System

Abstract

An object of the system according to the embodiment is to design and manufacture an intellectual training toy based on the age and tendency of a baby or child.SOLUTION: A system according to an embodiment includes an age trend analysis unit, a design generation unit, and a manufacturing unit. The age trend analysis unit analyzes the ages and trends of babies and children. The design generation unit generates a design of the intellectual training toy based on the data analyzed by the age trend analysis unit. The manufacturing section manufactures the intellectual training toy based on the design generated by the design generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately design educational toys based on the age and tendencies of babies and children, and there is room for improvement.

[0005] The system according to the embodiment aims to design and manufacture educational toys based on the age and tendencies of babies and children. [Means for solving the problem]

[0006] The system according to the embodiment includes an age trend analysis unit, a design generation unit, and a manufacturing unit. The age trend analysis unit analyzes the ages and trends of babies and children. The design generation unit generates designs for educational toys based on the data analyzed by the age trend analysis unit. The manufacturing unit manufactures educational toys based on the designs generated by the design generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can design and manufacture educational toys based on the age and tendencies of babies and children. [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 educational toy manufacturing system according to an embodiment of the present invention analyzes the age and tendencies of babies and children, and based on the results, uses a 3D printer to manufacture educational toys that the child is likely to be interested in. This allows the educational toy manufacturing system to quickly provide original educational toys tailored to the child's interests, thereby enhancing the educational effect.

[0029] An educational toy manufacturing system according to an embodiment includes an age tendency analysis unit, a design generation unit, and a manufacturing unit. The age tendency analysis unit analyzes the ages and tendencies of babies and children. For example, information provided by parents and behavioral data of children are collected and analyzed by AI. The AI ​​analyzes, for example, what types of play a child prefers and what colors and shapes they are interested in. The design generation unit generates a design for an educational toy based on the data analyzed by the age tendency analysis unit. For example, if a child is interested in animals, the design generation unit generates a design for an educational toy shaped like an animal. Also, if a child likes a particular color, the design generation unit generates a design incorporating that color. The manufacturing unit manufactures educational toys based on the design generated by the design generation unit. For example, the design data generated by the AI ​​is input into a 3D printer, and educational toys are manufactured using materials such as plastic and resin. This allows the educational toy manufacturing system according to an embodiment to provide optimal educational toys based on the ages and tendencies of children.

[0030] The age trend analysis unit can perform video analysis of how children play, and perform a detailed analysis of movement patterns and frequency of play. For example, the age trend analysis unit records a child playing with a video camera, and the AI ​​analyzes the video. For example, it analyzes how often a particular toy is used. The age trend analysis unit also analyzes movement patterns to gain a detailed understanding of how children play. For example, it analyzes the type, frequency, and duration of movements, and performs a detailed analysis of how children play. This allows for a detailed analysis of how children play.

[0031] The age trend analysis unit can analyze the child's eating and sleeping patterns and suggest educational toys based on the child's overall health condition. The age trend analysis unit, for example, analyzes the child's eating patterns to understand the child's health condition. For example, it analyzes the frequency of meals and nutritional balance and suggests educational toys based on the results. The age trend analysis unit also analyzes the child's sleeping patterns to understand the child's health condition. For example, it analyzes the sleeping time and sleep quality and suggests educational toys based on the results. This makes it possible to suggest educational toys based on the child's health condition.

[0032] The age trend analysis unit can analyze parents' parenting styles and values ​​and customize educational toy suggestions based on that. For example, the age trend analysis unit collects information about parents' parenting styles through questionnaires and interviews, and AI analyzes the data. For example, it identifies the educational principles and values ​​that parents place importance on. The age trend analysis unit also analyzes parents' values ​​and customizes educational toy suggestions based on that information. For example, it can suggest educational toys based on the educational principles desired by parents. This makes it possible to customize educational toys based on parents' parenting styles and values.

[0033] The design generation unit can perform dynamic design generation that automatically and continuously updates the design in response to changes in a child's interests. For example, the design generation unit analyzes changes in a child's interests in real time and automatically updates the design of the educational toy accordingly. For example, if a child begins to become interested in a new animal, a design of that animal is added. The design generation unit also automatically and continuously updates the design in response to changes in a child's interests. For example, if a child begins to become interested in a particular color or shape, a design incorporating that color or shape is generated. This allows the design to be automatically updated in response to changes in a child's interests.

[0034] The design generation unit can incorporate elements that evolve in line with a child's growth into the design of the educational toy, allowing it to be used for a long period of time. The design generation unit, for example, incorporates elements that allow the design to evolve in line with a child's growth. For example, adding or rearranging parts provides new ways of playing as the child grows. The design generation unit also incorporates elements that evolve in the design of the educational toy, allowing it to be used for a long period of time. For example, functions can be added or the design can be changed according to the child's stage of growth. This allows the educational toy to be used for a long period of time as the child grows.

[0035] The design generation unit can incorporate educational elements into the design of the educational toy to enhance the learning effect. The design generation unit, for example, incorporates educational elements such as alphabets and numbers into the design of the educational toy. For example, the alphabet is printed on puzzle pieces. The design generation unit can also incorporate educational elements into the design of the educational toy to enhance the learning effect. For example, by incorporating learning content into the educational toy, it is possible to learn while playing. In this way, educational elements can be incorporated into the design of the educational toy to enhance the learning effect.

[0036] The design generation unit adds elements to the design of the educational toy that allow parents and children to work together, thereby deepening the bond between them. The design generation unit, for example, incorporates elements into the design that allow parents and children to work together. For example, it provides puzzles or blocks that parents and children can assemble together. The design generation unit also adds elements to the design of the educational toy that allow parents and children to work together, thereby deepening the bond between them. For example, parents and children can play together, thereby deepening the bond between them. In this way, elements that allow parents and children to work together can be added, thereby deepening the bond between them.

[0037] The manufacturing department can optimize the 3D printer manufacturing process and significantly reduce manufacturing time. For example, the manufacturing department can optimize the 3D printer settings to reduce manufacturing time. For example, they can adjust the print speed and layer height. The manufacturing department can also optimize the manufacturing process and significantly reduce manufacturing time. For example, they can use methods to shorten processes and increase efficiency. This allows the 3D printer manufacturing process to be optimized and significantly reduce manufacturing time.

[0038] The manufacturing department can incorporate sensors into educational toys manufactured with a 3D printer and add interactive functions. For example, the manufacturing department can incorporate touch sensors into educational toys manufactured with a 3D printer. For example, the manufacturing department can add a function that makes a sound when a child touches it. The manufacturing department can also incorporate sensors into educational toys and add interactive functions. For example, the manufacturing department can add a function that lights up or makes a sound in response to movement. This makes it possible to provide educational toys with incorporated sensors and added interactive functions.

[0039] The manufacturing department can add customizable parts to educational toys manufactured with a 3D printer, allowing children to assemble them themselves. For example, the manufacturing department can add assembleable parts to educational toys manufactured with a 3D printer. For example, the manufacturing department can provide blocks or puzzle pieces. The manufacturing department can also add customizable parts to educational toys, allowing children to assemble them themselves. For example, the manufacturing department can replace or rearrange parts so that children can assemble them themselves. In this way, it is possible to provide educational toys that can be assembled by adding customizable parts and that children can assemble themselves.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The educational toy manufacturing system further includes a voice recognition unit. The voice recognition unit can analyze words and sounds uttered by a child and generate an educational toy design based on the analysis. For example, if a child says "car," a car-shaped educational toy design is generated. The voice recognition unit can also analyze a child's pronunciation and word usage to suggest educational toys that support language development. This makes it possible to provide educational toys that promote children's language development.

[0042] The educational toy manufacturing system further includes an environment recognition unit. The environment recognition unit can analyze the environment in which a child is playing and suggest educational toys that are suitable for that environment. For example, if the child is playing outdoors, it can suggest educational toys made from durable materials. The environment recognition unit can also analyze the lighting and acoustic environment indoors and suggest educational toys that are suitable for that environment. This makes it possible to provide educational toys that are optimal for the environment in which a child is playing.

[0043] The educational toy manufacturing system further includes a feedback collection unit that collects parent feedback. The feedback collection unit collects parent opinions about the educational toy and can improve the design and functions of the educational toy based on that feedback. For example, if a parent provides an opinion such as "I want more colors," a design that reflects that opinion is generated. The feedback collection unit can also periodically collect parent feedback and continuously improve the educational toy. This makes it possible to provide educational toys that reflect parent opinions.

[0044] The educational toy manufacturing system further includes a health data collection unit that collects health data of the child. The health data collection unit collects health data such as the child's body temperature and heart rate, and can suggest educational toys based on that data. For example, if a child has a cold, it can suggest educational toys that allow the child to play quietly. The health data collection unit can also monitor the child's health condition and suggest educational toys that suit the child's health condition. This makes it possible to provide educational toys that are optimal for the child's health condition.

[0045] The educational toy manufacturing system further includes a communication promotion unit that promotes communication between parents and children. The communication promotion unit analyzes communication between parents and children when they play together and can suggest educational toys based on the analysis results. For example, it can suggest puzzles or blocks that parents and children can assemble together. The communication promotion unit can also suggest activities to promote communication between parents and children. This makes it possible to provide educational toys that deepen communication between parents and children.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The age trend analysis unit analyzes the age and trends of babies and children. For example, information provided by parents and behavioral data of children are collected and analyzed by AI. For example, the AI ​​analyzes what types of play a child prefers and what colors and shapes they are interested in. Step 2: The design generation unit generates a design for the educational toy based on the data analyzed by the age tendency analysis unit. For example, if a child is interested in animals, it generates an animal-shaped educational toy design. Also, if a child likes a particular color, it generates a design incorporating that color. Step 3: The manufacturing department manufactures the educational toy based on the design generated by the design generation department. For example, the design data generated by the AI ​​is input into a 3D printer, and the educational toy is manufactured using materials such as plastic and resin.

[0048] (Example 2) The educational toy manufacturing system according to an embodiment of the present invention analyzes the age and tendencies of babies and children, and based on the results, uses a 3D printer to manufacture educational toys that the child is likely to be interested in. This allows the educational toy manufacturing system to quickly provide original educational toys tailored to the child's interests, thereby enhancing the educational effect.

[0049] An educational toy manufacturing system according to an embodiment includes an age tendency analysis unit, a design generation unit, and a manufacturing unit. The age tendency analysis unit analyzes the ages and tendencies of babies and children. For example, information provided by parents and behavioral data of children are collected and analyzed by AI. The AI ​​analyzes, for example, what types of play a child prefers and what colors and shapes they are interested in. The design generation unit generates a design for an educational toy based on the data analyzed by the age tendency analysis unit. For example, if a child is interested in animals, the design generation unit generates a design for an educational toy shaped like an animal. Also, if a child likes a particular color, the design generation unit generates a design incorporating that color. The manufacturing unit manufactures educational toys based on the design generated by the design generation unit. For example, the design data generated by the AI ​​is input into a 3D printer, and educational toys are manufactured using materials such as plastic and resin. This allows the educational toy manufacturing system according to an embodiment to provide optimal educational toys based on the ages and tendencies of children.

[0050] The age trend analysis unit analyzes a child's facial expressions and tone of voice in real time, allowing changes in interests to be reflected immediately. For example, the age trend analysis unit captures a child's facial expressions while playing with a camera, and AI analyzes those expressions. For example, it detects smiling and surprised expressions and grasps changes in interests in real time. The age trend analysis unit also analyzes a child's tone of voice and reflects changes in interests immediately. For example, it analyzes the tone and speed of voice and grasps changes in interests in real time. This allows changes in a child's interests to be reflected immediately.

[0051] The age trend analysis unit can perform video analysis of how children play, and perform a detailed analysis of movement patterns and frequency of play. For example, the age trend analysis unit records a child playing with a video camera, and the AI ​​analyzes the video. For example, it analyzes how often a particular toy is used. The age trend analysis unit also analyzes movement patterns to gain a detailed understanding of how children play. For example, it analyzes the type, frequency, and duration of movements, and performs a detailed analysis of how children play. This allows for a detailed analysis of how children play.

[0052] The age tendency analysis unit uses the emotion estimation function to estimate the emotions of a child while playing, and can identify ways of playing that bring out positive emotions. The age tendency analysis unit, for example, analyzes the facial expressions of a child while playing, and identifies positive emotions using the emotion estimation function. For example, it detects smiling or excited facial expressions. The age tendency analysis unit also analyzes the tone of a child's voice to identify positive emotions. For example, it analyzes the tone and speed of the voice to identify positive emotions. This makes it possible to identify ways of playing that bring out positive emotions in a child.

[0053] The age trend analysis unit can analyze the child's eating and sleeping patterns and suggest educational toys based on the child's overall health condition. The age trend analysis unit, for example, analyzes the child's eating patterns to understand the child's health condition. For example, it analyzes the frequency of meals and nutritional balance and suggests educational toys based on the results. The age trend analysis unit also analyzes the child's sleeping patterns to understand the child's health condition. For example, it analyzes the sleeping time and sleep quality and suggests educational toys based on the results. This makes it possible to suggest educational toys based on the child's health condition.

[0054] The age trend analysis unit can analyze parents' parenting styles and values ​​and customize educational toy suggestions based on that. For example, the age trend analysis unit collects information about parents' parenting styles through questionnaires and interviews, and AI analyzes the data. For example, it identifies the educational principles and values ​​that parents place importance on. The age trend analysis unit also analyzes parents' values ​​and customizes educational toy suggestions based on that information. For example, it can suggest educational toys based on the educational principles desired by parents. This makes it possible to customize educational toys based on parents' parenting styles and values.

[0055] The age tendency analysis unit also analyzes the emotions of the parent using the emotion estimation function, and can suggest educational toys that can be enjoyed by both parents and children. The age tendency analysis unit, for example, analyzes the facial expressions of the parent while playing with the child, and identifies positive emotions using the emotion estimation function. For example, it detects smiling faces and happy facial expressions. The age tendency analysis unit also analyzes the tone of the parent's voice to identify positive emotions. For example, it analyzes the tone and speed of the voice to identify positive emotions. This makes it possible to suggest educational toys that can be enjoyed by both parents and children.

[0056] The design generation unit can perform dynamic design generation that automatically and continuously updates the design in response to changes in a child's interests. For example, the design generation unit analyzes changes in a child's interests in real time and automatically updates the design of the educational toy accordingly. For example, if a child begins to become interested in a new animal, a design of that animal is added. The design generation unit also automatically and continuously updates the design in response to changes in a child's interests. For example, if a child begins to become interested in a particular color or shape, a design incorporating that color or shape is generated. This allows the design to be automatically updated in response to changes in a child's interests.

[0057] The design generation unit can incorporate elements that evolve in line with a child's growth into the design of the educational toy, allowing it to be used for a long period of time. The design generation unit, for example, incorporates elements that allow the design to evolve in line with a child's growth. For example, adding or rearranging parts provides new ways of playing as the child grows. The design generation unit also incorporates elements that evolve in the design of the educational toy, allowing it to be used for a long period of time. For example, functions can be added or the design can be changed according to the child's stage of growth. This allows the educational toy to be used for a long period of time as the child grows.

[0058] The design generation unit can use the emotion estimation function to identify the design elements that children will enjoy most and generate a design that reflects them. The design generation unit, for example, analyzes the facial expressions of children while they are playing and uses the emotion estimation function to identify the design elements that children will enjoy most. For example, design elements that show a lot of smiling faces are reflected. The design generation unit can also analyze the tone of a child's voice and identify the design elements that children will enjoy most. For example, the tone and speed of the voice can be analyzed to identify the design elements that children will enjoy most. This makes it possible to identify the design elements that children will enjoy most and generate a design that reflects them.

[0059] The design generation unit can incorporate educational elements into the design of the educational toy to enhance the learning effect. The design generation unit, for example, incorporates educational elements such as alphabets and numbers into the design of the educational toy. For example, the alphabet is printed on puzzle pieces. The design generation unit can also incorporate educational elements into the design of the educational toy to enhance the learning effect. For example, by incorporating learning content into the educational toy, it is possible to learn while playing. In this way, educational elements can be incorporated into the design of the educational toy to enhance the learning effect.

[0060] The design generation unit adds elements to the design of the educational toy that allow parents and children to work together, thereby deepening the bond between them. The design generation unit, for example, incorporates elements into the design that allow parents and children to work together. For example, it provides puzzles or blocks that parents and children can assemble together. The design generation unit also adds elements to the design of the educational toy that allow parents and children to work together, thereby deepening the bond between them. For example, parents and children can play together, thereby deepening the bond between them. In this way, elements that allow parents and children to work together can be added, thereby deepening the bond between them.

[0061] The design generation unit uses the emotion estimation function to generate a design that also takes into account the parent's emotions, making it possible to provide an educational toy that can be enjoyed by both parents and children. The design generation unit, for example, analyzes the facial expression of a parent while playing with their child, and uses the emotion estimation function to generate a design that also takes into account the parent's emotions. For example, it reflects design elements that show the parent looking happy. The design generation unit also analyzes the tone of the parent's voice to generate a design that also takes into account the parent's emotions. For example, it analyzes the tone and speed of the voice to generate a design that also takes into account the parent's emotions. In this way, it is possible to generate a design that also takes into account the parent's emotions, making it possible to provide an educational toy that can be enjoyed by both parents and children.

[0062] The manufacturing department can optimize the 3D printer manufacturing process and significantly reduce manufacturing time. For example, the manufacturing department can optimize the 3D printer settings to reduce manufacturing time. For example, they can adjust the print speed and layer height. The manufacturing department can also optimize the manufacturing process and significantly reduce manufacturing time. For example, they can use methods to shorten processes and increase efficiency. This allows the 3D printer manufacturing process to be optimized and significantly reduce manufacturing time.

[0063] The manufacturing department can use the emotion estimation function to identify the materials and textures that children enjoy most and manufacture products that reflect those materials and textures. For example, the manufacturing department analyzes the facial expressions of children while they are playing and uses the emotion estimation function to identify the materials and textures that children enjoy most. For example, materials and textures that are associated with a lot of smiles are reflected. The manufacturing department can also analyze the tone of a child's voice to identify the materials and textures that children enjoy most. For example, the tone and speed of the voice can be analyzed to identify the materials and textures that children enjoy most. This allows the manufacturing department to identify the materials and textures that children enjoy most and manufacture products that reflect those materials and textures.

[0064] The manufacturing department can incorporate sensors into educational toys manufactured with a 3D printer and add interactive functions. For example, the manufacturing department can incorporate touch sensors into educational toys manufactured with a 3D printer. For example, the manufacturing department can add a function that makes a sound when a child touches it. The manufacturing department can also incorporate sensors into educational toys and add interactive functions. For example, the manufacturing department can add a function that lights up or makes a sound in response to movement. This makes it possible to provide educational toys with incorporated sensors and added interactive functions.

[0065] The manufacturing department can add customizable parts to educational toys manufactured with a 3D printer, allowing children to assemble them themselves. For example, the manufacturing department can add assembleable parts to educational toys manufactured with a 3D printer. For example, the manufacturing department can provide blocks or puzzle pieces. The manufacturing department can also add customizable parts to educational toys, allowing children to assemble them themselves. For example, the manufacturing department can replace or rearrange parts so that children can assemble them themselves. In this way, it is possible to provide educational toys that can be assembled by adding customizable parts and that children can assemble themselves.

[0066] The manufacturing department uses the emotion estimation function to manufacture products that also take into account the emotions of parents, making it possible to provide educational toys that can be enjoyed by both parents and children. For example, the manufacturing department analyzes the facial expressions of parents while playing with their children, and uses the emotion estimation function to manufacture products that also take into account the emotions of parents. For example, design elements that show the parent looking happy are incorporated. The manufacturing department also analyzes the tone of the parent's voice to manufacture products that also take into account the emotions of parents. For example, the tone and speed of the voice are analyzed to manufacture products that also take into account the emotions of parents. In this way, it is possible to manufacture products that also take into account the emotions of parents, making it possible to provide educational toys that can be enjoyed by both parents and children.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The educational toy manufacturing system further includes a voice recognition unit. The voice recognition unit can analyze words and sounds uttered by a child and generate an educational toy design based on the analysis. For example, if a child says "car," a car-shaped educational toy design is generated. The voice recognition unit can also analyze a child's pronunciation and word usage to suggest educational toys that support language development. This makes it possible to provide educational toys that promote children's language development.

[0069] The educational toy manufacturing system further includes an environment recognition unit. The environment recognition unit can analyze the environment in which a child is playing and suggest educational toys that are suitable for that environment. For example, if the child is playing outdoors, it can suggest educational toys made from durable materials. The environment recognition unit can also analyze the lighting and acoustic environment indoors and suggest educational toys that are suitable for that environment. This makes it possible to provide educational toys that are optimal for the environment in which a child is playing.

[0070] The educational toy manufacturing system further includes a feedback collection unit that collects parent feedback. The feedback collection unit collects parent opinions about the educational toy and can improve the design and functions of the educational toy based on that feedback. For example, if a parent provides an opinion such as "I want more colors," a design that reflects that opinion is generated. The feedback collection unit can also periodically collect parent feedback and continuously improve the educational toy. This makes it possible to provide educational toys that reflect parent opinions.

[0071] The educational toy manufacturing system further includes a health data collection unit that collects health data of the child. The health data collection unit collects health data such as the child's body temperature and heart rate, and can suggest educational toys based on that data. For example, if a child has a cold, it can suggest educational toys that allow the child to play quietly. The health data collection unit can also monitor the child's health condition and suggest educational toys that suit the child's health condition. This makes it possible to provide educational toys that are optimal for the child's health condition.

[0072] The educational toy manufacturing system further includes a communication promotion unit that promotes communication between parents and children. The communication promotion unit analyzes communication between parents and children when they play together and can suggest educational toys based on the analysis results. For example, it can suggest puzzles or blocks that parents and children can assemble together. The communication promotion unit can also suggest activities to promote communication between parents and children. This makes it possible to provide educational toys that deepen communication between parents and children.

[0073] The educational toy manufacturing system can further use the emotion estimation function to analyze a child's stress level and suggest educational toys that have a relaxing effect. For example, if a child is feeling stressed, the system can suggest educational toys that incorporate colors and shapes that have a relaxing effect. The emotion estimation function can also be used to monitor a child's stress level and suggest activities to reduce stress. This makes it possible to provide educational toys that reduce children's stress.

[0074] The educational toy manufacturing system can further use the emotion estimation function to analyze a child's level of concentration and suggest educational toys that will help improve that level of concentration. For example, it can analyze a child's facial expressions and behavior when they are concentrating and suggest educational toys that will help improve that level of concentration based on that data. It can also use the emotion estimation function to monitor a child's level of concentration and suggest activities to help them maintain their concentration. This makes it possible to provide educational toys that will help improve a child's level of concentration.

[0075] The educational toy manufacturing system can further use the emotion estimation function to analyze a child's excitement level and suggest educational toys that maintain an appropriate level of excitement. For example, if a child is overly excited, educational toys that have a calming effect can be suggested. The emotion estimation function can also be used to monitor a child's excitement level and suggest activities to maintain an appropriate level of excitement. This makes it possible to provide educational toys that appropriately control a child's excitement level.

[0076] The educational toy manufacturing system can further use the emotion estimation function to analyze changes in a child's emotions and suggest educational toys that balance their emotions. For example, if a child is sad, it can suggest educational toys that have the effect of brightening their mood. It can also use the emotion estimation function to monitor changes in a child's emotions and suggest activities to balance their emotions. This makes it possible to provide educational toys that balance a child's emotions.

[0077] The educational toy manufacturing system can further use the emotion estimation function to analyze a child's happiness level and suggest educational toys that will increase happiness. For example, it can analyze a child's facial expressions and behavior when they are feeling happy and suggest educational toys that will increase happiness based on that data. It can also use the emotion estimation function to monitor a child's happiness level and suggest activities to maintain that happiness level. This makes it possible to provide educational toys that will increase a child's happiness.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The age trend analysis unit analyzes the age and trends of babies and children. For example, information provided by parents and behavioral data of children are collected and analyzed by AI. For example, the AI ​​analyzes what types of play a child prefers and what colors and shapes they are interested in. Step 2: The design generation unit generates a design for the educational toy based on the data analyzed by the age tendency analysis unit. For example, if a child is interested in animals, it generates an animal-shaped educational toy design. Also, if a child likes a particular color, it generates a design incorporating that color. Step 3: The manufacturing department manufactures the educational toy based on the design generated by the design generation department. For example, the design data generated by the AI ​​is input into a 3D printer, and the educational toy is manufactured using materials such as plastic and resin.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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. An age trend analysis section that analyzes the age and trends of babies and children, a design generation unit that generates a design of an educational toy based on the data analyzed by the age tendency analysis unit; a manufacturing unit that manufactures educational toys based on the design generated by the design generation unit. A system characterized by:

2. The age tendency analysis unit Analyzes the child's facial expressions and tone of voice in real time and instantly reflects changes in interests 2. The system of claim 1.

3. The age tendency analysis unit Analyze the child's eating and sleeping patterns and suggest educational toys based on their overall health.

2. The system of claim 1.

4. The design generation unit Dynamic design generation that automatically updates the design as the child's interests change.

2. The system of claim 1.

5. The design generation unit Identifying design elements that the child enjoys most and generating the design that reflects them 2. The system of claim 1.

6. The manufacturing department The educational toys are made from environmentally friendly biodegradable plastics, providing eco-friendly products.

2. The system of claim 1.

7. The manufacturing department Identify the materials and textures that children enjoy most and manufacture products that reflect those.

2. The system of claim 1.

8. The age tendency analysis unit Estimating the child's emotions while playing and identifying the play style that elicits the positive emotions 2. The system of claim 1.

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