system

Abstract

The system according to this embodiment aims to allow children to color in scenes they have imagined. [Solution] The system according to the embodiment comprises a dialogue unit, a generation unit, a characterization unit, a delivery unit, and a learning unit. The dialogue unit speaks of the scene the child wants to color. The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. The characterization unit characterizes the child and places them in the coloring page. The delivery unit binds the generated coloring page and delivers it to the child's home. The learning unit learns the child's boundless imagination.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that a child could not color a scene imagined by himself / herself in a coloring book and it was difficult to sustain the interest.

[0005] The system according to the embodiment aims to enable a child to color a scene imagined by himself / herself in a coloring book.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, a generation unit, a characterization unit, a delivery unit, and a learning unit. The dialogue unit speaks of the scene the child wants to color. The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. The characterization unit characterizes the child and places them in the coloring page. The delivery unit binds the generated coloring page and delivers it to the child's home. The learning unit learns the child's boundless imagination. [Effects of the Invention]

[0007] The system according to this embodiment allows children to color in scenes they have imagined. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The coloring page generation system according to an embodiment of the present invention is a system that allows children who love coloring to create an unlimited number of coloring pages of their favorite characters. The coloring page generation system allows children to interact with an AI and provide information to turn their imagined scenes into coloring pages. Next, the AI ​​generates a coloring page based on that information. Furthermore, it is also possible to turn the child themselves into a character and include them in the coloring page. This mechanism allows children to create as many coloring pages of their favorite characters as they like. In addition, the child's boundless imagination is valuable learning material for the AI. For example, a child can talk to the AI ​​about a scene they would like to color. For example, they can tell the AI ​​a specific scene such as "playing with animals" or "exploring space." At this time, they can also provide detailed information such as which animals they would like to play with or which planet they would like to explore. Next, the AI ​​analyzes the information provided by the child and generates a coloring page. For example, if a child requests a scene of "playing with dinosaurs," the AI ​​will generate a coloring page based on that scene. Furthermore, it is also possible to turn the child themselves into a character and include them in the coloring page. For example, a child's photo can be taken, a character generated based on the photo, and placed in a coloring book. The generated coloring book is then bound and delivered to the home mailbox. This allows children to obtain their own original coloring books. Furthermore, the AI ​​learns from children's boundless imagination, enabling the generation of more sophisticated coloring books in the future. This system is very appealing to children who love coloring books and their parents. For children who have exhausted all the commercially available coloring books, the ability to create an unlimited number of coloring books of their favorite characters will be a great joy. Also, children's boundless imagination is valuable learning material for the AI, contributing to its growth. In this way, the coloring book generation system allows children to create an unlimited number of coloring books of their favorite characters.

[0029] The coloring page generation system according to this embodiment comprises a dialogue unit, a generation unit, a characterization unit, a delivery unit, and a learning unit. The dialogue unit receives information from the child about the scene they want to color in. The dialogue unit can, for example, allow the child to describe specific scenes such as "playing with animals" or "exploring space." The dialogue unit also accepts detailed information such as which animals the child wants to play with or which planet they want to explore. The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if the child requests "a scene of playing with dinosaurs," the generation unit will generate a coloring page based on that scene. The generation unit uses a generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit uses, for example, a text generation AI (e.g., LLM) to analyze what the child has said and generate a coloring page scene. The generation unit can also use a multimodal generation AI to generate a coloring page scene based on what the child has said. The characterization unit characterizes the child themselves and places them in the coloring page. The character creation unit, for example, takes a child's photo, generates a character based on that photo, and places it in the coloring book. The character creation unit uses a generation AI to analyze the child's photo and generate a character. The character creation unit uses an image generation AI to generate a character based on the child's photo. The character creation unit can also use a multimodal generation AI to generate a character based on the child's photo. The delivery unit binds the generated coloring book and delivers it to the home. The delivery unit binds the generated coloring book and mails it. The delivery unit uses a generation AI to optimize the binding and delivery schedule of the coloring book. The delivery unit uses a generation AI to automatically adjust the binding and delivery schedule of the coloring book. The learning unit learns the boundless imagination of children, enabling more advanced coloring book generation in the future. The learning unit uses a generation AI to learn the imagination of children and improve the coloring book generation algorithm. The learning unit uses a generation AI to optimize the coloring book generation algorithm based on the imagination of children. As a result, the coloring page generation system according to this embodiment allows children to create an unlimited number of coloring pages of their favorite characters.

[0030] The dialogue unit receives information from the child describing the scene they want to color. For example, the dialogue unit can describe specific scenes such as "playing with animals" or "exploring space." The dialogue unit also accepts detailed information such as which animals the child wants to play with or which planet they want to explore. The dialogue unit uses speech recognition technology to convert what the child says into text data and then analyzes that text data. For example, if the child says "a scene of playing with dinosaurs," the dialogue unit accurately recognizes the content and sends it to the generation unit as text data. The dialogue unit uses natural language processing technology to understand the intent of what the child says and extract the necessary information. For example, if the child says "a big dinosaur and a small dinosaur playing together," the dialogue unit extracts the keywords "big dinosaur," "small dinosaur," and "playing together" and sends them to the generation unit. The dialogue unit can analyze what the child says in real time and quickly obtain the necessary information. Furthermore, the dialogue unit can provide appropriate feedback on what the child says, facilitating a smooth conversation. For example, after a child describes a scene where they are playing with dinosaurs, the interactive program can ask, "What kind of dinosaurs would you like to play with?" to elicit more detailed information. This allows the interactive program to stimulate the child's imagination and gather information to generate a specific coloring scene.

[0031] The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if a child requests a scene of "playing with a dinosaur," the generation unit will generate a coloring page based on that scene. The generation unit uses generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit can also use text generation AI (e.g., LLM) to analyze what the child says and generate a coloring page scene. Furthermore, the generation unit can use multimodal generation AI to generate a coloring page scene based on what the child says. First, the generation unit analyzes the text data received from the dialogue unit to identify the main elements of the scene. For example, it extracts keywords such as "dinosaur," "playing," and "child," and converts each element into a concrete image. Next, the generation unit uses image generation AI to generate the entire scene based on the extracted elements. For example, to generate a scene of a "dinosaur" and a "child" playing together, it combines images of the dinosaur and the child, adds a background and small objects to complete the scene. The generation unit optimizes the generated scene as a coloring page and converts it into a line drawing. This completes the coloring page for the child to color. The generation unit checks the quality of the generated coloring page and makes corrections as needed. For example, if the scene is unbalanced or important elements are missing, it will regenerate the image to provide the optimal coloring page. In this way, the generation unit can translate the child's imagination into a concrete coloring page and provide an enjoyable coloring experience.

[0032] The character creation unit transforms the child into a character and places it in the coloring book. For example, the character creation unit takes a photograph of the child, generates a character based on that photograph, and places it in the coloring book. The character creation unit uses generation AI to analyze the child's photograph and generate a character. For example, the character creation unit uses image generation AI to generate a character based on the child's photograph. The character creation unit can also use multimodal generation AI to generate a character based on the child's photograph. First, the character creation unit takes a photograph of the child and analyzes it to identify facial features and body proportions. Next, it uses image generation AI to generate a character based on the child's features. For example, it generates an anime-style character based on the child's facial features and places that character in the coloring book scene. The character creation unit checks the quality of the generated character and makes corrections as needed. For example, if the character's expression or pose is unnatural, it regenerates it to provide the optimal character. The character creation unit naturally places the generated character in the coloring book scene and adjusts the overall balance of the scene. This provides a fun experience where the child themselves appears in the coloring book. Furthermore, the character creation department can customize the character's costume and accessories according to the child's preferences. For example, it can generate costumes incorporating the child's favorite colors and designs and dress the character in them. This allows the character creation department to provide coloring pages that reflect the child's individuality, resulting in a more enjoyable coloring experience.

[0033] The delivery department binds the generated coloring pages and delivers them to the recipient's home. For example, the delivery department binds the generated coloring pages and mails them. The delivery department uses generational AI to optimize the binding and delivery schedule for the coloring pages. For example, the delivery department uses generational AI to automatically adjust the binding and delivery schedule for the coloring pages. First, the delivery department saves the generated coloring pages as digital data and uses that data to bind them. The binding is done using high-quality printing technology, ensuring that the line art and characters of the coloring pages are printed clearly. Next, the bound coloring pages are wrapped in appropriate packaging materials and prepared for delivery. The delivery department uses generational AI to calculate the optimal delivery route and schedule, ensuring that the coloring pages are delivered quickly and reliably. For example, it considers the delivery address and the delivery company's schedule to select the optimal delivery date and ship the coloring pages. The delivery department can also track the delivery status in real time and adjust the delivery schedule as needed. This allows the delivery department to deliver the generated coloring pages quickly and reliably. Furthermore, the delivery department will notify children and their guardians of the delivery status, allowing them to wait for the coloring book to arrive with peace of mind. For example, they will notify them of the delivery progress via email or SMS and inform them of the expected arrival date of the coloring book. This will allow the delivery department to ensure that the coloring book that children are looking forward to is delivered safely, increasing their satisfaction.

[0034] The learning unit learns children's boundless imagination, enabling the generation of more sophisticated coloring pages in the future. The learning unit uses generative AI to learn children's imagination and improve the coloring page generation algorithm. For example, the learning unit uses generative AI to optimize the coloring page generation algorithm based on children's imagination. First, the learning unit analyzes data collected from the dialogue unit and generation unit to learn children's imagination and preferences. For example, it collects and analyzes information such as what kinds of scenes and characters children like, and what kinds of colors and designs they choose. Next, the learning unit improves the generative AI algorithm based on the collected data, enabling the generation of more sophisticated coloring pages. For example, it adjusts and optimizes the parameters of the generative AI to generate coloring page scenes and characters that reflect children's preferences and imagination. The learning unit continuously updates the generative AI's learning data, reflecting the latest information to ensure that optimal coloring page generation is always possible. Furthermore, the learning unit can flexibly adjust the algorithm to respond to changes in children's imagination and preferences. For example, if a child develops a new interest or their preferences change, the AI ​​algorithm for coloring pages is readjusted based on that information to accommodate the latest coloring page generation. This allows the learning unit to learn from the child's boundless imagination and always provide the most optimal coloring page generation.

[0035] The dialogue unit can analyze a child's past dialogue history and select the optimal dialogue scenario. For example, the dialogue unit can analyze trends in scenes the child has talked about in the past and suggest similar scenes. For example, the dialogue unit can create a new scenario based on characters the child has shown interest in in the past. For example, the dialogue unit can adjust the way the dialogue progresses by referring to patterns of dialogues the child has enjoyed in the past. This provides the optimal dialogue scenario based on the child's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's past dialogue history data into a generating AI and have the generating AI select the optimal dialogue scenario.

[0036] The dialogue unit can customize the content of questions based on the child's interests and concerns during the conversation. For example, the dialogue unit can ask a child about their favorite character and suggest scenes related to that character. For example, the dialogue unit can create questions based on themes the child is interested in (e.g., animals or sports). For example, the dialogue unit can ask a child about a movie or anime they have recently watched and suggest scenes based on that content. This provides questions that are tailored to the child's interests and concerns. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data about the child's interests and concerns into a generating AI and have the generating AI customize the content of the questions.

[0037] The generation unit can select the optimal design by referring to the child's past coloring history during generation. For example, the generation unit can analyze the trends of designs the child has liked in the past and generate similar designs. For example, the generation unit can create new designs based on characters the child has shown interest in in the past. For example, the generation unit can generate designs by referring to themes the child has enjoyed in the past (e.g., animals or nature). This provides the optimal design based on the child's past coloring history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's past coloring history data into a generation AI and have the generation AI select the optimal design.

[0038] The generation unit can adjust the complexity of the coloring page according to the level of detail of the information provided by the child during generation. For example, if the child provides detailed information, the generation unit can generate a complex and detailed design. For example, if the child provides simple information, the generation unit can generate a simple and easy-to-understand design. For example, if the child provides moderately detailed information, the generation unit can generate a design of moderate complexity. In this way, the complexity of the coloring page is adjusted according to the level of detail of the information provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the information provided by the child into a generation AI and have the generation AI perform the adjustment of the complexity of the coloring page.

[0039] The character creation unit can generate an optimal character by referring to past photographs of the child during the character creation process. For example, the character creation unit can generate a character with similar features based on past photographs of the child. For example, the character creation unit can create a character incorporating the child's favorite clothing and accessories from past photographs. For example, the character creation unit can analyze past photographs of the child and generate a character that matches their growth. This provides an optimal character based on past photographs. Some or all of the above processes in the character creation unit may be performed using AI, for example, or without AI. For example, the character creation unit can input past photograph data of the child into a generation AI and have the generation AI perform the generation of an optimal character.

[0040] The character creation unit can customize the character's clothing and accessories based on information provided by the child during the character creation process. For example, the character creation unit can customize the character's clothing based on the child's favorite colors and designs. For example, the character creation unit can add specific accessories (e.g., hats or ribbons) to the character based on information provided by the child. For example, the character creation unit can customize the character's clothing based on the child's interests (e.g., sports or music). This results in the character's clothing and accessories being customized based on the provided information. Some or all of the above processes in the character creation unit may be performed using AI, for example, or without AI. For example, the character creation unit can input information provided by the child into a generating AI and have the generating AI perform the customization of the character's clothing and accessories.

[0041] The delivery department can select the optimal delivery method by referring to the child's past delivery history during delivery. For example, the delivery department can select the optimal method based on the child's preferred delivery method in the past (e.g., express delivery or standard delivery). For example, the delivery department can select the optimal delivery time by referring to the time slots when the child has received deliveries in the past. For example, the delivery department can select a highly reliable carrier based on the carrier the child has used in the past. This provides the optimal delivery method based on past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the child's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0042] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective algorithm. For example, the learning unit can extract specific patterns from past learning data and optimize the algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. This provides an optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0043] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0044] The coloring page generation system can also include a customization options section. This section allows children to choose the design and character details of the coloring page themselves. For example, if a child requests a "scene of playing with dinosaurs," the customization options section can provide options to select the type and color of the dinosaur and background details. The customization options section can also refer to the child's past selection history and suggest similar options. For example, if a child previously liked "blue dinosaurs," the customization options section can suggest blue dinosaurs for the new scene as well. Furthermore, the customization options section can estimate the child's emotions and adjust the suggested options based on those emotions. For example, if the child is excited, it can offer more customization options, while if the child is relaxed, it can offer simpler options.

[0045] The coloring page generation system can also include a sharing function. This function allows children to share their completed coloring pages with family and friends. For example, after a child completes a "scene of playing with dinosaurs," the sharing function can offer the option to share the coloring page via email or social media. The sharing function can also refer to the child's past sharing history and suggest the most frequently used sharing method. For instance, if a child previously preferred "sharing via email," the sharing function can suggest email sharing for new coloring pages as well. Furthermore, the sharing function can estimate the child's emotions and adjust the timing and method of sharing based on that estimation. For example, if the child is excited, it can quickly offer sharing options; if the child is relaxed, it can present sharing options slowly.

[0046] The coloring page generation system can also include an achievement section. The achievement section provides rewards that allow children to feel a sense of accomplishment upon completing a coloring page. For example, after a child completes a scene of "playing with dinosaurs," the achievement section can provide a badge or sticker. The achievement section can also refer to the child's past achievement history and suggest similar rewards. For example, if a child previously enjoyed "animal badges," the achievement section can suggest animal badges for new scenes. Furthermore, the achievement section can estimate the child's emotions and adjust the content and timing of rewards based on those emotions. For example, if the child is excited, the reward can be given immediately, while if the child is relaxed, the reward can be given slowly.

[0047] The coloring page generation system can also include an advice section. The advice section provides advice and hints to help children enjoy coloring more. For example, if a child is coloring a scene of "playing with dinosaurs," the advice section can provide hints on how to choose colors for the dinosaurs and how to draw the background. The advice section can also refer to the child's past coloring history and suggest advice for similar scenes. For example, if a child has previously enjoyed "animal scenes," the advice section can provide advice on how to draw animals in new scenes. Furthermore, the advice section can estimate the child's emotions and adjust the content and timing of the advice based on those emotions. For example, if the child is excited, it can provide specific advice, and if the child is relaxed, it can provide simple hints.

[0048] The coloring page generation system can also include a community section. The community section provides a platform where children can share and interact with other children. For example, after a child completes a "scene of playing with dinosaurs," the community section can offer the option to post that coloring page to an online community. The community section can also refer to a child's past posting history and facilitate interaction with children who have similar interests. For example, if a child has previously posted a "space exploration" scene, the community section can suggest interaction with children who are also interested in space exploration for their new scene. Furthermore, the community section can estimate a child's emotions and adjust the timing and method of interaction based on those estimates. For example, if a child is excited, it can immediately offer interaction options, while if a child is relaxed, it can offer interaction options more slowly.

[0049] The following briefly describes the processing flow for example form 1.

[0050] Step 1: The interactive section receives information about the scene the child wants to color. For example, the child can describe a specific scene such as "playing with animals" or "exploring space." The interactive section also accepts more detailed information, such as which animals the child wants to play with or which planet they want to explore. Step 2: The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if a child requests a "scene of playing with dinosaurs," the generation unit will generate a coloring page based on that scene. The generation unit uses a generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit also uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to analyze what the child has said and generate a scene for the coloring page. Step 3: The character creation unit transforms the child into a character and places it in the coloring book. For example, it takes a photo of the child, generates a character based on that photo, and places it in the coloring book. The character creation unit uses generation AI to analyze the child's photo and generate a character. The character creation unit uses, for example, image generation AI or multimodal generation AI to generate a character based on the child's photo. Step 4: The delivery department binds the generated coloring pages and delivers them to the home. For example, the generated coloring pages are bound and mailed. The delivery department uses generational AI to optimize the binding and delivery schedule for the coloring pages. For example, the delivery department uses generational AI to automatically adjust the binding and delivery schedule for the coloring pages. Step 5: The learning unit learns from children's boundless imagination, enabling more sophisticated coloring page generation in the future. The learning unit uses generative AI to learn from children's ideas and improve the coloring page generation algorithm. For example, the learning unit uses generative AI to optimize the coloring page generation algorithm based on children's ideas.

[0051] (Example of form 2) The coloring page generation system according to an embodiment of the present invention is a system that allows children who love coloring to create an unlimited number of coloring pages of their favorite characters. The coloring page generation system allows children to interact with an AI and provide information to turn their imagined scenes into coloring pages. Next, the AI ​​generates a coloring page based on that information. Furthermore, it is also possible to turn the child themselves into a character and include them in the coloring page. This mechanism allows children to create as many coloring pages of their favorite characters as they like. In addition, the child's boundless imagination is valuable learning material for the AI. For example, a child can talk to the AI ​​about a scene they would like to color. For example, they can tell the AI ​​a specific scene such as "playing with animals" or "exploring space." At this time, they can also provide detailed information such as which animals they would like to play with or which planet they would like to explore. Next, the AI ​​analyzes the information provided by the child and generates a coloring page. For example, if a child requests a scene of "playing with dinosaurs," the AI ​​will generate a coloring page based on that scene. Furthermore, it is also possible to turn the child themselves into a character and include them in the coloring page. For example, a child's photo can be taken, a character generated based on the photo, and placed in a coloring book. The generated coloring book is then bound and delivered to the home mailbox. This allows children to obtain their own original coloring books. Furthermore, the AI ​​learns from children's boundless imagination, enabling the generation of more sophisticated coloring books in the future. This system is very appealing to children who love coloring books and their parents. For children who have exhausted all the commercially available coloring books, the ability to create an unlimited number of coloring books of their favorite characters will be a great joy. Also, children's boundless imagination is valuable learning material for the AI, contributing to its growth. In this way, the coloring book generation system allows children to create an unlimited number of coloring books of their favorite characters.

[0052] The coloring page generation system according to this embodiment comprises a dialogue unit, a generation unit, a characterization unit, a delivery unit, and a learning unit. The dialogue unit receives information from the child about the scene they want to color in. The dialogue unit can, for example, allow the child to describe specific scenes such as "playing with animals" or "exploring space." The dialogue unit also accepts detailed information such as which animals the child wants to play with or which planet they want to explore. The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if the child requests "a scene of playing with dinosaurs," the generation unit will generate a coloring page based on that scene. The generation unit uses a generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit uses, for example, a text generation AI (e.g., LLM) to analyze what the child has said and generate a coloring page scene. The generation unit can also use a multimodal generation AI to generate a coloring page scene based on what the child has said. The characterization unit characterizes the child themselves and places them in the coloring page. The character creation unit, for example, takes a child's photo, generates a character based on that photo, and places it in the coloring book. The character creation unit uses a generation AI to analyze the child's photo and generate a character. The character creation unit uses an image generation AI to generate a character based on the child's photo. The character creation unit can also use a multimodal generation AI to generate a character based on the child's photo. The delivery unit binds the generated coloring book and delivers it to the home. The delivery unit binds the generated coloring book and mails it. The delivery unit uses a generation AI to optimize the binding and delivery schedule of the coloring book. The delivery unit uses a generation AI to automatically adjust the binding and delivery schedule of the coloring book. The learning unit learns the boundless imagination of children, enabling more advanced coloring book generation in the future. The learning unit uses a generation AI to learn the imagination of children and improve the coloring book generation algorithm. The learning unit uses a generation AI to optimize the coloring book generation algorithm based on the imagination of children. As a result, the coloring page generation system according to this embodiment allows children to create an unlimited number of coloring pages of their favorite characters.

[0053] The dialogue unit receives information from the child describing the scene they want to color. For example, the dialogue unit can describe specific scenes such as "playing with animals" or "exploring space." The dialogue unit also accepts detailed information such as which animals the child wants to play with or which planet they want to explore. The dialogue unit uses speech recognition technology to convert what the child says into text data and then analyzes that text data. For example, if the child says "a scene of playing with dinosaurs," the dialogue unit accurately recognizes the content and sends it to the generation unit as text data. The dialogue unit uses natural language processing technology to understand the intent of what the child says and extract the necessary information. For example, if the child says "a big dinosaur and a small dinosaur playing together," the dialogue unit extracts the keywords "big dinosaur," "small dinosaur," and "playing together" and sends them to the generation unit. The dialogue unit can analyze what the child says in real time and quickly obtain the necessary information. Furthermore, the dialogue unit can provide appropriate feedback on what the child says, facilitating a smooth conversation. For example, after a child describes a scene where they are playing with dinosaurs, the interactive program can ask, "What kind of dinosaurs would you like to play with?" to elicit more detailed information. This allows the interactive program to stimulate the child's imagination and gather information to generate a specific coloring scene.

[0054] The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if a child requests a scene of "playing with a dinosaur," the generation unit will generate a coloring page based on that scene. The generation unit uses generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit can also use text generation AI (e.g., LLM) to analyze what the child says and generate a coloring page scene. Furthermore, the generation unit can use multimodal generation AI to generate a coloring page scene based on what the child says. First, the generation unit analyzes the text data received from the dialogue unit to identify the main elements of the scene. For example, it extracts keywords such as "dinosaur," "playing," and "child," and converts each element into a concrete image. Next, the generation unit uses image generation AI to generate the entire scene based on the extracted elements. For example, to generate a scene of a "dinosaur" and a "child" playing together, it combines images of the dinosaur and the child, adds a background and small objects to complete the scene. The generation unit optimizes the generated scene as a coloring page and converts it into a line drawing. This completes the coloring page for the child to color. The generation unit checks the quality of the generated coloring page and makes corrections as needed. For example, if the scene is unbalanced or important elements are missing, it will regenerate the image to provide the optimal coloring page. In this way, the generation unit can translate the child's imagination into a concrete coloring page and provide an enjoyable coloring experience.

[0055] The character creation unit transforms the child into a character and places it in the coloring book. For example, the character creation unit takes a photograph of the child, generates a character based on that photograph, and places it in the coloring book. The character creation unit uses generation AI to analyze the child's photograph and generate a character. For example, the character creation unit uses image generation AI to generate a character based on the child's photograph. The character creation unit can also use multimodal generation AI to generate a character based on the child's photograph. First, the character creation unit takes a photograph of the child and analyzes it to identify facial features and body proportions. Next, it uses image generation AI to generate a character based on the child's features. For example, it generates an anime-style character based on the child's facial features and places that character in the coloring book scene. The character creation unit checks the quality of the generated character and makes corrections as needed. For example, if the character's expression or pose is unnatural, it regenerates it to provide the optimal character. The character creation unit naturally places the generated character in the coloring book scene and adjusts the overall balance of the scene. This provides a fun experience where the child themselves appears in the coloring book. Furthermore, the character creation department can customize the character's costume and accessories according to the child's preferences. For example, it can generate costumes incorporating the child's favorite colors and designs and dress the character in them. This allows the character creation department to provide coloring pages that reflect the child's individuality, resulting in a more enjoyable coloring experience.

[0056] The delivery department binds the generated coloring pages and delivers them to the recipient's home. For example, the delivery department binds the generated coloring pages and mails them. The delivery department uses generational AI to optimize the binding and delivery schedule for the coloring pages. For example, the delivery department uses generational AI to automatically adjust the binding and delivery schedule for the coloring pages. First, the delivery department saves the generated coloring pages as digital data and uses that data to bind them. The binding is done using high-quality printing technology, ensuring that the line art and characters of the coloring pages are printed clearly. Next, the bound coloring pages are wrapped in appropriate packaging materials and prepared for delivery. The delivery department uses generational AI to calculate the optimal delivery route and schedule, ensuring that the coloring pages are delivered quickly and reliably. For example, it considers the delivery address and the delivery company's schedule to select the optimal delivery date and ship the coloring pages. The delivery department can also track the delivery status in real time and adjust the delivery schedule as needed. This allows the delivery department to deliver the generated coloring pages quickly and reliably. Furthermore, the delivery department will notify children and their guardians of the delivery status, allowing them to wait for the coloring book to arrive with peace of mind. For example, they will notify them of the delivery progress via email or SMS and inform them of the expected arrival date of the coloring book. This will allow the delivery department to ensure that the coloring book that children are looking forward to is delivered safely, increasing their satisfaction.

[0057] The learning unit learns children's boundless imagination, enabling the generation of more sophisticated coloring pages in the future. The learning unit uses generative AI to learn children's imagination and improve the coloring page generation algorithm. For example, the learning unit uses generative AI to optimize the coloring page generation algorithm based on children's imagination. First, the learning unit analyzes data collected from the dialogue unit and generation unit to learn children's imagination and preferences. For example, it collects and analyzes information such as what kinds of scenes and characters children like, and what kinds of colors and designs they choose. Next, the learning unit improves the generative AI algorithm based on the collected data, enabling the generation of more sophisticated coloring pages. For example, it adjusts and optimizes the parameters of the generative AI to generate coloring page scenes and characters that reflect children's preferences and imagination. The learning unit continuously updates the generative AI's learning data, reflecting the latest information to ensure that optimal coloring page generation is always possible. Furthermore, the learning unit can flexibly adjust the algorithm to respond to changes in children's imagination and preferences. For example, if a child develops a new interest or their preferences change, the AI ​​algorithm for coloring pages is readjusted based on that information to accommodate the latest coloring page generation. This allows the learning unit to learn from the child's boundless imagination and always provide the most optimal coloring page generation.

[0058] The dialogue unit can estimate the child's emotions and adjust the pace of the dialogue based on the estimated emotions. For example, if the child is excited, the dialogue unit can speed up the pace of the dialogue to keep the child interested. For example, if the child is tired, the dialogue unit can slow down the pace of the dialogue to create a relaxed atmosphere. For example, if the child is feeling anxious, the dialogue unit can use gentle language to provide reassurance. This makes it possible to conduct the dialogue in accordance with the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0059] The dialogue unit can analyze a child's past dialogue history and select the optimal dialogue scenario. For example, the dialogue unit can analyze trends in scenes the child has talked about in the past and suggest similar scenes. For example, the dialogue unit can create a new scenario based on characters the child has shown interest in in the past. For example, the dialogue unit can adjust the way the dialogue progresses by referring to patterns of dialogues the child has enjoyed in the past. This provides the optimal dialogue scenario based on the child's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's past dialogue history data into a generating AI and have the generating AI select the optimal dialogue scenario.

[0060] The dialogue unit can customize the content of questions based on the child's interests and concerns during the conversation. For example, the dialogue unit can ask a child about their favorite character and suggest scenes related to that character. For example, the dialogue unit can create questions based on themes the child is interested in (e.g., animals or sports). For example, the dialogue unit can ask a child about a movie or anime they have recently watched and suggest scenes based on that content. This provides questions that are tailored to the child's interests and concerns. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data about the child's interests and concerns into a generating AI and have the generating AI customize the content of the questions.

[0061] The generation unit can estimate a child's emotions and adjust the coloring page design based on the estimated emotions. For example, if the child is excited, the generation unit can generate a bright and colorful design. For example, if the child is relaxed, the generation unit can generate a design with calming colors. For example, if the child is sad, the generation unit can generate an uplifting design. This provides coloring page designs that correspond to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0062] The generation unit can select the optimal design by referring to the child's past coloring history during generation. For example, the generation unit can analyze the trends of designs the child has liked in the past and generate similar designs. For example, the generation unit can create new designs based on characters the child has shown interest in in the past. For example, the generation unit can generate designs by referring to themes the child has enjoyed in the past (e.g., animals or nature). This provides the optimal design based on the child's past coloring history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's past coloring history data into a generation AI and have the generation AI select the optimal design.

[0063] The generation unit can adjust the complexity of the coloring page according to the level of detail of the information provided by the child during generation. For example, if the child provides detailed information, the generation unit can generate a complex and detailed design. For example, if the child provides simple information, the generation unit can generate a simple and easy-to-understand design. For example, if the child provides moderately detailed information, the generation unit can generate a design of moderate complexity. In this way, the complexity of the coloring page is adjusted according to the level of detail of the information provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the information provided by the child into a generation AI and have the generation AI perform the adjustment of the complexity of the coloring page.

[0064] The characterization unit can estimate a child's emotions and adjust the character's facial expression based on the estimated emotions. For example, if the child is excited, the characterization unit can make the character's facial expression bright and cheerful. For example, if the child is relaxed, the characterization unit can make the character's facial expression calm. For example, if the child is sad, the characterization unit can make the character's facial expression gentle and encouraging. This provides a character with facial expressions that correspond to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the characterization unit may be performed using AI, for example, or without AI. For example, the characterization unit can input child facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The character creation unit can generate an optimal character by referring to past photographs of the child during the character creation process. For example, the character creation unit can generate a character with similar features based on past photographs of the child. For example, the character creation unit can create a character incorporating the child's favorite clothing and accessories from past photographs. For example, the character creation unit can analyze past photographs of the child and generate a character that matches their growth. This provides an optimal character based on past photographs. Some or all of the above processes in the character creation unit may be performed using AI, for example, or without AI. For example, the character creation unit can input past photograph data of the child into a generation AI and have the generation AI perform the generation of an optimal character.

[0066] The character creation unit can customize the character's clothing and accessories based on information provided by the child during the character creation process. For example, the character creation unit can customize the character's clothing based on the child's favorite colors and designs. For example, the character creation unit can add specific accessories (e.g., hats or ribbons) to the character based on information provided by the child. For example, the character creation unit can customize the character's clothing based on the child's interests (e.g., sports or music). This results in the character's clothing and accessories being customized based on the provided information. Some or all of the above processes in the character creation unit may be performed using AI, for example, or without AI. For example, the character creation unit can input information provided by the child into a generating AI and have the generating AI perform the customization of the character's clothing and accessories.

[0067] The delivery unit can estimate a child's emotions and adjust the delivery timing based on the estimated emotions. For example, if a child is excited, the delivery unit can expedite the delivery so the child can get the coloring book sooner. If a child is relaxed, the delivery unit can deliver according to the normal delivery schedule. If a child is feeling anxious, the delivery unit can provide reassurance by notifying the child of the delivery status step by step. This provides delivery timing that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The delivery department can select the optimal delivery method by referring to the child's past delivery history during delivery. For example, the delivery department can select the optimal method based on the child's preferred delivery method in the past (e.g., express delivery or standard delivery). For example, the delivery department can select the optimal delivery time by referring to the time slots when the child has received deliveries in the past. For example, the delivery department can select a highly reliable carrier based on the carrier the child has used in the past. This provides the optimal delivery method based on past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the child's past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0069] The learning unit can estimate a child's emotions and select training data based on the estimated emotions. For example, if the child is excited, the learning unit can select training data that includes cheerful and bright scenes. For example, if the child is relaxed, the learning unit can select training data that includes calm and peaceful scenes. For example, if the child is sad, the learning unit can select training data that includes uplifting scenes. This ensures that training data is selected that corresponds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective algorithm. For example, the learning unit can extract specific patterns from past learning data and optimize the algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. This provides an optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

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

[0072] The coloring page generation system can also be equipped with a speech recognition unit. The speech recognition unit converts what the child says into text in real time and provides it to the dialogue unit. For example, if the child says "a scene of playing with dinosaurs," the speech recognition unit converts that content into text and sends it to the dialogue unit. This allows the dialogue unit to more accurately understand the scene the child wants based on the text received from the speech recognition unit and provide information to the generation unit. The speech recognition unit can also analyze the tone and speed of the child's voice and use it to assist in emotion estimation. For example, if a child is excited, their voice tone is often higher, so the speech recognition unit can provide this information to the dialogue unit as part of the emotion estimation. This allows the dialogue unit to more precisely adjust how the conversation progresses according to the child's emotions.

[0073] The coloring page generation system can also include a background generation unit. The background generation unit automatically generates backgrounds according to the scene the child desires. For example, if the child requests a "scene exploring space," the background generation unit can generate images of a starry sky and planets and provide them as the background for the coloring page. The background generation unit can also refer to the child's past coloring history and suggest similar backgrounds. For example, if the child previously liked "scenes underwater," the background generation unit can generate a background incorporating ocean elements for the new scene. Furthermore, the background generation unit can estimate the child's emotions and adjust the background's color scheme and atmosphere based on those emotions. For example, if the child is relaxed, it can generate a background with calm colors, and if the child is excited, it can generate a bright and colorful background.

[0074] The coloring page generation system can also be equipped with a music provider. This music provider offers music that helps children relax while they enjoy coloring. For example, if a child is coloring a scene of "playing with animals," the music provider can play background music that includes animal sounds and nature sounds. Furthermore, the music provider can estimate the child's emotions and adjust the music genre and tempo based on those estimates. For instance, it can provide upbeat music if the child is excited and slow music if the child is relaxed. Additionally, the music provider can refer to the child's past musical history and suggest new music based on their preferences. This allows children to enjoy coloring while listening to their favorite music.

[0075] The coloring page generation system can also include a story generation unit. This unit generates short stories based on scenes requested by the child and provides them along with the coloring pages. For example, if a child requests a scene of "playing with dinosaurs," the unit can generate an adventure story based on that scene and insert it into the coloring pages. The unit can also refer to the child's past coloring history to create a continuous story. For instance, if a child previously enjoyed a "space exploration" scene, the unit can create a sequel to that space exploration story for a new scene. Furthermore, the unit can estimate the child's emotions and adjust the tone and content of the story based on those emotions. For example, if the child is relaxed, it can generate a calm story; if the child is excited, it can generate an adventurous story.

[0076] The coloring page generation system can also be equipped with a feedback collection unit. This unit collects how children feel about their completed coloring pages and uses this information to improve the system. For example, after a child completes a "scene of playing with dinosaurs," the feedback collection unit can ask for their thoughts on the coloring page. The feedback collection unit can also estimate the child's emotions and adjust the feedback questions based on that estimation. For instance, if the child is excited, it can ask more specific questions; if the child is relaxed, it can provide simpler questions. Furthermore, the feedback collection unit can refer to the child's past feedback history to identify areas for system improvement. This allows the coloring page generation system to continuously improve based on children's feedback, providing a more satisfying coloring experience.

[0077] The coloring page generation system can also include a customization options section. This section allows children to choose the design and character details of the coloring page themselves. For example, if a child requests a "scene of playing with dinosaurs," the customization options section can provide options to select the type and color of the dinosaur and background details. The customization options section can also refer to the child's past selection history and suggest similar options. For example, if a child previously liked "blue dinosaurs," the customization options section can suggest blue dinosaurs for the new scene as well. Furthermore, the customization options section can estimate the child's emotions and adjust the suggested options based on those emotions. For example, if the child is excited, it can offer more customization options, while if the child is relaxed, it can offer simpler options.

[0078] The coloring page generation system can also include a sharing function. This function allows children to share their completed coloring pages with family and friends. For example, after a child completes a "scene of playing with dinosaurs," the sharing function can offer the option to share the coloring page via email or social media. The sharing function can also refer to the child's past sharing history and suggest the most frequently used sharing method. For instance, if a child previously preferred "sharing via email," the sharing function can suggest email sharing for new coloring pages as well. Furthermore, the sharing function can estimate the child's emotions and adjust the timing and method of sharing based on that estimation. For example, if the child is excited, it can quickly offer sharing options; if the child is relaxed, it can present sharing options slowly.

[0079] The coloring page generation system can also include an achievement section. The achievement section provides rewards that allow children to feel a sense of accomplishment upon completing a coloring page. For example, after a child completes a scene of "playing with dinosaurs," the achievement section can provide a badge or sticker. The achievement section can also refer to the child's past achievement history and suggest similar rewards. For example, if a child previously enjoyed "animal badges," the achievement section can suggest animal badges for new scenes. Furthermore, the achievement section can estimate the child's emotions and adjust the content and timing of rewards based on those emotions. For example, if the child is excited, the reward can be given immediately, while if the child is relaxed, the reward can be given slowly.

[0080] The coloring page generation system can also include an advice section. The advice section provides advice and hints to help children enjoy coloring more. For example, if a child is coloring a scene of "playing with dinosaurs," the advice section can provide hints on how to choose colors for the dinosaurs and how to draw the background. The advice section can also refer to the child's past coloring history and suggest advice for similar scenes. For example, if a child has previously enjoyed "animal scenes," the advice section can provide advice on how to draw animals in new scenes. Furthermore, the advice section can estimate the child's emotions and adjust the content and timing of the advice based on those emotions. For example, if the child is excited, it can provide specific advice, and if the child is relaxed, it can provide simple hints.

[0081] The coloring page generation system can also include a community section. The community section provides a platform where children can share and interact with other children. For example, after a child completes a "scene of playing with dinosaurs," the community section can offer the option to post that coloring page to an online community. The community section can also refer to a child's past posting history and facilitate interaction with children who have similar interests. For example, if a child has previously posted a "space exploration" scene, the community section can suggest interaction with children who are also interested in space exploration for their new scene. Furthermore, the community section can estimate a child's emotions and adjust the timing and method of interaction based on those estimates. For example, if a child is excited, it can immediately offer interaction options, while if a child is relaxed, it can offer interaction options more slowly.

[0082] The following briefly describes the processing flow for example form 2.

[0083] Step 1: The interactive section receives information about the scene the child wants to color. For example, the child can describe a specific scene such as "playing with animals" or "exploring space." The interactive section also accepts more detailed information, such as which animals the child wants to play with or which planet they want to explore. Step 2: The generation unit analyzes the information provided by the dialogue unit and generates a coloring page. For example, if a child requests a "scene of playing with dinosaurs," the generation unit will generate a coloring page based on that scene. The generation unit uses a generation AI to convert the child's imagined scene into a concrete coloring page. The generation unit also uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to analyze what the child has said and generate a scene for the coloring page. Step 3: The character creation unit transforms the child into a character and places it in the coloring book. For example, it takes a photo of the child, generates a character based on that photo, and places it in the coloring book. The character creation unit uses generation AI to analyze the child's photo and generate a character. The character creation unit uses, for example, image generation AI or multimodal generation AI to generate a character based on the child's photo. Step 4: The delivery department binds the generated coloring pages and delivers them to the home. For example, the generated coloring pages are bound and mailed. The delivery department uses generational AI to optimize the binding and delivery schedule for the coloring pages. For example, the delivery department uses generational AI to automatically adjust the binding and delivery schedule for the coloring pages. Step 5: The learning unit learns from children's boundless imagination, enabling more sophisticated coloring page generation in the future. The learning unit uses generative AI to learn from children's ideas and improve the coloring page generation algorithm. For example, the learning unit uses generative AI to optimize the coloring page generation algorithm based on children's ideas.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0086] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] Each of the multiple elements described above, including the dialogue unit, generation unit, characterization unit, delivery unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit receives information spoken by the child using the microphone 38B of the smart device 14 and processes that information with the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes what the child said and generates a coloring page. The characterization unit captures a photograph of the child using the camera 42 of the smart device 14 and generates a character with the specific processing unit 290 of the data processing unit 12. The delivery unit binds the generated coloring page and optimizes the delivery schedule with the specific processing unit 290 of the data processing unit 12. The learning unit learns the child's ideas with the specific processing unit 290 of the data processing unit 12 and improves the coloring page generation algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0088] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0089] As shown in Figure 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.

[0090] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0094] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0095] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0096] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0097] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0098] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0102] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the dialogue unit, generation unit, characterization unit, delivery unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit receives information spoken by the child using the microphone 238 of the smart glasses 214 and processes that information with the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes what the child said and generates a coloring page. The characterization unit captures a photograph of the child using the camera 42 of the smart glasses 214 and generates a character with the specific processing unit 290 of the data processing unit 12. The delivery unit binds the generated coloring page and optimizes the delivery schedule with the specific processing unit 290 of the data processing unit 12. The learning unit learns the child's ideas with the specific processing unit 290 of the data processing unit 12 and improves the coloring page generation algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0104] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0105] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the dialogue unit, generation unit, characterization unit, delivery unit, and learning unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit receives information spoken by the child using the microphone 238 of the headset terminal 314 and processes that information with the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes what the child said and generates a coloring page. The characterization unit captures a photograph of the child using the camera 42 of the headset terminal 314 and generates a character with the specific processing unit 290 of the data processing unit 12. The delivery unit binds the generated coloring page and optimizes the delivery schedule with the specific processing unit 290 of the data processing unit 12. The learning unit learns the child's ideas with the specific processing unit 290 of the data processing unit 12 and improves the coloring page generation algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0121] As shown in Figure 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0128] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the dialogue unit, generation unit, characterization unit, delivery unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the dialogue unit receives information spoken by the child using the microphone 238 of the robot 414 and processes that information with the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes what the child said and generates a coloring page. The characterization unit takes a picture of the child using the camera 42 of the robot 414 and generates a character with the specific processing unit 290 of the data processing unit 12. The delivery unit binds the generated coloring page and optimizes the delivery schedule with the specific processing unit 290 of the data processing unit 12. The learning unit learns the child's ideas with the specific processing unit 290 of the data processing unit 12 and improves the coloring page generation algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0139] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0140] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0141] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0145] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0146] 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.

[0147] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0148] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0149] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0150] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0152] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0153] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0154] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0155] (Note 1) A dialogue section where children talk about scenes they want to color in, A generation unit analyzes the information provided by the aforementioned dialogue unit and generates a coloring page, The character creation section involves turning the child themselves into a character and putting it into the coloring book, The delivery department binds the generated coloring pages and delivers them to your home, It includes a learning section for learning children's boundless imagination. A system characterized by the following features. (Note 2) The aforementioned dialogue unit, Estimate the child's emotions and adjust the way the conversation proceeds based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, Analyze the child's past conversation history and select the optimal conversation scenario. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, During the conversation, customize the questions based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates the child's emotions and adjusts the coloring page design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is During generation, the system references the child's past coloring history to select the most suitable design. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is During generation, the complexity of the coloring page is adjusted according to the level of detail in the information provided by the child. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned characterization unit is It estimates the child's emotions and adjusts the character's facial expressions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned characterization unit is When creating a character, the system references past photos of the child to generate the most suitable character. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned characterization unit is When creating a character, the character's clothing and accessories are customized based on information provided by the child. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned delivery department, The system estimates the child's emotions and adjusts the delivery timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned delivery department, During delivery, the system will refer to the child's past delivery history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, The system estimates the child's emotions and selects training data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A dialogue section where children talk about scenes they want to color in, A generation unit analyzes the information provided by the aforementioned dialogue unit and generates a coloring page, The character creation section involves turning the child themselves into a character and putting it into the coloring book, The delivery department binds the generated coloring pages and delivers them to your home, It includes a learning section for learning children's boundless imagination. A system characterized by the following features.

2. The aforementioned dialogue unit, Estimate the child's emotions and adjust the way the conversation proceeds based on those estimated emotions. The system according to feature 1.

3. The aforementioned dialogue unit, Analyze the child's past conversation history and select the optimal conversation scenario. The system according to feature 1.

4. The aforementioned dialogue unit, During the conversation, customize the questions based on the child's interests and concerns. The system according to feature 1.

5. The generating unit is It estimates the child's emotions and adjusts the coloring page design based on those estimated emotions. The system according to feature 1.

6. The generating unit is During generation, the system references the child's past coloring history to select the most suitable design. The system according to feature 1.

7. The generating unit is During generation, the complexity of the coloring page is adjusted according to the level of detail in the information provided by the child. The system according to feature 1.

8. The aforementioned characterization unit is It estimates the child's emotions and adjusts the character's facial expressions based on those estimated emotions. The system according to feature 1.

9. The aforementioned characterization unit is When creating a character, the system references past photos of the child to generate the most suitable character. The system according to feature 1.

10. The aforementioned characterization unit is When creating a character, the character's clothing and accessories are customized based on information provided by the child. The system according to feature 1.

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