System

Abstract

An object of the system according to the embodiment is to automatically generate and provide a customized line drawing based on the preference and interest of a child.SOLUTION: A system includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs a request. The analysis unit analyzes the request input by the input unit. The generation unit generates a line drawing customized based on the request analyzed by the analysis unit. The provision unit provides the line drawing generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the drawback of making it difficult to easily obtain customized coloring books based on a child's preferences and interests.

[0005] The system according to the embodiment aims to automatically generate and provide customized coloring books based on a child's preferences and interests. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs a request. The analysis unit analyzes the request input by the input unit. The generation unit generates a customized coloring book based on the request analyzed by the analysis unit. The provision unit provides the coloring book generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and provide customized coloring books based on a child's preferences and interests. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A customized coloring book generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate a customized coloring book based on a child's preferences and interests. In this system, parents or children input their coloring book requests, and the generation AI analyzes the requests to generate and provide a customized coloring book. For example, parents or children can input specific characters, themes, and color preferences, and the generation AI automatically generates a coloring book based on those requests. The generated coloring book can be downloaded and printed by parents or children, promoting children's creativity and providing a fun learning experience. This allows the customized coloring book generation system to automatically generate and provide coloring books based on children's preferences and interests. For example, parents can easily find coloring books that match their children's interests and preferences, and children can experience the joy of expressing their own world.

[0029] A customized coloring book generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit allows parents or children to input coloring book requests. For example, specific characters, themes, and color preferences can be input. The input unit can provide various input methods, such as text, audio, and image formats. The analysis unit analyzes the requests input by the input unit. For example, it can analyze text requests using natural language processing technology and image requests using image analysis technology. The analysis unit determines what type of coloring book is appropriate based on the content of the request. The generation unit generates a customized coloring book based on the analysis results of the analysis unit. The generation unit uses a generation AI to generate a coloring book that reflects specific characters, themes, and color preferences. For example, the generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a coloring book according to the request. The provision unit makes the generated coloring book available for download. The provision unit allows parents or children to download and print the generated coloring book. For example, the provision unit can provide the coloring book in PDF format or image format. As a result, the customized coloring book creation system according to the embodiment can automatically create and provide a customized coloring book based on the user's requests.

[0030] The input unit can input requests for specific characters, themes, and color preferences. For example, a parent or child can input specific character, theme, and color preferences through the input unit. For example, an anime character, a seasonal theme, or a color palette can be input. The input unit can provide various input methods, such as text, voice, and image formats. For example, a user can simply input "dinosaur coloring book" by voice, and the request is automatically set. In addition, a user can upload an image to generate a coloring book based on that image. This allows a coloring book to be customized based on the user's specific requests.

[0031] The analysis unit can analyze the input request and determine a suitable coloring book. The analysis unit can, for example, analyze a request in text format using natural language processing technology. For example, the analysis unit can analyze the text input by the user and determine what kind of coloring book is suitable. The analysis unit can also analyze a request in image format using image analysis technology. For example, the analysis unit can analyze an image uploaded by the user and extract information for generating a coloring book based on the image. The analysis unit can also analyze a request in audio format using voice analysis technology. For example, the analysis unit can analyze a request input by voice using the voice and determine a suitable coloring book. This makes it possible to perform analysis to generate a coloring book that best suits the user's request.

[0032] The generation unit can generate a customized coloring book based on the analysis results of the analysis unit. The generation unit generates a customized coloring book based on the analysis results of the analysis unit, for example, using a generation AI. For example, the generation AI generates a coloring book according to a user's request using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate a coloring book that reflects specific characters, themes, and color preferences. For example, it can generate a coloring book that includes illustrations of dinosaurs or a coloring book that includes a lot of blue. This makes it possible to generate an appropriate coloring book based on the analysis results.

[0033] The providing unit can make the generated coloring book downloadable. For example, the providing unit can allow a parent or child to download and print the generated coloring book. The providing unit can provide the coloring book in a PDF format or an image format. For example, the generated coloring book can be downloaded in a PDF format and printed. The providing unit can also download the generated coloring book in an image format and display it on a digital device. This allows a user to download and use the generated coloring book.

[0034] The input unit can analyze past input history and suggest the optimal input method. For example, the input unit automatically displays characters and themes that the user has frequently input in the past as candidates. For example, the input unit preferentially suggests dinosaurs and blue themes that the user has input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input frequently in the past, the input unit preferentially suggests voice input. The input unit can also predict and suggest characters and themes to be used during specific time periods based on the user's past input history. For example, if the user has input a lot of dinosaur coloring pages at night, the input unit suggests dinosaur coloring pages at night. In this way, the optimal input method can be suggested based on the past input history.

[0035] The input unit can present input candidates based on the user's current interests or trends. For example, the input unit can suggest related characters or themes based on topics recently searched by the user. For example, if the user has recently searched for dinosaurs, the input unit can suggest dinosaur coloring books. The input unit can also present input candidates based on trends in accounts the user follows on social media. For example, the input unit can suggest characters or themes that are popular among the accounts the user follows. The input unit can also suggest characters or themes related to products the user recently purchased. For example, if the user purchased a dinosaur toy, the input unit can suggest dinosaur coloring books. In this way, input candidates can be presented based on the user's interests and trends.

[0036] The input unit can provide a variety of input means, such as voice input or image input. For example, the input unit can automatically set a user's request by simply inputting "dinosaur coloring book" by voice. The input unit can also generate a coloring book based on an image uploaded by the user. For example, if a user uploads an image of a dinosaur, a coloring book based on that image is generated. The input unit can also combine voice input and image input to set a more detailed request. For example, if a user inputs "blue dinosaur coloring book" by voice and uploads an image of a dinosaur, a blue dinosaur coloring book is generated. This provides a variety of input means, thereby improving user convenience.

[0037] The input unit can present highly relevant input candidates based on the user's geographical location information. For example, if the user is in a specific area, the input unit can suggest characters or themes related to the area. For example, if the user is at a dinosaur museum, the input unit can suggest dinosaur coloring books. Furthermore, if the user is traveling, the input unit can also suggest coloring books related to the travel destination. For example, if the user is at the beach, the input unit can suggest sea creature coloring books. Furthermore, if the user is participating in a specific event, the input unit can also suggest coloring books related to the event. For example, if the user is participating in a Halloween event, the input unit can suggest Halloween-themed coloring books. In this way, highly relevant input candidates can be presented based on the user's geographical location information.

[0038] The input unit can analyze the user's social media activity and present relevant input candidates. For example, the input unit can suggest characters or themes that the user follows on social media as input candidates. For example, the input unit can suggest characters or themes that are popular among accounts that the user follows. The input unit can also analyze the content of the user's social media posts and suggest related coloring books. For example, if the user posts a lot about dinosaurs, the input unit can suggest dinosaur coloring books. The input unit can also suggest related coloring books based on the activity of the user's friends on social media. For example, if the user's friends enjoy dinosaur coloring books, the input unit can suggest dinosaur coloring books. In this way, relevant input candidates can be presented based on the user's social media activity.

[0039] The input unit can change the input method by reflecting the user's past feedback. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, if the user has previously rated dinosaur coloring books, the input unit will preferentially suggest dinosaur coloring books. The input unit can also preferentially display specific characters or themes based on the user's past feedback. For example, if the user has previously preferred blue coloring books, the input unit will preferentially suggest blue coloring books. The input unit can also analyze the user's past feedback and customize the input interface. For example, if the user has previously preferred voice input, the input unit will preferentially suggest voice input. This makes it possible to provide an optimal input method based on the user's past feedback.

[0040] The analysis unit can improve the accuracy of the analysis based on past analysis results. For example, the analysis unit improves the analysis accuracy for similar requests based on past analysis results. For example, the analysis unit improves the analysis accuracy for similar requests based on analysis results from when a dinosaur coloring book was generated in the past. The analysis unit can also extract specific patterns from past analysis results and optimize the analysis algorithm. For example, the analysis accuracy for requests related to dinosaur coloring books is improved based on past analysis results. The analysis unit can also minimize analysis errors by referring to past analysis results. For example, the analysis unit adjusts the algorithm to minimize analysis errors based on past analysis results. This makes it possible to improve the analysis accuracy based on past analysis results.

[0041] The analysis unit can apply an appropriate analysis method depending on the category of the request. For example, if the request is about a character, the analysis unit applies an analysis method specialized for the specific character. For example, if the request is about dinosaurs, the analysis unit applies an analysis method specialized for dinosaurs. The analysis unit can also apply a color analysis method if the request is about color preferences. For example, if the request is about the color blue, the analysis unit applies an analysis method specialized for blue. The analysis unit can also apply a theme analysis method if the request is about a theme. For example, if the request is about a seasonal theme, the analysis method specialized for the season is applied. This makes it possible to provide the optimal analysis method depending on the category of the request.

[0042] The analysis unit can improve the accuracy of the analysis based on the user's past request history. For example, the analysis unit improves the analysis accuracy for similar requests based on the user's past request history. For example, if the user has requested many dinosaur coloring books in the past, the analysis accuracy for requests related to dinosaur coloring books can be improved. The analysis unit can also extract specific patterns from the user's past request history and optimize the analysis algorithm. For example, the analysis accuracy for requests related to dinosaur coloring books can be improved based on the user's past request history. The analysis unit can also minimize analysis errors by referring to the user's past request history. For example, the analysis unit adjusts the algorithm to minimize analysis errors based on the user's past request history. This makes it possible to improve the analysis accuracy based on the user's past request history.

[0043] The analysis unit can determine the order of analysis based on the time of request submission. The analysis unit can determine the priority of analysis based on, for example, the time period when the request is submitted. For example, if a request is submitted at night, the request submitted at night is given priority in analysis. The analysis unit can also determine the priority of analysis based on the date when the request is submitted. For example, if a request is submitted on a specific date, the request submitted on that date is given priority in analysis. The analysis unit can also determine the priority of analysis based on the frequency when the request is submitted. For example, if a request is submitted frequently, the request is given priority in analysis. This makes it possible to provide optimal analysis priority based on the time when the request is submitted.

[0044] The analysis unit can change the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analyzing the most relevant requests based on the relevance of the requests. For example, if many requests related to dinosaurs have been submitted, the analysis unit prioritizes analyzing the requests related to dinosaurs. The analysis unit can also postpone less relevant requests based on the relevance of the requests. For example, it prioritizes analyzing more relevant requests while putting less relevant requests off. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. For example, it dynamically changes the order of analysis based on the relevance of the requests. This makes it possible to provide an optimal order of analysis based on the relevance of the requests.

[0045] The analysis unit can change the use of technical terminology in the analysis depending on the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. For example, if the user has technical expertise about dinosaurs, the analysis unit displays the analysis results using detailed technical terminology about dinosaurs. The analysis unit can also display the analysis results in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise about dinosaurs, the analysis unit displays the analysis results in simple language. The analysis unit can also customize the display method of the analysis results depending on the user's level of expertise. For example, the display method of the analysis results can be changed depending on the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0046] The generation unit can change the complexity of the coloring book to be generated based on the requested level of detail. For example, if the request is detailed, the generation unit generates a coloring book with a complex design. For example, if the user requests a detailed dinosaur coloring book, the generation unit generates a complex dinosaur design. The generation unit can also generate a coloring book with a simple design if the request is simple. For example, if the user requests a simple dinosaur coloring book, the generation unit generates a simple dinosaur design. The generation unit can also dynamically adjust the complexity of the coloring book based on the requested level of detail. For example, the generation unit dynamically changes the complexity of the coloring book based on the requested level of detail. This makes it possible to provide a coloring book with optimal complexity according to the requested level of detail.

[0047] The generation unit can apply an appropriate generation algorithm depending on the category of the request. For example, in the case of a request regarding a character, the generation unit applies a generation algorithm specialized for the specific character. For example, in the case of a request regarding a dinosaur, the generation unit applies a generation algorithm specialized for dinosaurs. Furthermore, in the case of a request regarding color preferences, the generation unit can also apply a color generation algorithm. For example, in the case of a request regarding the color blue, the generation unit applies a generation algorithm specialized for blue. Furthermore, in the case of a request regarding a theme, the generation unit can apply a theme generation algorithm. For example, in the case of a request regarding a seasonal theme, the generation unit applies a generation algorithm specialized for the season. This makes it possible to provide the optimal generation algorithm depending on the category of the request.

[0048] The generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, improves the accuracy of generation for similar requests based on the user's past generation results. For example, the generation unit improves the accuracy of generation for similar requests based on the results of the user's past generation of a dinosaur coloring book. The generation unit can also extract specific patterns from the user's past generation results and optimize the generation algorithm. For example, the generation unit improves the accuracy of generation for requests related to dinosaur coloring books based on the user's past generation results. The generation unit can also minimize generation errors by referring to the user's past generation results. For example, the generation unit adjusts the algorithm to minimize generation errors based on the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0049] The generation unit can determine the order of generation based on the time of request submission. The generation unit determines the priority of generation based on, for example, the time zone in which the request is submitted. For example, if a request is submitted at night, the request submitted at night is preferentially generated. The generation unit can also determine the priority of generation based on the date on which the request is submitted. For example, if a request is submitted on a specific date, the request submitted on that date is preferentially generated. The generation unit can also determine the priority of generation based on the frequency with which the request is submitted. For example, if a request is submitted frequently, the request is preferentially generated. This makes it possible to provide optimal priority of generation based on the time of request submission.

[0050] The generation unit can change the order of generation based on the relevance of the requests. For example, the generation unit generates the most relevant requests preferentially based on the relevance of the requests. For example, if many requests related to dinosaurs have been submitted, the generation unit generates requests related to dinosaurs preferentially. The generation unit can also postpone requests with low relevance based on the relevance of the requests. For example, the generation unit generates requests with high relevance preferentially, putting requests with low relevance on hold. The generation unit can also dynamically adjust the order of generation based on the relevance of the requests. For example, the generation order is dynamically changed based on the relevance of the requests. This makes it possible to provide an optimal order of generation based on the relevance of the requests.

[0051] The generation unit can change the level of detail of the coloring book to be generated according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates a coloring book with a detailed design. For example, if the user has specialized knowledge about dinosaurs, the generation unit generates a detailed dinosaur design. The generation unit can also generate a coloring book with a simple design if the user does not have specialized knowledge. For example, if the user does not have specialized knowledge about dinosaurs, the generation unit generates a simple dinosaur design. The generation unit can also dynamically adjust the level of detail of the coloring book according to the user's level of expertise. For example, the generation unit dynamically changes the level of detail of the coloring book based on the user's level of expertise. This makes it possible to provide a coloring book with an optimal level of detail according to the user's level of expertise.

[0052] The providing unit can select the optimal providing method based on the user's past download history. For example, the providing unit suggests similar coloring books based on the user's history of coloring books downloaded in the past. For example, if the user has previously downloaded a dinosaur coloring book, the providing unit preferentially suggests dinosaur coloring books. The providing unit can also extract a specific pattern from the user's past download history and select the optimal providing method. For example, the providing unit preferentially suggests dinosaur coloring books based on the user's past download history. The providing unit can also minimize errors in providing by referring to the user's past download history. For example, the providing unit adjusts an algorithm to minimize errors in providing based on the user's past download history. This makes it possible to provide the optimal providing method based on the user's past download history.

[0053] The providing unit can change the providing method depending on the user's current device. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, it provides a coloring book optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a larger screen. For example, it provides a coloring book optimized for the tablet screen size. Furthermore, if the user is using a personal computer, the providing unit can also provide a providing method that includes detailed information. For example, it provides a coloring book optimized for the personal computer screen size. This makes it possible to provide an optimal providing method that matches the user's current device.

[0054] The providing unit can change the providing method by reflecting user feedback. For example, the providing unit suggests an optimal providing method based on feedback provided by the user regarding the providing method. For example, if the user has previously preferred coloring books in PDF format, the providing unit can preferentially suggest coloring books in PDF format. The providing unit can also extract a specific pattern from the user feedback and optimize the providing method. For example, the coloring book providing method can be optimized based on the user feedback. The providing unit can also minimize providing errors by referring to the user feedback. For example, the providing unit can adjust an algorithm to minimize providing errors based on the user feedback. This makes it possible to provide an optimal providing method based on the user feedback.

[0055] The providing unit can select the optimal providing method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit provides coloring books related to that area. For example, if the user is at a dinosaur museum, the providing unit provides dinosaur coloring books. Furthermore, if the user is traveling, the providing unit can also provide coloring books related to the travel destination. For example, if the user is at the beach, the providing unit can provide sea creature coloring books. Furthermore, if the user is participating in a specific event, the providing unit can also provide coloring books related to the event. For example, if the user is participating in a Halloween event, the providing unit can provide Halloween-themed coloring books. In this way, the optimal providing method can be provided based on the user's geographical location information.

[0056] The providing unit can analyze the user's social media activity and suggest a provision method. For example, the providing unit can provide coloring books related to characters or themes that the user follows on social media. For example, the providing unit can provide characters or themes that are popular among accounts that the user follows. The providing unit can also analyze the content of the user's social media posts and provide related coloring books. For example, if the user posts a lot about dinosaurs, the providing unit can provide dinosaur coloring books. The providing unit can also provide related coloring books based on the activities of the user's friends on social media. For example, if the user's friends enjoy dinosaur coloring books, the providing unit can provide dinosaur coloring books. This makes it possible to provide an optimal provision method based on the user's social media activity.

[0057] The providing unit can change the providing method by reflecting the user's past feedback. For example, the providing unit suggests an optimal providing method based on feedback provided by the user regarding the providing method. For example, if the user has previously preferred coloring books in PDF format, the providing unit can preferentially suggest coloring books in PDF format. The providing unit can also extract a specific pattern from the user's feedback and optimize the providing method. For example, the coloring book providing method can be optimized based on the user's feedback. The providing unit can also minimize the providing error by referring to the user's feedback. For example, the providing unit can adjust an algorithm to minimize the providing error based on the user's feedback. This makes it possible to provide an optimal providing method based on the user's feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can analyze the user's past search history and suggest related coloring books. For example, if the user has frequently searched for dinosaurs in the past, dinosaur coloring books will be suggested first. The analysis unit can also suggest related themes and characters based on the user's past search keywords. For example, if the user has searched for "sea creatures" or "space," related coloring books will be suggested. Furthermore, the analysis unit can extract topics of interest from the user's search history and suggest new coloring book themes. This makes it possible to provide the most suitable coloring books based on the user's interests.

[0060] The providing unit can change the method of providing a coloring book based on the usage status of the user's device. For example, if the user is using a smartphone, a coloring book optimized for the screen size can be provided. Also, if the user is using a tablet, a coloring book optimized for a large screen can be provided. Furthermore, if the user is using a PC, a coloring book including detailed information can be provided. This makes it possible to provide an optimal method of providing a coloring book according to the user's device.

[0061] The analysis unit can analyze the user's social media activity and suggest related coloring books. For example, it can suggest coloring books related to characters or themes that the user follows on social media. It can also analyze the content of the user's posts and suggest related coloring books. It can also suggest related coloring books based on the activities of the user's friends. This makes it possible to provide the most suitable coloring books based on the user's social media activity.

[0062] The providing unit can suggest related coloring books based on the user's geographical location information. For example, if the user is in a specific area, coloring books with characters or themes related to that area can be suggested. If the user is traveling, coloring books related to the travel destination can be suggested. Furthermore, if the user is participating in a specific event, coloring books related to the event can be suggested. This makes it possible to provide the most suitable coloring books based on the user's geographical location information.

[0063] The generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, it can improve the generation accuracy for similar requests based on the designs and themes of coloring books the user has generated in the past. It can also extract specific patterns from the user's past generation results and optimize the generation algorithm. It can also refer to the user's past generation results and adjust the algorithm to minimize generation errors. This makes it possible to provide the optimal coloring book based on the user's past generation results.

[0064] The analysis unit can change the way the analysis results are displayed depending on the user's level of expertise. For example, if the user has specialized knowledge, the analysis results can be displayed using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the analysis results can be displayed in simple terms. Furthermore, the display format of the analysis results can be customized depending on the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The input section allows parents or children to input their coloring requests. For example, they can input specific characters, themes, color preferences, etc. The input section provides various input methods, such as text, audio, and image formats. Step 2: The analysis unit analyzes the request input by the input unit. For example, it uses natural language processing technology to analyze text requests and image analysis technology to analyze image requests. The analysis unit determines what kind of coloring book is appropriate based on the content of the request. Step 3: The generator generates a customized coloring book based on the analysis results of the analyzer. The generator uses a generation AI to generate a coloring book that reflects specific characters, themes, and color preferences. For example, the generator AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a coloring book that meets the user's needs. Step 4: The providing unit makes the generated coloring book available for download. The providing unit allows parents and children to download and print the generated coloring book. For example, the providing unit provides the coloring book in PDF format or image format.

[0067] (Example 2) A customized coloring book generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate a customized coloring book based on a child's preferences and interests. In this system, parents or children input their coloring book requests, and the generation AI analyzes the requests to generate and provide a customized coloring book. For example, parents or children can input specific characters, themes, and color preferences, and the generation AI automatically generates a coloring book based on those requests. The generated coloring book can be downloaded and printed by parents or children, promoting children's creativity and providing a fun learning experience. This allows the customized coloring book generation system to automatically generate and provide coloring books based on children's preferences and interests. For example, parents can easily find coloring books that match their children's interests and preferences, and children can experience the joy of expressing their own world.

[0068] A customized coloring book generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit allows parents or children to input coloring book requests. For example, specific characters, themes, and color preferences can be input. The input unit can provide various input methods, such as text, audio, and image formats. The analysis unit analyzes the requests input by the input unit. For example, it can analyze text requests using natural language processing technology and image requests using image analysis technology. The analysis unit determines what type of coloring book is appropriate based on the content of the request. The generation unit generates a customized coloring book based on the analysis results of the analysis unit. The generation unit uses a generation AI to generate a coloring book that reflects specific characters, themes, and color preferences. For example, the generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a coloring book according to the request. The provision unit makes the generated coloring book available for download. The provision unit allows parents or children to download and print the generated coloring book. For example, the provision unit can provide the coloring book in PDF format or image format. As a result, the customized coloring book creation system according to the embodiment can automatically create and provide a customized coloring book based on the user's requests.

[0069] The input unit can input requests for specific characters, themes, and color preferences. For example, a parent or child can input specific character, theme, and color preferences through the input unit. For example, an anime character, a seasonal theme, or a color palette can be input. The input unit can provide various input methods, such as text, voice, and image formats. For example, a user can simply input "dinosaur coloring book" by voice, and the request is automatically set. In addition, a user can upload an image to generate a coloring book based on that image. This allows a coloring book to be customized based on the user's specific requests.

[0070] The analysis unit can analyze the input request and determine a suitable coloring book. The analysis unit can, for example, analyze a request in text format using natural language processing technology. For example, the analysis unit can analyze the text input by the user and determine what kind of coloring book is suitable. The analysis unit can also analyze a request in image format using image analysis technology. For example, the analysis unit can analyze an image uploaded by the user and extract information for generating a coloring book based on the image. The analysis unit can also analyze a request in audio format using voice analysis technology. For example, the analysis unit can analyze a request input by voice using the voice and determine a suitable coloring book. This makes it possible to perform analysis to generate a coloring book that best suits the user's request.

[0071] The generation unit can generate a customized coloring book based on the analysis results of the analysis unit. The generation unit generates a customized coloring book based on the analysis results of the analysis unit, for example, using a generation AI. For example, the generation AI generates a coloring book according to a user's request using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate a coloring book that reflects specific characters, themes, and color preferences. For example, it can generate a coloring book that includes illustrations of dinosaurs or a coloring book that includes a lot of blue. This makes it possible to generate an appropriate coloring book based on the analysis results.

[0072] The providing unit can make the generated coloring book downloadable. For example, the providing unit can allow a parent or child to download and print the generated coloring book. The providing unit can provide the coloring book in a PDF format or an image format. For example, the generated coloring book can be downloaded in a PDF format and printed. The providing unit can also download the generated coloring book in an image format and display it on a digital device. This allows a user to download and use the generated coloring book.

[0073] The input unit can estimate the user's emotion and change the desired input method based on the estimated user emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal input method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The input unit can analyze past input history and suggest the optimal input method. For example, the input unit automatically displays characters and themes that the user has frequently input in the past as candidates. For example, the input unit preferentially suggests dinosaurs and blue themes that the user has input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input frequently in the past, the input unit preferentially suggests voice input. The input unit can also predict and suggest characters and themes to be used during specific time periods based on the user's past input history. For example, if the user has input a lot of dinosaur coloring pages at night, the input unit suggests dinosaur coloring pages at night. In this way, the optimal input method can be suggested based on the past input history.

[0075] The input unit can present input candidates based on the user's current interests or trends. For example, the input unit can suggest related characters or themes based on topics recently searched by the user. For example, if the user has recently searched for dinosaurs, the input unit can suggest dinosaur coloring books. The input unit can also present input candidates based on trends in accounts the user follows on social media. For example, the input unit can suggest characters or themes that are popular among the accounts the user follows. The input unit can also suggest characters or themes related to products the user recently purchased. For example, if the user purchased a dinosaur toy, the input unit can suggest dinosaur coloring books. In this way, input candidates can be presented based on the user's interests and trends.

[0076] The input unit can provide a variety of input means, such as voice input or image input. For example, the input unit can automatically set a user's request by simply inputting "dinosaur coloring book" by voice. The input unit can also generate a coloring book based on an image uploaded by the user. For example, if a user uploads an image of a dinosaur, a coloring book based on that image is generated. The input unit can also combine voice input and image input to set a more detailed request. For example, if a user inputs "blue dinosaur coloring book" by voice and uploads an image of a dinosaur, a blue dinosaur coloring book is generated. This provides a variety of input means, thereby improving user convenience.

[0077] The input unit can estimate the user's emotions and determine the order of input contents based on the estimated user emotions. For example, the input unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expressions. The input unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the input unit can analyze the tone and speed of the voice and calculate an emotion score. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the input unit can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal priority order for input contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The input unit can present highly relevant input candidates based on the user's geographical location information. For example, if the user is in a specific area, the input unit can suggest characters or themes related to the area. For example, if the user is at a dinosaur museum, the input unit can suggest dinosaur coloring books. Furthermore, if the user is traveling, the input unit can also suggest coloring books related to the travel destination. For example, if the user is at the beach, the input unit can suggest sea creature coloring books. Furthermore, if the user is participating in a specific event, the input unit can also suggest coloring books related to the event. For example, if the user is participating in a Halloween event, the input unit can suggest Halloween-themed coloring books. In this way, highly relevant input candidates can be presented based on the user's geographical location information.

[0079] The input unit can analyze the user's social media activity and present relevant input candidates. For example, the input unit can suggest characters or themes that the user follows on social media as input candidates. For example, the input unit can suggest characters or themes that are popular among accounts that the user follows. The input unit can also analyze the content of the user's social media posts and suggest related coloring books. For example, if the user posts a lot about dinosaurs, the input unit can suggest dinosaur coloring books. The input unit can also suggest related coloring books based on the activity of the user's friends on social media. For example, if the user's friends enjoy dinosaur coloring books, the input unit can suggest dinosaur coloring books. In this way, relevant input candidates can be presented based on the user's social media activity.

[0080] The input unit can change the input method by reflecting the user's past feedback. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, if the user has previously rated dinosaur coloring books, the input unit will preferentially suggest dinosaur coloring books. The input unit can also preferentially display specific characters or themes based on the user's past feedback. For example, if the user has previously preferred blue coloring books, the input unit will preferentially suggest blue coloring books. The input unit can also analyze the user's past feedback and customize the input interface. For example, if the user has previously preferred voice input, the input unit will preferentially suggest voice input. This makes it possible to provide an optimal input method based on the user's past feedback.

[0081] The analysis unit can estimate the user's emotions and change the analysis algorithm based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal analysis algorithm according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The analysis unit can improve the accuracy of the analysis based on past analysis results. For example, the analysis unit improves the analysis accuracy for similar requests based on past analysis results. For example, the analysis unit improves the analysis accuracy for similar requests based on analysis results from when a dinosaur coloring book was generated in the past. The analysis unit can also extract specific patterns from past analysis results and optimize the analysis algorithm. For example, the analysis accuracy for requests related to dinosaur coloring books is improved based on past analysis results. The analysis unit can also minimize analysis errors by referring to past analysis results. For example, the analysis unit adjusts the algorithm to minimize analysis errors based on past analysis results. This makes it possible to improve the analysis accuracy based on past analysis results.

[0083] The analysis unit can apply an appropriate analysis method depending on the category of the request. For example, if the request is about a character, the analysis unit applies an analysis method specialized for the specific character. For example, if the request is about dinosaurs, the analysis unit applies an analysis method specialized for dinosaurs. The analysis unit can also apply a color analysis method if the request is about color preferences. For example, if the request is about the color blue, the analysis unit applies an analysis method specialized for blue. The analysis unit can also apply a theme analysis method if the request is about a theme. For example, if the request is about a seasonal theme, the analysis method specialized for the season is applied. This makes it possible to provide the optimal analysis method depending on the category of the request.

[0084] The analysis unit can improve the accuracy of the analysis based on the user's past request history. For example, the analysis unit improves the analysis accuracy for similar requests based on the user's past request history. For example, if the user has requested many dinosaur coloring books in the past, the analysis accuracy for requests related to dinosaur coloring books can be improved. The analysis unit can also extract specific patterns from the user's past request history and optimize the analysis algorithm. For example, the analysis accuracy for requests related to dinosaur coloring books can be improved based on the user's past request history. The analysis unit can also minimize analysis errors by referring to the user's past request history. For example, the analysis unit adjusts the algorithm to minimize analysis errors based on the user's past request history. This makes it possible to improve the analysis accuracy based on the user's past request history.

[0085] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The analysis unit can determine the order of analysis based on the time of request submission. The analysis unit can determine the priority of analysis based on, for example, the time period when the request is submitted. For example, if a request is submitted at night, the request submitted at night is given priority in analysis. The analysis unit can also determine the priority of analysis based on the date when the request is submitted. For example, if a request is submitted on a specific date, the request submitted on that date is given priority in analysis. The analysis unit can also determine the priority of analysis based on the frequency when the request is submitted. For example, if a request is submitted frequently, the request is given priority in analysis. This makes it possible to provide optimal analysis priority based on the time when the request is submitted.

[0087] The analysis unit can change the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analyzing the most relevant requests based on the relevance of the requests. For example, if many requests related to dinosaurs have been submitted, the analysis unit prioritizes analyzing the requests related to dinosaurs. The analysis unit can also postpone less relevant requests based on the relevance of the requests. For example, it prioritizes analyzing more relevant requests while putting less relevant requests off. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. For example, it dynamically changes the order of analysis based on the relevance of the requests. This makes it possible to provide an optimal order of analysis based on the relevance of the requests.

[0088] The analysis unit can change the use of technical terminology in the analysis depending on the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. For example, if the user has technical expertise about dinosaurs, the analysis unit displays the analysis results using detailed technical terminology about dinosaurs. The analysis unit can also display the analysis results in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise about dinosaurs, the analysis unit displays the analysis results in simple language. The analysis unit can also customize the display method of the analysis results depending on the user's level of expertise. For example, the display method of the analysis results can be changed depending on the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0089] The generation unit can estimate the user's emotions and change the coloring book design to be generated based on the estimated user emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal coloring book design based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The generation unit can change the complexity of the coloring book to be generated based on the requested level of detail. For example, if the request is detailed, the generation unit generates a coloring book with a complex design. For example, if the user requests a detailed dinosaur coloring book, the generation unit generates a complex dinosaur design. The generation unit can also generate a coloring book with a simple design if the request is simple. For example, if the user requests a simple dinosaur coloring book, the generation unit generates a simple dinosaur design. The generation unit can also dynamically adjust the complexity of the coloring book based on the requested level of detail. For example, the generation unit dynamically changes the complexity of the coloring book based on the requested level of detail. This makes it possible to provide a coloring book with optimal complexity according to the requested level of detail.

[0091] The generation unit can apply an appropriate generation algorithm depending on the category of the request. For example, in the case of a request regarding a character, the generation unit applies a generation algorithm specialized for the specific character. For example, in the case of a request regarding a dinosaur, the generation unit applies a generation algorithm specialized for dinosaurs. Furthermore, in the case of a request regarding color preferences, the generation unit can also apply a color generation algorithm. For example, in the case of a request regarding the color blue, the generation unit applies a generation algorithm specialized for blue. Furthermore, in the case of a request regarding a theme, the generation unit can apply a theme generation algorithm. For example, in the case of a request regarding a seasonal theme, the generation unit applies a generation algorithm specialized for the season. This makes it possible to provide the optimal generation algorithm depending on the category of the request.

[0092] The generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, improves the accuracy of generation for similar requests based on the user's past generation results. For example, the generation unit improves the accuracy of generation for similar requests based on the results of the user's past generation of a dinosaur coloring book. The generation unit can also extract specific patterns from the user's past generation results and optimize the generation algorithm. For example, the generation unit improves the accuracy of generation for requests related to dinosaur coloring books based on the user's past generation results. The generation unit can also minimize generation errors by referring to the user's past generation results. For example, the generation unit adjusts the algorithm to minimize generation errors based on the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0093] The generation unit can estimate the user's emotions and change the coloring of the generated coloring book based on the estimated user's emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal coloring book coloring based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The generation unit can determine the order of generation based on the time of request submission. The generation unit determines the priority of generation based on, for example, the time zone in which the request is submitted. For example, if a request is submitted at night, the request submitted at night is preferentially generated. The generation unit can also determine the priority of generation based on the date on which the request is submitted. For example, if a request is submitted on a specific date, the request submitted on that date is preferentially generated. The generation unit can also determine the priority of generation based on the frequency with which the request is submitted. For example, if a request is submitted frequently, the request is preferentially generated. This makes it possible to provide optimal priority of generation based on the time of request submission.

[0095] The generation unit can change the order of generation based on the relevance of the requests. For example, the generation unit generates the most relevant requests preferentially based on the relevance of the requests. For example, if many requests related to dinosaurs have been submitted, the generation unit generates requests related to dinosaurs preferentially. The generation unit can also postpone requests with low relevance based on the relevance of the requests. For example, the generation unit generates requests with high relevance preferentially, putting requests with low relevance on hold. The generation unit can also dynamically adjust the order of generation based on the relevance of the requests. For example, the generation order is dynamically changed based on the relevance of the requests. This makes it possible to provide an optimal order of generation based on the relevance of the requests.

[0096] The generation unit can change the level of detail of the coloring book to be generated according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates a coloring book with a detailed design. For example, if the user has specialized knowledge about dinosaurs, the generation unit generates a detailed dinosaur design. The generation unit can also generate a coloring book with a simple design if the user does not have specialized knowledge. For example, if the user does not have specialized knowledge about dinosaurs, the generation unit generates a simple dinosaur design. The generation unit can also dynamically adjust the level of detail of the coloring book according to the user's level of expertise. For example, the generation unit dynamically changes the level of detail of the coloring book based on the user's level of expertise. This makes it possible to provide a coloring book with an optimal level of detail according to the user's level of expertise.

[0097] The providing unit can estimate the user's emotions and change the display method of the coloring book to be provided based on the estimated user's emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal coloring book display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The providing unit can select the optimal providing method based on the user's past download history. For example, the providing unit suggests similar coloring books based on the user's history of coloring books downloaded in the past. For example, if the user has previously downloaded a dinosaur coloring book, the providing unit preferentially suggests dinosaur coloring books. The providing unit can also extract a specific pattern from the user's past download history and select the optimal providing method. For example, the providing unit preferentially suggests dinosaur coloring books based on the user's past download history. The providing unit can also minimize errors in providing by referring to the user's past download history. For example, the providing unit adjusts an algorithm to minimize errors in providing based on the user's past download history. This makes it possible to provide the optimal providing method based on the user's past download history.

[0099] The providing unit can change the providing method depending on the user's current device. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, it provides a coloring book optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a larger screen. For example, it provides a coloring book optimized for the tablet screen size. Furthermore, if the user is using a personal computer, the providing unit can also provide a providing method that includes detailed information. For example, it provides a coloring book optimized for the personal computer screen size. This makes it possible to provide an optimal providing method that matches the user's current device.

[0100] The providing unit can change the providing method by reflecting user feedback. For example, the providing unit suggests an optimal providing method based on feedback provided by the user regarding the providing method. For example, if the user has previously preferred coloring books in PDF format, the providing unit can preferentially suggest coloring books in PDF format. The providing unit can also extract a specific pattern from the user feedback and optimize the providing method. For example, the coloring book providing method can be optimized based on the user feedback. The providing unit can also minimize providing errors by referring to the user feedback. For example, the providing unit can adjust an algorithm to minimize providing errors based on the user feedback. This makes it possible to provide an optimal providing method based on the user feedback.

[0101] The providing unit can estimate the user's emotions and determine the order of coloring pages to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal coloring order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The providing unit can select the optimal providing method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit provides coloring books related to that area. For example, if the user is at a dinosaur museum, the providing unit provides dinosaur coloring books. Furthermore, if the user is traveling, the providing unit can also provide coloring books related to the travel destination. For example, if the user is at the beach, the providing unit can provide sea creature coloring books. Furthermore, if the user is participating in a specific event, the providing unit can also provide coloring books related to the event. For example, if the user is participating in a Halloween event, the providing unit can provide Halloween-themed coloring books. In this way, the optimal providing method can be provided based on the user's geographical location information.

[0103] The providing unit can analyze the user's social media activity and suggest a provision method. For example, the providing unit can provide coloring books related to characters or themes that the user follows on social media. For example, the providing unit can provide characters or themes that are popular among accounts that the user follows. The providing unit can also analyze the content of the user's social media posts and provide related coloring books. For example, if the user posts a lot about dinosaurs, the providing unit can provide dinosaur coloring books. The providing unit can also provide related coloring books based on the activities of the user's friends on social media. For example, if the user's friends enjoy dinosaur coloring books, the providing unit can provide dinosaur coloring books. This makes it possible to provide an optimal provision method based on the user's social media activity.

[0104] The providing unit can change the providing method by reflecting the user's past feedback. For example, the providing unit suggests an optimal providing method based on feedback provided by the user regarding the providing method. For example, if the user has previously preferred coloring books in PDF format, the providing unit can preferentially suggest coloring books in PDF format. The providing unit can also extract a specific pattern from the user's feedback and optimize the providing method. For example, the coloring book providing method can be optimized based on the user's feedback. The providing unit can also minimize the providing error by referring to the user's feedback. For example, the providing unit can adjust an algorithm to minimize the providing error based on the user's feedback. This makes it possible to provide an optimal providing method based on the user's feedback. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized using the reception device 38 of the smart device 14. For example, text-format requests are input via the touch panel 38A, and audio-format requests are input via the microphone 38B. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input requests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized coloring book using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and makes the generated coloring book available for download. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized using the microphone 238 of the smart glasses 214. For example, a request in audio format is input via the microphone 238. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input request. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized coloring book using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and makes the generated coloring book available for download. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit is realized using the microphone 238 of the headset type terminal 314. For example, a request in audio format is input via the microphone 238. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input request. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized coloring book using a generation AI. The provision unit is realized, for example, by the display 343 of the headset type terminal 314, and makes the generated coloring book available for download. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized using the microphone 238 of the robot 414. For example, a request in voice format is input via the microphone 238. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input request. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized coloring book using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and makes the generated coloring book available for download.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can analyze the user's past search history and suggest related coloring books. For example, if the user has frequently searched for dinosaurs in the past, dinosaur coloring books will be suggested first. The analysis unit can also suggest related themes and characters based on the user's past search keywords. For example, if the user has searched for "sea creatures" or "space," related coloring books will be suggested. Furthermore, the analysis unit can extract topics of interest from the user's search history and suggest new coloring book themes. This makes it possible to provide the most suitable coloring books based on the user's interests.

[0107] The generation unit can estimate the user's emotions and adjust the difficulty of the coloring book based on the estimated emotions. For example, if the user is relaxed, a coloring book with a complex design can be provided. Alternatively, if the user is stressed, a coloring book with a simple, relaxing design can be provided. Furthermore, the generation unit can suggest color options according to the user's emotions. For example, if the user is feeling happy, a coloring book containing many bright colors can be provided. This makes it possible to provide the optimal coloring book according to the user's emotions.

[0108] The providing unit can change the method of providing a coloring book based on the usage status of the user's device. For example, if the user is using a smartphone, a coloring book optimized for the screen size can be provided. Also, if the user is using a tablet, a coloring book optimized for a large screen can be provided. Furthermore, if the user is using a PC, a coloring book including detailed information can be provided. This makes it possible to provide an optimal method of providing a coloring book according to the user's device.

[0109] The analysis unit can analyze the user's social media activity and suggest related coloring books. For example, it can suggest coloring books related to characters or themes that the user follows on social media. It can also analyze the content of the user's posts and suggest related coloring books. It can also suggest related coloring books based on the activities of the user's friends. This makes it possible to provide the most suitable coloring books based on the user's social media activity.

[0110] The generation unit can estimate the user's emotions and change the theme of the coloring book based on the estimated emotions. For example, if the user is feeling sad, a coloring book with an uplifting theme can be provided. Alternatively, if the user is feeling excited, a coloring book with a calming theme can be provided. Furthermore, the design and coloring of the coloring book can be adjusted according to the user's emotions. This makes it possible to provide the most suitable coloring book according to the user's emotions.

[0111] The providing unit can suggest related coloring books based on the user's geographical location information. For example, if the user is in a specific area, coloring books with characters or themes related to that area can be suggested. If the user is traveling, coloring books related to the travel destination can be suggested. Furthermore, if the user is participating in a specific event, coloring books related to the event can be suggested. This makes it possible to provide the most suitable coloring books based on the user's geographical location information.

[0112] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be displayed. Alternatively, if the user is feeling stressed, concise analysis results can be displayed. Furthermore, the display format of the analysis results can be changed depending on the user's emotions. This makes it possible to provide an optimal display method of the analysis results according to the user's emotions.

[0113] The generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, it can improve the generation accuracy for similar requests based on the designs and themes of coloring books the user has generated in the past. It can also extract specific patterns from the user's past generation results and optimize the generation algorithm. It can also refer to the user's past generation results and adjust the algorithm to minimize generation errors. This makes it possible to provide the optimal coloring book based on the user's past generation results.

[0114] The providing unit can estimate the user's emotions and change the order in which coloring books are provided based on the estimated emotions. For example, if the user is relaxed, coloring books with complex designs can be provided preferentially. Also, if the user is stressed, coloring books with simple designs can be provided preferentially. Furthermore, the order in which coloring books are provided can be dynamically adjusted according to the user's emotions. This makes it possible to provide the optimal order in which coloring books are provided based on the user's emotions.

[0115] The analysis unit can change the way the analysis results are displayed depending on the user's level of expertise. For example, if the user has specialized knowledge, the analysis results can be displayed using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the analysis results can be displayed in simple terms. Furthermore, the display format of the analysis results can be customized depending on the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The input section allows parents or children to input their coloring requests. For example, they can input specific characters, themes, color preferences, etc. The input section provides various input methods, such as text, audio, and image formats. Step 2: The analysis unit analyzes the request input by the input unit. For example, it uses natural language processing technology to analyze text requests and image analysis technology to analyze image requests. The analysis unit determines what kind of coloring book is appropriate based on the content of the request. Step 3: The generator generates a customized coloring book based on the analysis results of the analyzer. The generator uses a generation AI to generate a coloring book that reflects specific characters, themes, and color preferences. For example, the generator AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a coloring book that meets the user's needs. Step 4: The providing unit makes the generated coloring book available for download. The providing unit allows parents and children to download and print the generated coloring book. For example, the providing unit provides the coloring book in PDF format or image format.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting requests; an analysis unit that analyzes the request input by the input unit; a generation unit that generates a customized coloring book based on the needs analyzed by the analysis unit; a providing unit that provides the coloring book generated by the generating unit; Equipped with A system characterized by:

2. The input unit Enter your preferences for specific characters, themes, and colors 2. The system of claim 1.

3. The analysis unit Analyze the input request and determine the appropriate coloring book 2. The system of claim 1.

4. The generation unit A customized coloring book is generated based on the analysis result of the analysis unit.

2. The system of claim 1.

5. The providing unit Make the generated coloring book available for download 2. The system of claim 1.

6. The input unit Estimate the user's emotions and change the way they input their requests based on the estimated emotions.

2. The system of claim 1.

7. The input unit Analyzes past input history and suggests appropriate input methods 2. The system of claim 1.

8. The input unit Suggests input based on the user's current interests or trends 2. The system of claim 1.

Images