System

Abstract

An object of the system according to the embodiment is to dynamically adjust the content based on the interest or reaction of the child.SOLUTION: A system includes a presetting unit, a content selection unit, a reaction analysis unit, and a dynamic adjustment unit. The presetting unit selects content based on the presetting performed by the parent. The content selection unit provides the content selected by the presetting unit. The reaction analysis unit analyzes a reaction of the child to the content provided by the content selection unit. The dynamic adjustment unit dynamically adjusts the content on the basis of the reaction of the child analyzed by the reaction analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to dynamically adjust content based on a child's interests and reactions.

[0005] The system of the embodiment aims to dynamically adjust content based on the child's interests and reactions. [Means for solving the problem]

[0006] The system according to the embodiment includes a preset unit, a content selection unit, a response analysis unit, and a dynamic adjustment unit. The preset unit selects content based on a preset configured by a parent. The content selection unit provides the content selected by the preset unit. The response analysis unit analyzes a child's response to the content provided by the content selection unit. The dynamic adjustment unit dynamically adjusts the content based on the child's response analyzed by the response analysis unit. [Effects of the Invention]

[0007] An embodiment of the system can dynamically adjust content based on a child's interests and reactions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 content provision system according to an embodiment of the present invention is a system in which AI selects and provides content based on parental settings, and provides content that matches a child's interests while analyzing the child's reactions using a camera, etc. This allows the content provision system to provide optimal content for the child and keep them interested.

[0029] A content provision system according to an embodiment includes a preset unit, a content selection unit, a response analysis unit, and a dynamic adjustment unit. The preset unit selects content based on a parent's preset settings. For example, a category or specific theme, such as educational videos, music, or games, can be selected according to the parent's criteria. The content selection unit provides the content selected by the preset unit. For example, if a parent sets "educational videos about animals," the content selection unit selects videos that match the criteria and provides them to the child. The response analysis unit analyzes the child's response to the content provided by the content selection unit. For example, the response analysis unit uses a camera or sensor to analyze the child's facial expressions, gaze, and movements to determine the child's level of interest. The dynamic adjustment unit dynamically adjusts the content based on the child's response analyzed by the response analysis unit. For example, if the child shows no interest in a particular video, the dynamic adjustment unit selects and provides a different video. This allows the content provision system according to an embodiment to provide appropriate content based on the parent's settings and dynamically adjust the content according to the child's response, thereby maintaining the child's interest.

[0030] The pre-setting unit can automatically incorporate history data to reflect the child's past learning history and interests into the criteria set by the parent. The pre-setting unit, for example, builds a system that automatically incorporates the child's past learning history into the criteria set by the parent. For example, the pre-setting unit reflects the child's interests based on data on videos watched in the past and games played in the past. This allows the child's past learning history and interests to be reflected, making it possible to provide more appropriate content.

[0031] The pre-setting unit can utilize community data for referring to the settings and feedback of other parents when setting standards for parents. The pre-setting unit, for example, builds a system that utilizes community data for referring to the settings and feedback of other parents. For example, parents with children in the same age group can share their settings. This allows for more effective content selection by referring to the settings and feedback of other parents.

[0032] The pre-setting unit can add a function that allows parents to intuitively set the criteria using voice input. The pre-setting unit, for example, builds a system that uses voice input to set the criteria. For example, AI analyzes what the parent says and reflects it as the setting criteria. This allows parents to intuitively set the criteria using voice input.

[0033] The pre-setting unit can add a function that allows multiple children's profiles to be managed simultaneously based on criteria set by a parent. The pre-setting unit, for example, builds a system that allows multiple children's profiles to be managed simultaneously based on criteria set by a parent. For example, each child's interests and learning progress can be individually set. This allows multiple children's profiles to be managed simultaneously, improving the efficiency of settings.

[0034] The content selection unit can provide content of an appropriate level of difficulty, taking into account the child's learning progress and level of understanding. The content selection unit, for example, builds a system that takes the child's learning progress into consideration when selecting content. For example, it selects content of an appropriate level of difficulty based on past learning data. This makes it possible to improve learning effectiveness by providing content of an appropriate level of difficulty according to the child's learning progress and level of understanding.

[0035] The content selection unit can be added with a function to automatically select a theme according to the season or event. For example, the content selection unit constructs a system that automatically selects a theme according to the season or event when selecting content. For example, content related to Christmas or Halloween can be provided. This automatically selecting a theme according to the season or event can keep children interested.

[0036] The content selection unit can be added with a function that considers the interests of a child's friends and siblings in addition to the criteria set by the parent. For example, the content selection unit can build a system that considers the interests of a child's friends and siblings in addition to the criteria set by the parent when selecting content. For example, it can provide content that has been highly rated by friends. This allows more appropriate content to be provided by taking into account the interests of a child's friends and siblings.

[0037] The content selection unit can add a function to provide a combination of different media formats. For example, when selecting content, the content selection unit builds a system that provides a combination of different media formats. For example, educational content that combines video and music can be provided. This allows children to continue to be interested in the content by providing a combination of different media formats.

[0038] The reaction analysis unit can add a function to track changes in interests by comparing past reaction data when analyzing a child's reaction. For example, the reaction analysis unit builds a system that compares past reaction data when analyzing a child's reaction. For example, it tracks changes in interests based on past viewing history and reaction data. This makes it possible to provide more appropriate content by tracking changes in children's interests.

[0039] The reaction analysis unit can collect more diversified data by using multiple cameras and sensors when analyzing a child's reaction. For example, the reaction analysis unit constructs a system that uses multiple cameras to collect diversified data when analyzing a child's reaction. For example, it analyzes video from different angles. By using multiple cameras and sensors, the reaction of a child can be analyzed in more detail.

[0040] The reaction analysis unit can add a function that allows parents and teachers to provide feedback in real time when analyzing a child's reaction. For example, the reaction analysis unit builds a system that allows parents and teachers to provide feedback in real time when analyzing a child's reaction. For example, feedback can be sent through an app. This allows parents and teachers to provide feedback in real time and immediately respond to the child's reaction.

[0041] The dynamic adjustment unit can provide optimal content by taking into account a child's learning goals and achievement level when dynamically adjusting the content. The dynamic adjustment unit, for example, builds a system that takes into account a child's learning goals when dynamically adjusting the content. For example, it provides content aimed at a specific learning goal. This can improve learning effectiveness by providing optimal content according to a child's learning goals and achievement level.

[0042] When dynamically adjusting content, the dynamic adjustment unit can detect the child's physical condition and mood using a sensor and provide content that corresponds to that. For example, when dynamically adjusting content, the dynamic adjustment unit constructs a system that detects the child's physical condition using a sensor. For example, sensors that measure heart rate and body temperature are used. This makes it possible to provide content that takes the child's health into consideration by providing content that corresponds to the child's physical condition and mood.

[0043] The dynamic adjustment unit can add a function that allows parents to intervene in real time when dynamic content adjustment is being made. The dynamic adjustment unit, for example, builds a system that allows parents to intervene in real time when dynamic content adjustment is being made. For example, the content is changed through an app. This allows parents to intervene in real time and immediately respond to their child's reactions.

[0044] The dynamic adjustment unit can add a function for seamlessly switching content between different devices when dynamically adjusting the content. For example, the dynamic adjustment unit builds a system for seamlessly switching content between different devices when dynamically adjusting the content. For example, switching from a tablet to a smartphone. This allows seamless switching of content between different devices to keep children engaged.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The content providing system may further include a guideline providing unit for supporting parental settings. The guideline providing unit provides guidelines and hints that parents can refer to when setting up. For example, it may present a list of recommended content by age group or how to select content with high educational value. This allows parents to set up more effectively and helps provide content that is appropriate for their children.

[0047] The content providing system can further include a health management unit that monitors the child's health condition. The health management unit uses sensors to detect the child's physical condition and health status and provides appropriate content. For example, if the child is tired, it provides relaxing content, and if the child is energetic, it provides active content. This makes it possible to provide content that suits the child's health condition.

[0048] The content provision system can further include a report generation unit that reports a child's learning progress based on criteria set by the parent. The report generation unit compiles a report of the child's learning progress and changes in interests based on the content viewed by the child and their reaction data. For example, it can display a graph of weekly learning progress or changes in interest in a specific topic. This makes it easier for parents to understand their child's learning situation.

[0049] The content provision system may further include a goal setting unit that sets learning goals for the child based on criteria set by the parent. The goal setting unit allows the parent to set learning goals for the child and provides content according to those goals. For example, it provides content for acquiring specific skills and monitors the degree of achievement. This makes it possible to provide content that is effective according to the child's learning goals.

[0050] The content provision system may further include a style adaptation unit that takes into account a child's learning style based on criteria set by the parent. The style adaptation unit provides content according to the child's learning style (visual, auditory, experiential, etc.). For example, visual-oriented content is provided to a visual child, and audio-oriented content is provided to an auditory child. This makes it possible to provide optimal content according to a child's learning style.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The preset unit selects content based on parental presets. For example, parents can select categories or specific themes such as educational videos, music, games, etc., according to their set criteria. Step 2: The content selector provides the content selected by the pre-setting unit. For example, if a parent sets "educational videos about animals," the content selector selects videos that meet that criteria and provides them to the child. Step 3: The reaction analysis unit analyzes the child's reaction to the content provided by the content selection unit. For example, it uses cameras and sensors to analyze the child's facial expressions, gaze, and movements to determine the child's level of interest. Step 4: The dynamic adjustment unit dynamically adjusts the content based on the child's reaction analyzed by the reaction analysis unit. For example, if the child is not interested in a particular video, the dynamic adjustment unit selects and provides another video.

[0053] (Example 2) A content provision system according to an embodiment of the present invention is a system in which AI selects and provides content based on parental settings, and provides content that matches a child's interests while analyzing the child's reactions using a camera, etc. This allows the content provision system to provide optimal content for the child and keep them interested.

[0054] A content provision system according to an embodiment includes a preset unit, a content selection unit, a response analysis unit, and a dynamic adjustment unit. The preset unit selects content based on a parent's preset settings. For example, a category or specific theme, such as educational videos, music, or games, can be selected according to the parent's criteria. The content selection unit provides the content selected by the preset unit. For example, if a parent sets "educational videos about animals," the content selection unit selects videos that match the criteria and provides them to the child. The response analysis unit analyzes the child's response to the content provided by the content selection unit. For example, the response analysis unit uses a camera or sensor to analyze the child's facial expressions, gaze, and movements to determine the child's level of interest. The dynamic adjustment unit dynamically adjusts the content based on the child's response analyzed by the response analysis unit. For example, if the child shows no interest in a particular video, the dynamic adjustment unit selects and provides a different video. This allows the content provision system according to an embodiment to provide appropriate content based on the parent's settings and dynamically adjust the content according to the child's response, thereby maintaining the child's interest.

[0055] The pre-setting unit can automatically incorporate history data to reflect the child's past learning history and interests into the criteria set by the parent. The pre-setting unit, for example, builds a system that automatically incorporates the child's past learning history into the criteria set by the parent. For example, the pre-setting unit reflects the child's interests based on data on videos watched in the past and games played in the past. This allows the child's past learning history and interests to be reflected, making it possible to provide more appropriate content.

[0056] The pre-setting unit can utilize community data for referring to the settings and feedback of other parents when setting standards for parents. The pre-setting unit, for example, builds a system that utilizes community data for referring to the settings and feedback of other parents. For example, parents with children in the same age group can share their settings. This allows for more effective content selection by referring to the settings and feedback of other parents.

[0057] The pre-setting unit can use the emotion estimation function to analyze the emotions of the parent when setting up the device and provide setup support to reduce stress. For example, the pre-setting unit uses the emotion estimation function to build a system that analyzes the emotions of the parent when setting up the device in real time. For example, the pre-setting unit can analyze the parent's facial expressions and voice to detect stress. This reduces stress for the parent and allows the device to be set up more comfortably.

[0058] The pre-setting unit can add a function that allows parents to intuitively set the criteria using voice input. The pre-setting unit, for example, builds a system that uses voice input to set the criteria. For example, AI analyzes what the parent says and reflects it as the setting criteria. This allows parents to intuitively set the criteria using voice input.

[0059] The pre-setting unit can add a function that allows multiple children's profiles to be managed simultaneously based on criteria set by a parent. The pre-setting unit, for example, builds a system that allows multiple children's profiles to be managed simultaneously based on criteria set by a parent. For example, each child's interests and learning progress can be individually set. This allows multiple children's profiles to be managed simultaneously, improving the efficiency of settings.

[0060] The pre-setting unit can use the emotion estimation function to provide real-time feedback on the emotions of the parent when setting up the device and make suggestions to elicit positive emotions. The pre-setting unit, for example, uses the emotion estimation function to build a system that provides real-time feedback on the emotions of the parent when setting up the device. For example, the pre-setting unit analyzes the parent's facial expressions and voice and displays an emotion score. This provides real-time feedback on the parent's emotions and elicits positive emotions, making the setting process more comfortable.

[0061] The content selection unit can provide content of an appropriate level of difficulty, taking into account the child's learning progress and level of understanding. The content selection unit, for example, builds a system that takes the child's learning progress into consideration when selecting content. For example, it selects content of an appropriate level of difficulty based on past learning data. This makes it possible to improve learning effectiveness by providing content of an appropriate level of difficulty according to the child's learning progress and level of understanding.

[0062] The content selection unit can be added with a function to automatically select a theme according to the season or event. For example, the content selection unit constructs a system that automatically selects a theme according to the season or event when selecting content. For example, content related to Christmas or Halloween can be provided. This automatically selecting a theme according to the season or event can keep children interested.

[0063] The content selection unit can use the emotion estimation function to preferentially select content to which children have responded positively in the past. The content selection unit, for example, uses the emotion estimation function to build a system that preferentially selects content to which children have responded positively in the past. For example, it analyzes facial expressions of smiles and excitement. This allows the content to be preferentially selected to which children have responded positively in the past, thereby continuing to attract the child's interest.

[0064] The content selection unit can be added with a function that considers the interests of a child's friends and siblings in addition to the criteria set by the parent. For example, the content selection unit can build a system that considers the interests of a child's friends and siblings in addition to the criteria set by the parent when selecting content. For example, it can provide content that has been highly rated by friends. This allows more appropriate content to be provided by taking into account the interests of a child's friends and siblings.

[0065] The content selection unit can add a function to provide a combination of different media formats. For example, when selecting content, the content selection unit builds a system that provides a combination of different media formats. For example, educational content that combines video and music can be provided. This allows children to continue to be interested in the content by providing a combination of different media formats.

[0066] The content selection unit can use the emotion estimation function to monitor in real time the emotions of children when they watch content and provide optimal content. The content selection unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of children when they watch content. For example, it analyzes the child's facial expressions and voice. This allows the system to monitor the child's emotions in real time and provide optimal content to keep the child interested.

[0067] The reaction analysis unit can analyze not only facial expressions but also tone of voice when analyzing a child's reaction. The reaction analysis unit, for example, builds a system that analyzes not only facial expressions but also tone of voice when analyzing a child's reaction. For example, it analyzes voice frequency and volume to evaluate emotions. This makes it possible to analyze not only facial expressions but also tone of voice to more accurately evaluate a child's reaction.

[0068] The reaction analysis unit can add a function to track changes in interests by comparing past reaction data when analyzing a child's reaction. For example, the reaction analysis unit builds a system that compares past reaction data when analyzing a child's reaction. For example, it tracks changes in interests based on past viewing history and reaction data. This makes it possible to provide more appropriate content by tracking changes in children's interests.

[0069] The reaction analysis unit can use the emotion estimation function to analyze changes in a child's emotions in real time and identify peaks of interest. The reaction analysis unit, for example, uses the emotion estimation function to build a system that analyzes changes in a child's emotions in real time. For example, it calculates an emotion score based on facial expressions and tone of voice. This allows for identifying peaks of a child's interest, making it possible to provide more effective content.

[0070] The reaction analysis unit can collect more diversified data by using multiple cameras and sensors when analyzing a child's reaction. For example, the reaction analysis unit constructs a system that uses multiple cameras to collect diversified data when analyzing a child's reaction. For example, it analyzes video from different angles. By using multiple cameras and sensors, the reaction of a child can be analyzed in more detail.

[0071] The reaction analysis unit can add a function that allows parents and teachers to provide feedback in real time when analyzing a child's reaction. For example, the reaction analysis unit builds a system that allows parents and teachers to provide feedback in real time when analyzing a child's reaction. For example, feedback can be sent through an app. This allows parents and teachers to provide feedback in real time and immediately respond to the child's reaction.

[0072] The reaction analysis unit can use the emotion estimation function to notify parents of their children's reactions in real time, allowing the parents to take appropriate action. The reaction analysis unit, for example, uses the emotion estimation function to build a system that notifies parents of their children's reactions in real time. For example, it displays an emotion score through an app. This allows parents to understand their children's reactions in real time and take appropriate action.

[0073] The dynamic adjustment unit can provide optimal content by taking into account a child's learning goals and achievement level when dynamically adjusting the content. The dynamic adjustment unit, for example, builds a system that takes into account a child's learning goals when dynamically adjusting the content. For example, it provides content aimed at a specific learning goal. This can improve learning effectiveness by providing optimal content according to a child's learning goals and achievement level.

[0074] When dynamically adjusting content, the dynamic adjustment unit can detect the child's physical condition and mood using a sensor and provide content that corresponds to that. For example, when dynamically adjusting content, the dynamic adjustment unit constructs a system that detects the child's physical condition using a sensor. For example, sensors that measure heart rate and body temperature are used. This makes it possible to provide content that takes the child's health into consideration by providing content that corresponds to the child's physical condition and mood.

[0075] The dynamic adjustment unit can use the emotion estimation function to adjust the difficulty level and theme of the content in accordance with changes in the child's emotions. The dynamic adjustment unit, for example, uses the emotion estimation function to build a system that adjusts the difficulty level of the content in accordance with changes in the child's emotions. For example, the difficulty level may be lowered if the emotion score is low. This allows the difficulty level and theme of the content to be adjusted in accordance with changes in the child's emotions, thereby keeping the child interested.

[0076] The dynamic adjustment unit can add a function that allows parents to intervene in real time when dynamic content adjustment is being made. The dynamic adjustment unit, for example, builds a system that allows parents to intervene in real time when dynamic content adjustment is being made. For example, the content is changed through an app. This allows parents to intervene in real time and immediately respond to their child's reactions.

[0077] The dynamic adjustment unit can add a function for seamlessly switching content between different devices when dynamically adjusting the content. For example, the dynamic adjustment unit builds a system for seamlessly switching content between different devices when dynamically adjusting the content. For example, switching from a tablet to a smartphone. This allows seamless switching of content between different devices to keep children engaged.

[0078] The dynamic adjustment unit can use the emotion estimation function to provide relaxing content or content that improves concentration according to the child's emotions. The dynamic adjustment unit, for example, uses the emotion estimation function to build a system that provides relaxing content according to the child's emotions. For example, relaxing music or meditation videos are provided. This makes it possible to provide appropriate content according to the child's psychological state by providing relaxing content or content that improves concentration according to the child's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The content providing system may further include a guideline providing unit for supporting parental settings. The guideline providing unit provides guidelines and hints that parents can refer to when setting up. For example, it may present a list of recommended content by age group or how to select content with high educational value. This allows parents to set up more effectively and helps provide content that is appropriate for their children.

[0081] The content providing system can further include a health management unit that monitors the child's health condition. The health management unit uses sensors to detect the child's physical condition and health status and provides appropriate content. For example, if the child is tired, it provides relaxing content, and if the child is energetic, it provides active content. This makes it possible to provide content that suits the child's health condition.

[0082] The content provision system can further include a report generation unit that reports a child's learning progress based on criteria set by the parent. The report generation unit compiles a report of the child's learning progress and changes in interests based on the content viewed by the child and their reaction data. For example, it can display a graph of weekly learning progress or changes in interest in a specific topic. This makes it easier for parents to understand their child's learning situation.

[0083] The content provision system may further include a goal setting unit that sets learning goals for the child based on criteria set by the parent. The goal setting unit allows the parent to set learning goals for the child and provides content according to those goals. For example, it provides content for acquiring specific skills and monitors the degree of achievement. This makes it possible to provide content that is effective according to the child's learning goals.

[0084] The content provision system may further include a style adaptation unit that takes into account a child's learning style based on criteria set by the parent. The style adaptation unit provides content according to the child's learning style (visual, auditory, experiential, etc.). For example, visual-oriented content is provided to a visual child, and audio-oriented content is provided to an auditory child. This makes it possible to provide optimal content according to a child's learning style.

[0085] The content provision system may further include an emotion adaptation unit that estimates the child's emotion based on criteria set by the parent and provides content based on the estimated emotion. For example, the emotion adaptation unit provides relaxing content when the child is stressed, and content that helps the child concentrate when the child is excited. This makes it possible to provide appropriate content according to the child's emotion.

[0086] The content providing system may further include an emotion reporting unit that estimates the child's emotions based on criteria set by the parent and reports the child's learning progress based on the estimated emotions. The emotion reporting unit may, for example, compile a report of content in which the child expressed positive emotions and content in which the child expressed negative emotions. This makes it easier for parents to understand changes in their child's emotions.

[0087] The content providing system may further include an emotional difficulty adjustment unit that estimates the child's emotions based on standards set by the parent and adjusts the difficulty level of the content based on the estimated emotions. For example, the emotional difficulty adjustment unit lowers the difficulty level when the child is feeling stressed and raises the difficulty level when the child is relaxed. This makes it possible to provide content with an appropriate difficulty level according to the child's emotions.

[0088] The content providing system may further include an emotion theme adjustment unit that estimates the child's emotion based on criteria set by the parent and adjusts the theme of the content based on the estimated emotion. For example, the emotion theme adjustment unit provides content with a fun theme when the child is showing positive emotion, and content with a relaxing theme when the child is showing negative emotion. This makes it possible to provide content with an appropriate theme according to the child's emotion.

[0089] The content provision system may further include an emotion timing adjustment unit that estimates a child's emotions based on criteria set by the parent and adjusts the timing of content provision based on the estimated emotions. For example, the emotion timing adjustment unit provides learning content when the child is relaxed and play content when the child is excited. This makes it possible to provide content at appropriate times according to the child's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The preset unit selects content based on parental presets. For example, parents can select categories or specific themes such as educational videos, music, games, etc., according to their set criteria. Step 2: The content selector provides the content selected by the pre-setting unit. For example, if a parent sets "educational videos about animals," the content selector selects videos that meet that criteria and provides them to the child. Step 3: The reaction analysis unit analyzes the child's reaction to the content provided by the content selection unit. For example, it uses cameras and sensors to analyze the child's facial expressions, gaze, and movements to determine the child's level of interest. Step 4: The dynamic adjustment unit dynamically adjusts the content based on the child's reaction analyzed by the reaction analysis unit. For example, if the child is not interested in a particular video, the dynamic adjustment unit selects and provides another video.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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, in order to avoid confusion and to 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a pre-setting unit for selecting content based on parental pre-settings; a content selection unit that provides content selected by the presetting unit; a reaction analysis unit that analyzes children's reactions to the content provided by the content selection unit; a dynamic adjustment unit that dynamically adjusts content based on the child's reaction analyzed by the reaction analysis unit. A system characterized by:

2. The presetting unit Add a feature that allows parents to intuitively set standards using voice input 2. The system of claim 1.

3. The content selection unit Provide the content at an appropriate level of difficulty, taking into consideration the child's learning progress and level of understanding.

2. The system of claim 1.

4. The reaction analysis unit When analyzing the child's response, not only facial expressions but also the tone of voice are analyzed.

2. The system of claim 1.

5. The dynamic adjustment unit When dynamically adjusting the content, the physical condition and mood of the child are detected by a sensor, and the content is provided in accordance with the detected physical condition and mood.

2. The system of claim 1.

6. The presetting unit Analyze parents' emotions when setting up and provide support to reduce stress 2. The system of claim 1.

Images