interests. For example, the generation AI in the variation providing unit detects changes in a child's interests in real time and suggests games based on the new interests. For example, if a child becomes interested in a new character, it suggests games using that character. The variation providing unit also analyzes changes in a child's interests and suggests games based on the new interests. For example, if a child becomes interested in a new theme, it suggests games related to that theme. The variation providing unit also monitors changes in a child's interests in real time and suggests games based on the new interests. For example, if a child becomes interested in a new activity, it suggests games that incorporate that activity. In this way, by detecting changes in a child's interests in real time and suggesting games based on the new interests, the child can continue playing without getting bored.

[0041] The variation providing unit can monitor the child's progress in play and set play goals that will give the child a sense of accomplishment. For example, the generation AI in the variation providing unit monitors the child's progress in play and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of completing a puzzle and praise the child when it is achieved. The variation providing unit also analyzes the child's progress in play and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of creating a specific shape with blocks and reward the child when it is achieved. The variation providing unit also monitors the child's progress in play in real time and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of reading a picture book to the end and praise the child when it is achieved. In this way, monitoring the child's progress in play and setting play goals that will give the child a sense of accomplishment increases the child's motivation.

[0042] The variation providing unit can share the child's play with the parent, allowing the parent to check the progress of the play. For example, the generation AI in the variation providing unit shares the child's play progress with the parent in real time. For example, the variation providing unit notifies the parent of the child's play progress on their smartphone. The variation providing unit also shares the child's play progress with the parent, allowing the parent to check the progress of the play. For example, the parent can check the child's play progress via an app. The variation providing unit also reports the child's play progress to the parent, allowing the parent to check the progress of the play. For example, the parent can receive the child's play progress by email. In this way, the child's play can be shared with the parent, allowing the parent to check the progress of the play, thereby increasing the parent's sense of security.

[0043] The variation providing unit can record the child's play so that it can be reviewed later. For example, the variation providing unit allows the generation AI to save a record of the child's play in a database so that it can be reviewed later. For example, it records which play the child played for how long. The variation providing unit also allows the generation AI to record the child's play progress so that it can be reviewed later. For example, it records the goals the child achieved and the results of the play. The variation providing unit also allows the generation AI to save data on the child's play so that it can be reviewed later. For example, it records the play the child enjoyed and the play the child got bored of. In this way, by recording the child's play so that it can be reviewed later, it is possible to check the child's growth.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The support system can also monitor children's eating and sleeping patterns to support healthy lifestyle habits. For example, it can send reminders to children to eat meals at appropriate times. It can also manage bedtimes to ensure children get enough sleep. It can also provide parents with nutritionally balanced meal suggestions to encourage their children to eat healthy meals. This can support children's healthy lifestyle habits and improve their overall health.

[0046] The support system can also monitor a child's learning progress and suggest play activities based on their progress. For example, if a child achieves a specific learning goal, it can suggest play activities to celebrate that achievement. It can also suggest fun activities related to the content they are learning to motivate them to study. It can also manage a good balance between study time and play time to help children concentrate on their studies. This can improve children's learning effectiveness and maintain their motivation to study.

[0047] The support system can also suggest activities to stimulate children's creativity. For example, it can provide time for children to freely draw pictures and share them with their parents. It can also suggest games in which children create their own stories and enjoy them together with their parents. It can also provide time for children to make their own crafts and show their creations to their parents. This can stimulate children's creativity and promote communication between parents and children.

[0048] The support system can also suggest activities to foster children's social skills. For example, it can schedule playtime for children with friends and suggest games and activities that can be enjoyed together. It can also suggest projects for children to complete in collaboration with other children to foster social skills. It can also suggest online collaborative activities to increase opportunities for children to communicate with other children. This helps foster children's social skills and build relationships with other children.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The play suggestion unit suggests appropriate play activities based on the child's age and interests. For example, it suggests simple puzzles or block games for a 3-year-old, picture book reading or simple crafts for a 5-year-old, and animal- or vehicle-related games based on the child's interests. Step 2: The time management unit manages the time so that the child plays for only the time specified by the parent. For example, if the parent gives the instruction, "Play only while I go to the toilet," the time will be measured and the child will be managed to play only during that time. When the time has elapsed, the unit will notify the child, "It's almost over." It also provides instructions on how to set the timer and criteria for extending or shortening the time. Step 3: The variation provider offers a variety of play options to keep children from getting bored. For example, if a child gets bored with one game, it suggests the next game. It might also suggest playing with blocks first, and then suggest reading a picture book if the child gets bored. It also offers other variations, such as indoor and outdoor play and digital games.

[0051] (Example 2) A support system according to an embodiment of the present invention is a system that supports dual-income households or households with small children in balancing childcare and housework. In this system, a generation AI provides a method for parents to let their children play alone for a specific period of time. This enables the support system to provide a method for parents to let their children play alone for a specific period of time.

[0052] The support system according to the embodiment includes a play suggestion unit, a time management unit, and a variation provision unit. The play suggestion unit suggests appropriate play based on the child's age and interests. For example, the play suggestion unit suggests simple puzzles or block games for a three-year-old child. The play suggestion unit also suggests picture book reading or simple crafts for a five-year-old child. The play suggestion unit also suggests animal- or vehicle-related play based on the child's interests. The time management unit manages time so that the child plays for only the time specified by the parent. For example, when a parent instructs the child to "play only while I go to the bathroom," the time management unit measures the time and manages the child to play only during that time. The time management unit also notifies the child when time has passed, saying, "It's almost over." The time management unit also provides a method for setting a timer and criteria for extending or shortening the time. The variation provision unit provides a variety of play options to prevent the child from getting bored. For example, if a child gets bored with one play, the variation provision unit suggests a new play. The variation providing unit first suggests playing with blocks, and then suggests reading a picture book to the child if the child gets bored. The variation providing unit also provides variations such as indoor games, outdoor games, digital games, etc. As a result, the support system according to the embodiment suggests appropriate games based on the child's age and interests, manages time, and provides variations in games, allowing the child to continue playing without getting bored.

[0053] The play suggestion unit analyzes a child's emotional state in real time and can suggest games that match the child's mood. For example, the generation AI analyzes a child's facial expressions and tone of voice to grasp a child's emotional state in real time. For example, if a child is tired, it will suggest relaxing games, and if a child is energetic, it will suggest active games. The generation AI also monitors a child's heart rate and body temperature to suggest games that take the child's physical condition into consideration. For example, if the heart rate is high, it will suggest quiet games, and if the heart rate is low, it will suggest games that encourage exercise. The generation AI also learns a child's past emotional data and suggests games that match the child's mood. For example, it will prioritize suggested games that the child has enjoyed in the past, improving the child's mood. This allows the game suggestion unit to suggest games that match the child's emotional state, making the child more enjoyable.

[0054] The play suggestion unit learns a child's past play history and can prioritize suggesting games that the child particularly enjoyed. For example, the generation AI stores the child's past play history in a database and analyzes the games that the child particularly enjoyed. For example, it prioritizes suggesting games that the child played for a long time or that the child frequently choses. The play suggestion unit also extracts patterns of games that the child particularly enjoyed based on the child's play history. For example, it prioritizes suggesting games that include specific themes or characters. The play suggestion unit also learns a child's play history and suggests games that the child particularly enjoyed in real time. For example, it suggests new games based on games that the child enjoyed in the past. In this way, by learning a child's past play history and prioritizing suggesting games that the child particularly enjoyed, the child can enjoy themselves more.

[0055] The play suggestion unit can monitor a child's physical activity level and suggest games that encourage moderate exercise. For example, the generation AI in the play suggestion unit monitors a child's physical activity level and suggests games that encourage moderate exercise. For example, if a child has been sitting for a long time, it suggests games that get the child moving. The play suggestion unit also analyzes a child's physical activity data and suggests games to help resolve a lack of exercise. For example, if a child has not achieved their daily exercise amount, it suggests games that encourage exercise. The play suggestion unit also monitors a child's physical activity level in real time and suggests games that encourage moderate exercise. For example, if a child is tired, it suggests light exercise, and if they are energetic, it suggests active exercise. In this way, by monitoring a child's physical activity level and suggesting games that encourage moderate exercise, children's health can be maintained.

[0056] The game suggestion unit can work with a child's friends and siblings to suggest games that can be played together. For example, the generation AI of the game suggestion unit collects information about a child's friends and siblings to suggest games that can be played together. For example, it suggests games and activities that can be enjoyed by multiple people. The game suggestion unit also works with a child's friends and siblings to suggest games that can be played together online. For example, it suggests games that can be played together via video calls. The game suggestion unit also takes into account the interests of a child's friends and siblings to suggest games that can be enjoyed together. For example, it suggests games based on common hobbies and interests. In this way, the game suggestion unit can work with a child's friends and siblings to suggest games that can be played together, allowing the child to enjoy themselves more.

[0057] The play suggestion unit is able to suggest games that include educational elements in conjunction with a child's learning content. For example, the generation AI analyzes a child's learning content and suggests games that include educational elements. For example, it may suggest games for solving math problems or science experiments. The generation AI also considers a child's learning progress and suggests games that are linked to the learning content. For example, it may suggest games related to the theme the child is studying. The generation AI also suggests games that include educational elements based on a child's learning content. For example, it may suggest games for learning about historical events or games for memorizing English vocabulary. In this way, by suggesting games that include educational elements in conjunction with a child's learning content, the child's learning effectiveness is improved.

[0058] The play suggestion unit analyzes a child's emotional state in real time and can suggest games that will help the child relax if they are feeling stressed. For example, the generation AI in the play suggestion unit analyzes a child's emotional state in real time and suggests games that will help the child relax if they are feeling stressed. For example, it suggests games such as listening to music that will calm the child. The play suggestion unit also monitors a child's stress level and suggests games that will help the child relax. For example, it suggests games such as drawing that will help the child relax. The play suggestion unit also suggests games that will help the child relax if they are feeling stressed based on the child's emotional data. For example, it suggests games such as meditation or deep breathing that will help the child relax. In this way, by suggesting games that will help the child relax if they are feeling stressed, the child's stress can be reduced.

[0059] The time management unit can monitor a child's concentration and suggest appropriate breaks when their concentration wanes. In the time management unit, for example, the generation AI monitors a child's concentration and suggests appropriate breaks when their concentration wanes. For example, it suggests a short break if the child is tired. In addition, the time management unit analyzes a child's concentration data and suggests breaks when their concentration wanes. For example, it suggests relaxing activities that will help the child regain their concentration. In addition, the time management unit monitors a child's concentration in real time and suggests breaks when their concentration wanes. For example, it suggests light exercise that will refresh the child. In this way, by suggesting appropriate breaks when a child's concentration wanes, the child's concentration can be restored.

[0060] The time management unit works in conjunction with the parent's schedule, adjusting playtime to suit the parent's schedule. For example, the generation AI analyzes the parent's schedule and adjusts the child's playtime to suit the parent's schedule. For example, it sets the child to play during times when the parent is busy. The time management unit also works in conjunction with the parent's calendar and adjusts the child's playtime to suit the parent's schedule. For example, it sets the child to play quietly while the parent is in a meeting. The time management unit also adjusts the child's playtime based on the parent's schedule. For example, it sets the child to play while the parent is doing housework. This adjusts playtime to suit the parent's schedule, reducing the burden on the parent.

[0061] The time management unit can gradually extend a child's play time, gradually increasing the amount of independent play time. In the time management unit, for example, the generation AI gradually extends a child's play time, gradually increasing the amount of independent play time. For example, it starts with a short time and gradually extends the play time. The time management unit also provides a program that allows the generation AI to gradually extend a child's play time, increasing the amount of independent play time. For example, it gradually increases the amount of time a child plays on their own. The time management unit also allows the generation AI to gradually extend a child's play time, increasing the amount of time a child plays independently. For example, it gradually lengthens the amount of time a child plays on their own. In this way, the child's play time is gradually extended and the amount of independent play time is gradually increased, thereby fostering a sense of independence.

[0062] The time management unit can synchronize a child's playtime with children in other households and provide time to play together online. For example, the generation AI in the time management unit synchronizes a child's playtime with children in other households and provides time to play together online. For example, the generation AI suggests games that can be played together via video calls. The time management unit also synchronizes a child's playtime with children in other households and suggests online activities that can be played together. For example, the generation AI suggests games to draw pictures together online. The time management unit also synchronizes a child's playtime with children in other households and provides games that can be enjoyed together online. For example, the generation AI suggests games to sing songs together online. In this way, the child's playtime can be synchronized with children in other households and provided with time to play together online, making the child more enjoyable.

[0063] The time management unit can notify the parent's smartphone or smartwatch of the amount of time the child is playing, allowing the parent to check in real time. For example, the generation AI in the time management unit notifies the parent's smartphone of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the time the child starts playing and the expected time to finish. The generation AI in the time management unit can also notify the parent's smartwatch of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the progress while the child is playing. The generation AI in the time management unit can also notify the parent's smart device of the amount of time the child is playing, allowing the parent to check in real time. For example, it notifies the parent of the time when the child finishes playing. This increases the parent's sense of security by notifying the parent's smartphone or smartwatch of the amount of time the child is playing, allowing the parent to check in real time.

[0064] The time management unit uses the emotion estimation function to suggest new games when a child gets bored of playing, thereby extending play time. In the time management unit, for example, the generation AI analyzes the child's emotional state and suggests new games when a child gets bored of playing. For example, if a child feels bored, it suggests the next game. In addition, the time management unit uses the child's emotional data to suggest new games when a child gets bored of playing. For example, it suggests a different game that the child might be interested in. In addition, the time management unit uses the child's emotion estimation function to suggest new games when a child gets bored of playing, thereby extending play time. For example, if a child feels bored, it suggests the next game. In this way, by suggesting new games when a child gets bored of playing and extending play time, children can continue playing without getting bored.

[0065] The variation providing unit can analyze children's play patterns and switch games at the optimal timing. For example, the generation AI in the variation providing unit analyzes children's play patterns and switches games at the optimal timing. For example, it suggests the next game before the child gets bored. In addition, the generation AI in the variation providing unit analyzes children's play data, understands play patterns, and switches games at the optimal timing. For example, it suggests the next game before the child loses concentration. In addition, the generation AI in the variation providing unit analyzes children's play patterns in real time and switches games at the optimal timing. For example, it suggests the next game if it detects signs that the child is getting bored. In this way, by analyzing children's play patterns and switching games at the optimal timing, children can continue playing without getting bored.

[0066] The variation providing unit can detect changes in a child's interests in real time and suggest games based on the new interests. For example, the generation AI in the variation providing unit detects changes in a child's interests in real time and suggests games based on the new interests. For example, if a child becomes interested in a new character, it suggests games using that character. The variation providing unit also analyzes changes in a child's interests and suggests games based on the new interests. For example, if a child becomes interested in a new theme, it suggests games related to that theme. The variation providing unit also monitors changes in a child's interests in real time and suggests games based on the new interests. For example, if a child becomes interested in a new activity, it suggests games that incorporate that activity. In this way, by detecting changes in a child's interests in real time and suggesting games based on the new interests, the child can continue playing without getting bored.

[0067] The variation providing unit can monitor the child's progress in play and set play goals that will give the child a sense of accomplishment. For example, the generation AI in the variation providing unit monitors the child's progress in play and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of completing a puzzle and praise the child when it is achieved. The variation providing unit also analyzes the child's progress in play and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of creating a specific shape with blocks and reward the child when it is achieved. The variation providing unit also monitors the child's progress in play in real time and sets play goals that will give the child a sense of accomplishment. For example, the generation AI can set a goal of reading a picture book to the end and praise the child when it is achieved. In this way, monitoring the child's progress in play and setting play goals that will give the child a sense of accomplishment increases the child's motivation.

[0068] The variation providing unit can share the child's play with the parent, allowing the parent to check the progress of the play. For example, the generation AI in the variation providing unit shares the child's play progress with the parent in real time. For example, the variation providing unit notifies the parent of the child's play progress on their smartphone. The variation providing unit also shares the child's play progress with the parent, allowing the parent to check the progress of the play. For example, the parent can check the child's play progress via an app. The variation providing unit also reports the child's play progress to the parent, allowing the parent to check the progress of the play. For example, the parent can receive the child's play progress by email. In this way, the child's play can be shared with the parent, allowing the parent to check the progress of the play, thereby increasing the parent's sense of security.

[0069] The variation providing unit can record the child's play so that it can be reviewed later. For example, the variation providing unit allows the generation AI to save a record of the child's play in a database so that it can be reviewed later. For example, it records which play the child played for how long. The variation providing unit also allows the generation AI to record the child's play progress so that it can be reviewed later. For example, it records the goals the child achieved and the results of the play. The variation providing unit also allows the generation AI to save data on the child's play so that it can be reviewed later. For example, it records the play the child enjoyed and the play the child got bored of. In this way, by recording the child's play so that it can be reviewed later, it is possible to check the child's growth.

[0070] The variation providing unit can use the emotion estimation function to record games that the child particularly enjoyed and reflect this in the next game suggestions. In the variation providing unit, for example, the generation AI analyzes the child's emotional state and records games that the child particularly enjoyed. For example, it records games that the child played with a smile. In addition, the variation providing unit records games that the child particularly enjoyed based on the child's emotional data and reflects this in the next game suggestions. For example, it records games that the child played with excitement. In addition, the variation providing unit uses the child's emotion estimation function to record games that the child particularly enjoyed and reflects this in the next game suggestions. For example, it records games that the child played with satisfaction. In this way, the games that the child particularly enjoyed are recorded and reflected in the next game suggestions, allowing the child to enjoy themselves even more.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The support system can also monitor parents' stress levels and provide them with time to relax. For example, it can set up quiet play for children during busy times, ensuring parents have time to relax. If parents are feeling stressed, it can also extend the time children spend playing alone. Furthermore, to help parents relax, it can suggest relaxing music or meditation for parents while their children are playing. This helps reduce stress for parents and provides them with time to relax.

[0073] The support system can also monitor children's eating and sleeping patterns to support healthy lifestyle habits. For example, it can send reminders to children to eat meals at appropriate times. It can also manage bedtimes to ensure children get enough sleep. It can also provide parents with nutritionally balanced meal suggestions to encourage their children to eat healthy meals. This can support children's healthy lifestyle habits and improve their overall health.

[0074] The support system can also monitor a child's learning progress and suggest play activities based on their progress. For example, if a child achieves a specific learning goal, it can suggest play activities to celebrate that achievement. It can also suggest fun activities related to the content they are learning to motivate them to study. It can also manage a good balance between study time and play time to help children concentrate on their studies. This can improve children's learning effectiveness and maintain their motivation to study.

[0075] The support system can also suggest activities to stimulate children's creativity. For example, it can provide time for children to freely draw pictures and share them with their parents. It can also suggest games in which children create their own stories and enjoy them together with their parents. It can also provide time for children to make their own crafts and show their creations to their parents. This can stimulate children's creativity and promote communication between parents and children.

[0076] The support system can also suggest activities to foster children's social skills. For example, it can schedule playtime for children with friends and suggest games and activities that can be enjoyed together. It can also suggest projects for children to complete in collaboration with other children to foster social skills. It can also suggest online collaborative activities to increase opportunities for children to communicate with other children. This helps foster children's social skills and build relationships with other children.

[0077] The support system can also analyze a child's emotional state and suggest activities that will make the child feel reassured if the child is feeling anxious. For example, if a child feels anxious, it can suggest spending time with their parents. It can also suggest music or stories that will make the child feel reassured. It can also suggest meditation or deep breathing activities to help the child relax. This can provide a sense of security and stabilize the child's emotions if the child is feeling anxious.

[0078] The support system can also analyze a child's emotional state and suggest activities to calm the child if they are excited. For example, if a child is excited, it can suggest time to listen to quiet music. It can also suggest drawing activities to help the child relax. It can also suggest meditation or deep breathing activities to help the child calm down. In this way, if a child is excited, it can suggest activities to calm them down and stabilize their emotions.

[0079] The support system can also analyze the child's emotional state and suggest activities to improve the child's mood if the child is sad. For example, if a child is sad, it can suggest spending time with their parents. It can also suggest games and activities that the child will find fun. It can also suggest funny videos and stories that will make the child smile. This can improve the child's mood and stabilize their emotions if the child is sad.

[0080] The support system can also analyze a child's emotional state and suggest activities to calm the child if they are angry. For example, if a child is angry, it can suggest time to listen to quiet music. It can also suggest drawing activities to help the child relax. It can also suggest meditation or deep breathing activities to help the child calm down. In this way, if a child is angry, it can suggest activities to calm them down and stabilize their emotions.

[0081] The support system can also analyze a child's emotional state and suggest activities to calm the child if they are excited. For example, if a child is excited, it can suggest time to listen to quiet music. It can also suggest drawing activities to help the child relax. It can also suggest meditation or deep breathing activities to help the child calm down. In this way, if a child is excited, it can suggest activities to calm them down and stabilize their emotions.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The play suggestion unit suggests appropriate play activities based on the child's age and interests. For example, it suggests simple puzzles or block games for a 3-year-old, picture book reading or simple crafts for a 5-year-old, and animal- or vehicle-related games based on the child's interests. Step 2: The time management unit manages the time so that the child plays for only the time specified by the parent. For example, if the parent gives the instruction, "Play only while I go to the toilet," the time will be measured and the child will be managed to play only during that time. When the time has elapsed, the unit will notify the child, "It's almost over." It also provides instructions on how to set the timer and criteria for extending or shortening the time. Step 3: The variation provider offers a variety of play options to keep children from getting bored. For example, if a child gets bored with one game, it suggests the next game. It might also suggest playing with blocks first, and then suggest reading a picture book if the child gets bored. It also offers other variations, such as indoor and outdoor play and digital games.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 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.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the 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.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. The play suggestion department suggests appropriate play based on the child's age and interests, A time management section manages time so that children can play only for the time specified by parents. A variation providing unit that provides a variety of play so that children do not get bored. A system characterized by:

2. The play suggestion unit Analyzing the child's emotional state in real time and suggesting the play that matches the child's mood 2. The system of claim 1.

3. The play suggestion unit The system learns the child's past play history and gives priority to suggesting the play that the child particularly enjoyed.

2. The system of claim 1.

4. The play suggestion unit Monitor the child's physical activity level and suggest activities that encourage moderate exercise.

2. The system of claim 1.

5. The play suggestion unit Collaborate with the child's friends and siblings to suggest games that can be played together.

2. The system of claim 1.

6. The play suggestion unit Propose the above-mentioned play that includes educational elements in conjunction with the child's learning content.

2. The system of claim 1.

7. The play suggestion unit Analyzing the child's emotional state in real time and suggesting a relaxing activity if the child is feeling stressed 2. The system of claim 1.

8. The time management unit Monitor the child's concentration and suggest appropriate breaks when the child's concentration decreases.

2. The system of claim 1.

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