system

Abstract

An object of a system according to an embodiment is to convert the expression of content into an expression suitable for the development level of a child and distribute the converted content.SOLUTION: A system includes a content analysis unit, a conversion unit, and a distribution unit. The content analysis unit analyzes the content on the VOOM. The converting unit converts the content analyzed by the content analyzing unit into an expression corresponding to the development level of the child. The distribution unit distributes the content converted by the conversion 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] With conventional technology, content presentation may not be appropriate for children's developmental level, leaving room for improvement.

[0005] The system according to the embodiment aims to convert the expression of content into one suitable for the developmental level of children and deliver the content. [Means for solving the problem]

[0006] The system according to the embodiment includes a content analysis unit, a conversion unit, and a distribution unit. The content analysis unit analyzes content on VOOM. The conversion unit converts the content analyzed by the content analysis unit into an expression appropriate for the child's developmental level. The distribution unit distributes the content converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert the expression of content into one suitable for the developmental level of the child and deliver the content. [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) The content conversion system according to an embodiment of the present invention converts and distributes all content on VOOM in a way that matches the developmental level of children, allowing children to enjoy safe and appropriate content.

[0029] A content conversion system according to an embodiment includes a content analysis unit, a conversion unit, and a distribution unit. The content analysis unit analyzes content on VOOM. For example, the content may include videos, text, and images, and detect expressions inappropriate for children or words difficult to understand. The content analysis unit uses a generation AI to perform analysis based on prompts that instruct content analysis and conversion. The conversion unit converts the content analyzed by the content analysis unit into expressions appropriate for the child's developmental level. For example, the conversion unit replaces difficult words with simpler words or provides easy-to-understand explanations of complex content. The conversion unit uses the generation AI to generate optimal expressions based on the child's age and comprehension level. The distribution unit distributes the content converted by the conversion unit. For example, the conversion unit converts the audio and subtitles of the converted video into expressions appropriate for children and distributes them. The distribution unit also provides the converted text and images in a format that is easy for children to understand. This enables the content conversion system according to an embodiment to provide appropriate content appropriate for the child's developmental level.

[0030] The content analysis unit can also analyze background sounds or music and convert it into an audio environment suitable for children. The content analysis unit, for example, analyzes the background sounds or music of a video and converts it into an audio environment suitable for children. For example, overly stimulating music is changed to a calm melody. The content analysis unit also uses voice recognition technology to analyze the audio of a video and converts it into an audio environment suitable for children. For example, the tone and speed of the audio are adjusted to convert it into an audio that is easy for children to hear. The content analysis unit also analyzes environmental sounds and converts it into an audio environment suitable for children. For example, excessively noisy environmental sounds are changed to quiet environmental sounds. This makes it possible to provide an audio environment suitable for children.

[0031] The content analysis unit can also analyze visual elements and convert them into a form that is visually easier for children to understand. For example, the content analysis unit can analyze the colors and design of a video and convert them into a form that is visually easier for children to understand. For example, it can change them to bright colors or a simple design. The content analysis unit can also analyze the content of an image and convert them into a form that is visually easier for children to understand. For example, it can add explanations to make it easier for children to understand. The content analysis unit can also analyze the layout of text and convert them into a form that is visually easier for children to understand. For example, it can change them to larger letters or a simple layout. This makes it possible to provide content that is visually easier for children to understand.

[0032] The conversion unit can customize content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize content for children of different age groups. For example, it converts content into simple language for young children and slightly more complex language for elementary school students. The conversion unit also customizes content for children with special needs. For example, it converts content into easy-to-understand language for children with learning disabilities. The conversion unit also customizes content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide content that is suitable for different age groups or children with special needs.

[0033] The conversion unit can feed back the results of the content analysis to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the content analysis to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0034] The conversion unit can add educational elements when generating expressions appropriate for a child's developmental level to enhance learning outcomes. For example, the conversion unit uses a generative AI to add educational elements when generating expressions appropriate for a child's developmental level. For example, it provides content that is aligned with learning goals. The conversion unit also adds educational content when generating expressions appropriate for a child's developmental level. For example, it incorporates scientific knowledge or historical facts. The conversion unit also suggests educational activities when generating expressions appropriate for a child's developmental level. For example, it provides educational games or quizzes. This makes it possible to provide expressions that enhance children's learning outcomes.

[0035] The conversion unit can add interactive elements when generating expressions appropriate for a child's developmental level, allowing the child to actively participate. The conversion unit adds interactive elements when generating expressions appropriate for a child's developmental level, for example, using a generative AI. For example, options are presented, and the story progresses depending on the choice made by the child. The conversion unit also provides interactive content to allow the child to actively participate. For example, learning is promoted through quizzes and games. The conversion unit also adds interactive elements when generating expressions appropriate for a child's developmental level. For example, the story progresses as the child answers questions. This makes it possible to provide interactive expressions in which the child can actively participate.

[0036] The conversion unit can adapt expressions appropriate for a child's developmental level to different cultures and languages. For example, the conversion unit uses generative AI to adapt expressions appropriate for a child's developmental level to different cultures. For example, it provides culturally appropriate characters and stories. The conversion unit also adapts expressions appropriate for a child's developmental level to different languages. For example, it provides content in multiple languages, such as English and Spanish. The conversion unit also refers to cultural background information and language models to generate expressions appropriate for different cultures and languages. For example, it generates expressions based on related literature and data. This makes it possible to provide expressions appropriate for different cultures and languages.

[0037] The content analysis unit can analyze the content of the video and add characters or storylines that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of the video and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the storyline of the video and add storylines that are likely to interest children. For example, it can add adventure stories or stories of friendship. The content analysis unit can also analyze the content of the video and incorporate elements that are likely to interest children. For example, it can add fun scenes or interesting characters. This makes it possible to provide characters and storylines that are likely to interest children.

[0038] The content analysis unit analyzes the content of the video and adds educational elements to enhance learning outcomes. The content analysis unit, for example, uses generative AI to analyze the content of the video and add educational elements. For example, it could include scientific knowledge or historical facts. The content analysis unit also analyzes the content of the video and adds educational content that aligns with learning goals. For example, it could provide content that includes math problems or English vocabulary. The content analysis unit also analyzes the content of the video and suggests educational activities. For example, it could provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0039] The conversion unit can customize video content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize video content for children of different age groups. For example, it converts the expression into simple language for young children and into slightly more complex language for elementary school students. The conversion unit also customizes video content for children with special needs. For example, it converts the expression into easy-to-understand language for children with learning disabilities. The conversion unit also customizes video content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide video content that is suitable for different age groups and children with special needs.

[0040] The conversion unit can feed back the results of the analysis of the video content to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the video content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the analysis of the video content to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0041] The content analysis unit can analyze the content of the text and add stories and characters that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of the text and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the storyline of the text and add storylines that are likely to interest children. For example, it can add adventure stories or stories of friendship. The content analysis unit can also analyze the content of the text and incorporate elements that are likely to interest children. For example, it can add fun scenes and interesting characters. This makes it possible to provide stories and characters that are likely to interest children.

[0042] The content analysis unit analyzes the content of the text and adds educational elements to enhance learning outcomes. For example, the content analysis unit uses generative AI to analyze the content of the text and add educational elements. For example, it can include scientific knowledge or historical facts. The content analysis unit also analyzes the content of the text and adds educational content that aligns with learning goals. For example, it can provide content that includes math problems or English vocabulary. The content analysis unit also analyzes the content of the text and suggests educational activities. For example, it can provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0043] The conversion unit can customize the text content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize the text content for children of different age groups. For example, it converts the text content into simple expressions for young children and slightly more complex expressions for elementary school students. The conversion unit also customizes the text content for children with special needs. For example, it converts the text content into easy-to-understand expressions for children with learning disabilities. The conversion unit also customizes the text content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide text content that is suitable for different age groups or children with special needs.

[0044] The conversion unit can feed back the results of the analysis of the text content to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the text content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding reading time and content. The conversion unit also feeds back the results of the analysis of the text content to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0045] The content analysis unit can analyze the content of an image and add characters or designs that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of an image and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the design of an image and add designs that are likely to interest children. For example, it can change the design to bright colors or a simple design. The content analysis unit can also analyze the content of an image and incorporate elements that are likely to interest children. For example, it can add fun scenes or interesting characters. This makes it possible to provide characters and designs that are likely to interest children.

[0046] The content analysis unit can analyze the content of an image and add educational elements to enhance learning outcomes. The content analysis unit, for example, uses generative AI to analyze the content of an image and add educational elements. For example, it can include scientific knowledge or historical facts. The content analysis unit can also analyze the content of an image and add educational content that aligns with learning goals. For example, it can provide content that includes math problems or English vocabulary. The content analysis unit can also analyze the content of an image and suggest educational activities. For example, it can provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0047] The conversion unit can customize image content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize image content for children of different age groups. For example, it converts the design into a simple one for toddlers and a slightly more complex one for elementary school children. The conversion unit also customizes image content for children with special needs. For example, it converts the design into one that is easy to understand for children with learning disabilities. The conversion unit also customizes image content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide image content that is suitable for different age groups or children with special needs.

[0048] The conversion unit can feed back the results of the image content analysis to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the image content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the image content analysis to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The content analysis unit can analyze the theme and genre of the content to attract children's interest and convert it into a theme that children like. For example, if a child is interested in animals, it can convert it into animal-related content. The content analysis unit can also analyze the storyline of the content to attract children's interest and convert it into a story that children like, such as adventure or fantasy. Furthermore, the content analysis unit can analyze the character settings to attract children's interest and change them into characters that children can easily relate to. This allows children to enjoy the content with greater interest.

[0051] The conversion unit can analyze the visual elements of the content and customize it to make it more visually enjoyable for children. For example, it can change the colors and designs to bright colors and simple designs that children prefer. The conversion unit can also change the character designs to ones that children can easily relate to. Furthermore, the conversion unit can change the background and scene designs to themes that children are more likely to be interested in. This makes it possible to provide content that children can enjoy visually.

[0052] Based on the analysis of the content, the converter can suggest activities that are beneficial to a child's development. For example, it can suggest crafts or experiments related to the content they have viewed. The converter can also suggest books or educational materials related to the content they have viewed to pique their child's interest. The converter can also suggest online resources or apps that are beneficial to a child's development. This allows children to further deepen their learning through the content they have viewed.

[0053] The converter can provide feedback that is useful for a child's development based on the results of content analysis. For example, it can evaluate a child's comprehension of the content they have viewed and suggest the next content to view. The converter can also track a child's learning progress and provide progress reports to parents and educators. Furthermore, the converter can suggest appropriate learning resources and activities based on the child's interests. This can improve a child's learning effectiveness.

[0054] Based on the content analysis results, the converter can provide a customized learning plan that contributes to a child's development. For example, it can create a plan tailored to a child's learning goals based on the content they have viewed. The converter can also suggest appropriate learning activities and resources based on the child's interests. Furthermore, the converter can track a child's learning progress and adjust the plan as needed. This maximizes the child's learning effectiveness.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The content analysis unit analyzes content on VOOM. For example, it analyzes content such as videos, text, and images to detect inappropriate expressions or difficult words for children. The content analysis unit also uses generative AI to analyze and convert content based on prompts. Step 2: The conversion unit converts the content analyzed by the content analysis unit into expressions appropriate for the child's developmental level. For example, it replaces difficult words with simpler ones and explains complex content in an easy-to-understand manner. The conversion unit also uses generative AI to generate optimal expressions based on the child's age and level of understanding. Step 3: The distribution unit distributes the content converted by the conversion unit. For example, the distribution unit converts the audio and subtitles of the converted video into expressions suitable for children and distributes them. The distribution unit also provides the converted text and images in a format that is easy for children to understand.

[0057] (Example 2) The content conversion system according to an embodiment of the present invention converts and distributes all content on VOOM in a way that matches the developmental level of children, allowing children to enjoy safe and appropriate content.

[0058] A content conversion system according to an embodiment includes a content analysis unit, a conversion unit, and a distribution unit. The content analysis unit analyzes content on VOOM. For example, the content may include videos, text, and images, and detect expressions inappropriate for children or words difficult to understand. The content analysis unit uses a generation AI to perform analysis based on prompts that instruct content analysis and conversion. The conversion unit converts the content analyzed by the content analysis unit into expressions appropriate for the child's developmental level. For example, the conversion unit replaces difficult words with simpler words or provides easy-to-understand explanations of complex content. The conversion unit uses the generation AI to generate optimal expressions based on the child's age and comprehension level. The distribution unit distributes the content converted by the conversion unit. For example, the conversion unit converts the audio and subtitles of the converted video into expressions appropriate for children and distributes them. The distribution unit also provides the converted text and images in a format that is easy for children to understand. This enables the content conversion system according to an embodiment to provide appropriate content appropriate for the child's developmental level.

[0059] The content analysis unit can add an emotion estimation function and convert the content into expressions that elicit positive emotions in children. The content analysis unit, for example, uses generative AI to analyze the audio and subtitles of videos and add the emotion estimation function. For example, the tone of voice and expressions can be adjusted to elicit positive emotions when children watch. The content analysis unit can also analyze the content of text and convert it into expressions that elicit positive emotions using the emotion estimation function. For example, expressions can be adjusted to elicit positive emotions when children read. The content analysis unit can also analyze the content of images and convert it into expressions that elicit positive emotions using the emotion estimation function. For example, colors and designs can be adjusted to elicit positive emotions when children visually view them. This makes it possible to provide content that elicits positive emotions in children.

[0060] The content analysis unit can also analyze background sounds or music and convert it into an audio environment suitable for children. The content analysis unit, for example, analyzes the background sounds or music of a video and converts it into an audio environment suitable for children. For example, overly stimulating music is changed to a calm melody. The content analysis unit also uses voice recognition technology to analyze the audio of a video and converts it into an audio environment suitable for children. For example, the tone and speed of the audio are adjusted to convert it into an audio that is easy for children to hear. The content analysis unit also analyzes environmental sounds and converts it into an audio environment suitable for children. For example, excessively noisy environmental sounds are changed to quiet environmental sounds. This makes it possible to provide an audio environment suitable for children.

[0061] The content analysis unit can also analyze visual elements and convert them into a form that is visually easier for children to understand. For example, the content analysis unit can analyze the colors and design of a video and convert them into a form that is visually easier for children to understand. For example, it can change them to bright colors or a simple design. The content analysis unit can also analyze the content of an image and convert them into a form that is visually easier for children to understand. For example, it can add explanations to make it easier for children to understand. The content analysis unit can also analyze the layout of text and convert them into a form that is visually easier for children to understand. For example, it can change them to larger letters or a simple layout. This makes it possible to provide content that is visually easier for children to understand.

[0062] The conversion unit can customize content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize content for children of different age groups. For example, it converts content into simple language for young children and slightly more complex language for elementary school students. The conversion unit also customizes content for children with special needs. For example, it converts content into easy-to-understand language for children with learning disabilities. The conversion unit also customizes content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide content that is suitable for different age groups or children with special needs.

[0063] The conversion unit can feed back the results of the content analysis to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the content analysis to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0064] The conversion unit uses the emotion estimation function to analyze a child's real-time emotional response while watching content, and can convert the content at an appropriate timing. For example, the conversion unit uses the emotion estimation function to analyze a child's facial expressions and voice while watching content, and understands the emotional response in real time. For example, if the child is bored, the conversion unit changes the content to make it more interesting. The conversion unit also collects the child's heart rate and electrodermal activity using a sensor, and analyzes the emotion in real time using an emotion estimation algorithm. For example, the conversion unit calculates an emotion score based on heart rate fluctuations and provides feedback. The conversion unit also dynamically adjusts the content and expression of the content based on the child's emotional response. For example, if the child is excited, the conversion unit changes the content to calming content. In this way, the content can be dynamically converted based on the child's real-time emotional response.

[0065] The conversion unit can prioritize expressions that elicit positive emotions using an emotion estimation function when generating expressions appropriate for a child's developmental level. For example, when using a generation AI to generate expressions appropriate for a child's developmental level, the conversion unit prioritizes expressions that elicit positive emotions using the emotion estimation function. For example, encouraging words and positive messages are added. The conversion unit also analyzes the child's emotional response and selects expressions that elicit positive emotions. For example, expressions that make the child feel joy are prioritized. The conversion unit also adds elements that elicit positive emotions when generating expressions appropriate for a child's developmental level. For example, fun images and bright colors are used. This makes it possible to provide children with expressions that elicit positive emotions.

[0066] The conversion unit can add educational elements when generating expressions appropriate for a child's developmental level to enhance learning outcomes. For example, the conversion unit uses a generative AI to add educational elements when generating expressions appropriate for a child's developmental level. For example, it provides content that is aligned with learning goals. The conversion unit also adds educational content when generating expressions appropriate for a child's developmental level. For example, it incorporates scientific knowledge or historical facts. The conversion unit also suggests educational activities when generating expressions appropriate for a child's developmental level. For example, it provides educational games or quizzes. This makes it possible to provide expressions that enhance children's learning outcomes.

[0067] The conversion unit can add interactive elements when generating expressions appropriate for a child's developmental level, allowing the child to actively participate. The conversion unit adds interactive elements when generating expressions appropriate for a child's developmental level, for example, using a generative AI. For example, options are presented, and the story progresses depending on the choice made by the child. The conversion unit also provides interactive content to allow the child to actively participate. For example, learning is promoted through quizzes and games. The conversion unit also adds interactive elements when generating expressions appropriate for a child's developmental level. For example, the story progresses as the child answers questions. This makes it possible to provide interactive expressions in which the child can actively participate.

[0068] The conversion unit can adapt expressions appropriate for a child's developmental level to different cultures and languages. For example, the conversion unit uses generative AI to adapt expressions appropriate for a child's developmental level to different cultures. For example, it provides culturally appropriate characters and stories. The conversion unit also adapts expressions appropriate for a child's developmental level to different languages. For example, it provides content in multiple languages, such as English and Spanish. The conversion unit also refers to cultural background information and language models to generate expressions appropriate for different cultures and languages. For example, it generates expressions based on related literature and data. This makes it possible to provide expressions appropriate for different cultures and languages.

[0069] The conversion unit can use the emotion estimation function to dynamically adjust the expression based on the child's real-time emotional reaction. The conversion unit, for example, uses the emotion estimation function to analyze the child's real-time emotional reaction and dynamically adjust the expression. For example, if the child is excited, the content is changed to something calming. The conversion unit also adjusts the content and expression of the content in real time based on the child's emotional reaction. For example, if the child is bored, the content is changed to something that attracts the child's interest. The conversion unit also analyzes the child's emotional reaction and changes the content at an appropriate time. For example, the content is changed when the change in emotion exceeds a certain threshold. This makes it possible to dynamically adjust the expression based on the child's real-time emotional reaction.

[0070] The content analysis unit can analyze the content of the video and add characters or storylines that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of the video and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the storyline of the video and add storylines that are likely to interest children. For example, it can add adventure stories or stories of friendship. The content analysis unit can also analyze the content of the video and incorporate elements that are likely to interest children. For example, it can add fun scenes or interesting characters. This makes it possible to provide characters and storylines that are likely to interest children.

[0071] The content analysis unit analyzes the content of the video and adds educational elements to enhance learning outcomes. The content analysis unit, for example, uses generative AI to analyze the content of the video and add educational elements. For example, it could include scientific knowledge or historical facts. The content analysis unit also analyzes the content of the video and adds educational content that aligns with learning goals. For example, it could provide content that includes math problems or English vocabulary. The content analysis unit also analyzes the content of the video and suggests educational activities. For example, it could provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0072] The conversion unit can customize video content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize video content for children of different age groups. For example, it converts the expression into simple language for young children and into slightly more complex language for elementary school students. The conversion unit also customizes video content for children with special needs. For example, it converts the expression into easy-to-understand language for children with learning disabilities. The conversion unit also customizes video content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide video content that is suitable for different age groups and children with special needs.

[0073] The conversion unit can feed back the results of the analysis of the video content to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the video content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the analysis of the video content to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0074] The conversion unit uses the emotion estimation function to analyze a child's real-time emotional response while watching a video and can convert the video at an appropriate timing. For example, the conversion unit uses the emotion estimation function to analyze a child's facial expressions and voice while watching a video and understands the child's emotional response in real time. For example, if the child is bored, the conversion unit changes the content to make it more interesting. The conversion unit also collects the child's heart rate and electrodermal activity using a sensor and analyzes the child's emotion in real time using an emotion estimation algorithm. For example, the conversion unit calculates an emotion score based on heart rate fluctuations and provides feedback. The conversion unit also dynamically adjusts the content and expression of the video based on the child's emotional response. For example, if the child is excited, the conversion unit changes the content to calm them down. In this way, the video can be dynamically converted based on the child's real-time emotional response.

[0075] The content analysis unit can analyze the content of the text and convert it into expressions that elicit positive emotions using an emotion estimation function. The content analysis unit, for example, uses generative AI to analyze the content of the text and add an emotion estimation function. For example, the expression is adjusted to elicit positive emotions when a child is reading. The content analysis unit also analyzes the content of the text and adds elements that elicit positive emotions. For example, it adds words of encouragement or positive messages. The content analysis unit also analyzes the content of the text and selects expressions that elicit positive emotions. For example, it prioritizes expressions that make children feel joy. This makes it possible to provide text expressions that elicit positive emotions in children.

[0076] The content analysis unit can analyze the content of the text and add stories and characters that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of the text and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the storyline of the text and add storylines that are likely to interest children. For example, it can add adventure stories or stories of friendship. The content analysis unit can also analyze the content of the text and incorporate elements that are likely to interest children. For example, it can add fun scenes and interesting characters. This makes it possible to provide stories and characters that are likely to interest children.

[0077] The content analysis unit analyzes the content of the text and adds educational elements to enhance learning outcomes. For example, the content analysis unit uses generative AI to analyze the content of the text and add educational elements. For example, it can include scientific knowledge or historical facts. The content analysis unit also analyzes the content of the text and adds educational content that aligns with learning goals. For example, it can provide content that includes math problems or English vocabulary. The content analysis unit also analyzes the content of the text and suggests educational activities. For example, it can provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0078] The conversion unit can customize the text content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize the text content for children of different age groups. For example, it converts the text content into simple expressions for young children and slightly more complex expressions for elementary school students. The conversion unit also customizes the text content for children with special needs. For example, it converts the text content into easy-to-understand expressions for children with learning disabilities. The conversion unit also customizes the text content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide text content that is suitable for different age groups or children with special needs.

[0079] The conversion unit can feed back the results of the analysis of the text content to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the text content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding reading time and content. The conversion unit also feeds back the results of the analysis of the text content to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0080] The content analysis unit can analyze the content of an image and convert it into an expression that elicits positive emotions using an emotion estimation function. The content analysis unit, for example, uses generative AI to analyze the content of an image and add an emotion estimation function. For example, it adjusts the color and design to elicit positive emotions when children visually view it. The content analysis unit also analyzes the content of the image and adds elements that elicit positive emotions. For example, it adds bright colors and images of smiling faces. The content analysis unit also analyzes the content of the image and selects expressions that elicit positive emotions. For example, it prioritizes expressions that make children feel joy. This makes it possible to provide image expressions that elicit positive emotions in children.

[0081] The content analysis unit can analyze the content of an image and add characters or designs that are likely to interest children. The content analysis unit can, for example, use generative AI to analyze the content of an image and add characters that are likely to interest children. For example, it can introduce animal characters or hero characters. The content analysis unit can also analyze the design of an image and add designs that are likely to interest children. For example, it can change the design to bright colors or a simple design. The content analysis unit can also analyze the content of an image and incorporate elements that are likely to interest children. For example, it can add fun scenes or interesting characters. This makes it possible to provide characters and designs that are likely to interest children.

[0082] The content analysis unit can analyze the content of an image and add educational elements to enhance learning outcomes. The content analysis unit, for example, uses generative AI to analyze the content of an image and add educational elements. For example, it can include scientific knowledge or historical facts. The content analysis unit can also analyze the content of an image and add educational content that aligns with learning goals. For example, it can provide content that includes math problems or English vocabulary. The content analysis unit can also analyze the content of an image and suggest educational activities. For example, it can provide learning games or quizzes. This makes it possible to provide educational elements that enhance children's learning outcomes.

[0083] The conversion unit can customize image content for different age groups or children with special needs. The conversion unit, for example, uses generative AI to customize image content for children of different age groups. For example, it converts the design into a simple one for toddlers and a slightly more complex one for elementary school children. The conversion unit also customizes image content for children with special needs. For example, it converts the design into one that is easy to understand for children with learning disabilities. The conversion unit also customizes image content for children with visual impairments. For example, it adds audio guides. This makes it possible to provide image content that is suitable for different age groups or children with special needs.

[0084] The conversion unit can feed back the results of the image content analysis to parents or educators and provide advice that is useful for children's development. For example, the conversion unit can feed back the results of the image content analyzed by the generative AI to parents and provide advice that is useful for children's development. For example, it can present recommendations regarding viewing time and content. The conversion unit also feeds back the results of the image content analysis to educators and provide educational advice. For example, it can provide advice regarding learning progress and areas for improvement. The conversion unit can also suggest activities and resources that are useful for children's development to parents and educators. For example, it can introduce appropriate learning materials and activities. This makes it possible to provide parents and educators with advice that is useful for children's development.

[0085] The conversion unit uses the emotion estimation function to analyze a child's real-time emotional response while viewing an image, and can convert the image at an appropriate timing. For example, the conversion unit uses the emotion estimation function to analyze a child's facial expression and voice while viewing an image, and understands the emotional response in real time. For example, if the child is bored, the conversion unit changes the content to make it more interesting. The conversion unit also collects the child's heart rate and electrodermal activity using a sensor, and analyzes the emotion in real time using an emotion estimation algorithm. For example, the conversion unit calculates an emotion score based on heart rate fluctuations and provides feedback. The conversion unit also dynamically adjusts the content and expression of the image based on the child's emotional response. For example, if the child is excited, the conversion unit changes the content to calm them down. In this way, images can be dynamically converted based on a child's real-time emotional response.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The content analysis unit can analyze the theme and genre of the content to attract children's interest and convert it into a theme that children like. For example, if a child is interested in animals, it can convert it into animal-related content. The content analysis unit can also analyze the storyline of the content to attract children's interest and convert it into a story that children like, such as adventure or fantasy. Furthermore, the content analysis unit can analyze the character settings to attract children's interest and change them into characters that children can easily relate to. This allows children to enjoy the content with greater interest.

[0088] The conversion unit can use its emotion estimation function to analyze the emotions of children while they are watching content and add interactive elements to elicit positive emotions. For example, if a child smiles, it can add more fun scenes. If a child is excited, the conversion unit can add interactive elements such as quizzes and games to keep the child interested. If a child is moved, the conversion unit can add inspiring messages and scenes to reinforce the positive emotions. This allows children to participate more actively in the content and elicit positive emotions.

[0089] The conversion unit can analyze the visual elements of the content and customize it to make it more visually enjoyable for children. For example, it can change the colors and designs to bright colors and simple designs that children prefer. The conversion unit can also change the character designs to ones that children can easily relate to. Furthermore, the conversion unit can change the background and scene designs to themes that children are more likely to be interested in. This makes it possible to provide content that children can enjoy visually.

[0090] The conversion unit can use the emotion estimation function to analyze the emotions of children when they are watching content and add audio guidance to elicit positive emotions. For example, if a child smiles, it can add cheerful music or sound effects. If a child is excited, the conversion unit can add encouraging words or positive messages as audio guidance. If a child is moved, the conversion unit can add inspiring music or narration to reinforce the positive emotions. This makes it possible to provide content that is more emotionally satisfying to children.

[0091] Based on the analysis of the content, the converter can suggest activities that are beneficial to a child's development. For example, it can suggest crafts or experiments related to the content they have viewed. The converter can also suggest books or educational materials related to the content they have viewed to pique their child's interest. The converter can also suggest online resources or apps that are beneficial to a child's development. This allows children to further deepen their learning through the content they have viewed.

[0092] The conversion unit can use the emotion estimation function to analyze the emotions of children when they are watching content and add visual effects to elicit positive emotions. For example, if a child smiles, fun animations and effects can be added. If a child is excited, the conversion unit can add colorful effects and moving visuals to keep the child interested. If a child is moved, the conversion unit can add moving visual effects to reinforce the positive emotions. This makes it possible to provide content that children can enjoy visually.

[0093] The converter can provide feedback that is useful for a child's development based on the results of content analysis. For example, it can evaluate a child's comprehension of the content they have viewed and suggest the next content to view. The converter can also track a child's learning progress and provide progress reports to parents and educators. Furthermore, the converter can suggest appropriate learning resources and activities based on the child's interests. This can improve a child's learning effectiveness.

[0094] The conversion unit can use the emotion estimation function to analyze the emotions of children when they are watching content and add story developments that elicit positive emotions. For example, if a child smiles, it can add fun episodes and scenes. If a child is excited, the conversion unit can add adventure and action scenes to keep the child interested. Furthermore, if a child is moved, the conversion unit can add an inspiring story development to reinforce the positive emotions. This makes it possible to provide content that is more emotionally satisfying to children.

[0095] Based on the content analysis results, the converter can provide a customized learning plan that contributes to a child's development. For example, it can create a plan tailored to a child's learning goals based on the content they have viewed. The converter can also suggest appropriate learning activities and resources based on the child's interests. Furthermore, the converter can track a child's learning progress and adjust the plan as needed. This maximizes the child's learning effectiveness.

[0096] The conversion unit can use the emotion estimation function to analyze the emotions of children when they are watching content and add character interactions to elicit positive emotions. For example, if a child smiles, a scene in which a character talks to the child is added. If the child is excited, the conversion unit can add a scene in which a character goes on an adventure with the child to keep the child interested. If the child is moved, the conversion unit can add a scene in which a character conveys an inspiring message to reinforce the positive emotions. This makes it possible to provide content that is more emotionally satisfying to children.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The content analysis unit analyzes content on VOOM. For example, it analyzes content such as videos, text, and images to detect inappropriate expressions or difficult words for children. The content analysis unit also uses generative AI to analyze and convert content based on prompts. Step 2: The conversion unit converts the content analyzed by the content analysis unit into expressions appropriate for the child's developmental level. For example, it replaces difficult words with simpler ones and explains complex content in an easy-to-understand manner. The conversion unit also uses generative AI to generate optimal expressions based on the child's age and level of understanding. Step 3: The distribution unit distributes the content converted by the conversion unit. For example, the distribution unit converts the audio and subtitles of the converted video into expressions suitable for children and distributes them. The distribution unit also provides the converted text and images in a format that is easy for children to understand.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

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

[0112] 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.

[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 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.

[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 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[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 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.

[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 (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).

[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] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 content analysis unit that analyzes content on VOOM, a conversion unit that converts the content analyzed by the content analysis unit into an expression appropriate for the child's developmental level; a distribution unit that distributes the content converted by the conversion unit. A system characterized by:

2. The content analysis unit Adds emotion estimation functionality when analyzing content, converting it into expressions that elicit positive emotions in children The system of claim 1 .

3. The conversion unit Customize content for different age groups or children with special needs The system of claim 1 .

4. The conversion unit Adapting expressions appropriate to the child's developmental level to different cultures and languages The system of claim 1 .

5. The conversion unit Customize video content for different age groups or children with special needs The system of claim 1 .

6. The conversion unit Using emotion estimation, the system analyzes children's real-time emotional responses while they are reading text and converts the text at the appropriate time. The system of claim 1 .

7. The content analysis unit Analyzes the content of an image and converts it into expressions that evoke positive emotions using emotion estimation functions The system of claim 1 .

8. The conversion unit Using emotion estimation, the system analyzes a child's real-time emotional response while viewing an image and changes the image at the appropriate time. The system of claim 1 .

Images