system

Abstract

The system according to this embodiment aims to enable users to easily create picture books. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an editing unit, and a storage unit. The reception unit receives input from the user regarding the theme or characters of the picture book and an outline of the story. The generation unit generates the text and illustrations of the picture book based on the information received by the reception unit. The editing unit allows the user to edit the picture book generated by the generation unit. The storage unit stores the picture book edited by the editing unit in print or digital format.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time and labor for a user to create a picture book.

[0005] The system according to the embodiment aims to enable a user to easily create a picture book.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an editing unit, and a storage unit. The reception unit receives input from the user regarding the theme or characters of the picture book and an outline of the story. The generation unit generates the text and illustrations of the picture book based on the information received by the reception unit. The editing unit allows the user to edit the picture book generated by the generation unit. The storage unit stores the picture book edited by the editing unit in print or digital format. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily create picture books. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor २8, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The picture book creation support system according to an embodiment of the present invention is a system in which a user inputs the theme, characters, and story outline of a picture book, a generating AI generates the text and illustrations of the picture book based on that input, the user edits them, and finally saves them. In this system, the user inputs the theme, characters, and story outline of a picture book, and the generating AI generates the text and illustrations of the picture book based on that input. The generated picture book can be edited and modified by the user, and the final completed picture book can be printed or saved in digital format. For example, the user can input a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." As a story outline, the user can input something like "An adventure in which Peter and his friends go on a treasure hunt" or "A story in which Emily overcomes difficulties using her magical powers." Next, the generating AI generates the text and illustrations of the picture book based on the input theme, characters, and story outline. The generating AI analyzes the user's input and automatically creates appropriate text and illustrations. For example, based on the story "An adventure in which Peter and his friends go on a treasure hunt," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. The generated picture books can be edited and modified by the user. For example, users can change parts of the generated text or adjust the colors and placement of illustrations. This allows users to create their own original picture books. Finally, the completed picture book can be saved in print or digital format. Printed picture books can be read together with children, and digital picture books can be viewed on smartphones and tablets. This allows families to create many favorite picture books together. This service allows parents and children to spend enjoyable time together while nurturing creativity. Furthermore, because it utilizes generation AI, it is possible to easily create high-quality picture books, and it is expected to be used by many families. In short, the picture book creation support system allows users to easily create and save high-quality picture books.

[0029] The picture book creation support system according to this embodiment comprises a reception unit, a generation unit, an editing unit, and a storage unit. The reception unit receives input from the user regarding the theme, characters, and story outline of the picture book. For example, the user can input a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." As a story outline, the user can input something like "An adventure in which Peter and his friends go on a treasure hunt" or "A story in which Emily overcomes difficulties using her magical powers." The generation unit uses a generation AI to generate the text and illustrations of the picture book based on the information received by the reception unit. The generation AI analyzes the user's input using, for example, a text generation AI (e.g., LLM) or an image generation AI, and automatically creates appropriate text and illustrations. For example, based on the story "An adventure in which Peter and his friends go on a treasure hunt," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. The editing unit has a function that allows the user to edit the picture book generated by the generation unit. For example, the user can change parts of the generated text or adjust the color and placement of the illustrations. This allows users to create their own original picture books. The storage section includes the ability to print or save the picture books edited by the editorial department in digital format. For example, the completed picture book can be printed and read with children, and the digital version can be viewed on smartphones and tablets. In this way, the picture book creation support system allows users to easily create and save high-quality picture books.

[0030] The generation unit can generate picture book text and illustrations using a generation AI. For example, the generation unit uses a text generation AI (e.g., LLM) to generate picture book text based on user input. For example, based on a story entered by the user, such as "Peter's adventure to find treasure with his friends," the generation AI generates appropriate text. The generation unit also uses an image generation AI to generate picture book illustrations based on user input. For example, it can generate illustrations of Peter and his friends searching for treasure, or the moment they find the treasure. In this way, by using a generation AI, picture book text and illustrations can be automatically generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can analyze user input and generate text and illustrations using an algorithm for generating appropriate text and illustrations. In this way, the generation unit can automatically generate picture book text and illustrations based on user input.

[0031] The generation unit can analyze user input using a generation AI and automatically create text and illustrations. For example, the generation unit analyzes user input using natural language processing technology. For instance, based on a story input by the user, such as "Peter's treasure hunt adventure with his friends," the generation AI generates appropriate text. The generation unit also uses image analysis technology to generate illustrations for the picture book based on user input. For example, it can generate illustrations of Peter and his friends searching for treasure, or the moment they find it. This allows the generation AI to analyze user input and automatically create appropriate text and illustrations. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can generate text and illustrations using an algorithm that analyzes user input and generates appropriate text and illustrations. This allows the generation unit to automatically generate picture book text and illustrations based on user input.

[0032] The editorial department may have a function that allows users to modify parts of the generated text. For example, the editorial department may provide an interface for modifying parts of the generated text. For example, users may select and modify parts of the generated text. The editorial department may also provide a tool for modifying parts of the generated text. For example, it may provide an editor for users to modify parts of the generated text. This allows users to modify parts of the generated text. Some or all of the above processing in the editorial department may be performed using, for example, a generation AI, or not using a generation AI. For example, the editorial department may modify the text using an algorithm for modifying parts of the generated text. This allows the editorial department to enable users to modify parts of the generated text.

[0033] The editorial department may have a function to adjust the color or placement of the generated illustrations. For example, the editorial department may provide an interface for adjusting the color of the generated illustrations. For example, the user can select and change the color of the generated illustrations. The editorial department may also provide a tool for adjusting the placement of the generated illustrations. For example, it may provide an editor for the user to change the placement of the generated illustrations. This allows the user to adjust the color and placement of the generated illustrations. Some or all of the above processing in the editorial department may be performed using, for example, a generation AI, or not using a generation AI. For example, the editorial department may adjust the illustrations using an algorithm for adjusting the color and placement of the generated illustrations. This allows the editorial department to adjust the color and placement of the generated illustrations.

[0034] The storage unit may have the functionality to print or digitally save the completed picture book. For example, the storage unit may provide an interface for printing the completed picture book. For example, the user can select an option to print the completed picture book and then print it. The storage unit may also provide a tool for digitally saving the completed picture book. For example, the user can select an option to save the completed picture book in PDF or JPEG format and then save it. This allows the user to print or digitally save the completed picture book. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit may perform the saving process using an algorithm for saving the completed picture book. This allows the storage unit to allow the user to print or digitally save the completed picture book.

[0035] The reception desk can analyze the user's past input history and assist in selecting a theme or character. For example, the reception desk can store the user's past input history in a database and use an analysis algorithm to suggest the most suitable theme or character. For example, it can suggest similar themes or characters based on themes or characters the user has selected in the past. The reception desk can also analyze the trends in picture books the user has created in the past and suggest related themes or characters. For example, if the theme of a picture book the user has created in the past is "animal adventures," it will suggest an animal-related theme for the next time. The reception desk can also suggest themes or characters related to specific seasons or events based on the user's past input history. For example, during the Christmas season, it will suggest Christmas-related themes or characters. This allows the reception desk to suggest the most suitable theme or character based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input data into a generative AI and have the generative AI suggest the most suitable theme or character. This allows the reception desk to suggest the most suitable theme or character based on the user's past input history.

[0036] The reception desk can provide story templates based on the user's age or interests when they input data. For example, the reception desk can obtain the user's age and interests from their profile information and provide an appropriate story template based on that. For example, if the user is a child, it can provide a simple and easy-to-understand story template. The reception desk can also provide story templates with complex and deep themes if the user is an adult. For example, if the user is an adult, it can provide a story template related to history or philosophy. The reception desk can also provide story templates related to animals or magic based on the user's interests. For example, if the user is interested in animals, it can provide a story template with an animal as the main character. This allows the reception desk to provide an appropriate story template based on the user's age and interests. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's profile information into a generative AI and have the generative AI provide an appropriate story template. This allows the reception desk to provide an appropriate story template based on the user's age and interests.

[0037] The reception desk can suggest themes and characters related to the user's region, taking into account the user's geographical location. For example, the reception desk can obtain the user's geographical location from GPS data or IP address and suggest themes and characters related to the region based on that information. For example, if the user is in Japan, it will suggest themes and characters related to Japanese traditions and culture. The reception desk can also suggest themes and characters related to American history and landscapes if the user is in the United States. For example, if the user is in the United States, it will suggest stories themed around historical figures and landscapes of America. The reception desk can also suggest themes and characters related to European myths and legends if the user is in Europe. For example, if the user is in Europe, it will suggest stories themed around European myths and legends. This allows the reception desk to suggest appropriate themes and characters based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI suggest themes and characters related to the region. This allows the reception desk to suggest appropriate themes and characters based on the user's geographical location.

[0038] The reception desk can analyze a user's social media activity and suggest relevant themes and characters. For example, the reception desk can store the user's social media activity in a database and suggest the most suitable themes and characters using an analysis algorithm. For instance, it can suggest relevant themes and characters based on the content the user frequently shares on social media. The reception desk can also analyze the trends of the accounts the user follows and suggest themes and characters that match their interests. For example, if a user follows many animal-related accounts, it can suggest a story with animals as the main characters. Furthermore, the reception desk can suggest the most suitable themes and characters based on the time of day the user is active on social media. For example, if a user is mostly active at night, it can suggest a story with many nighttime scenes. This allows the reception desk to suggest appropriate themes and characters based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest the most suitable themes and characters. This allows the reception desk to suggest appropriate themes and characters based on the user's social media activity.

[0039] The generation unit can generate more personalized text and illustrations by referencing the user's past input data during generation. For example, the generation unit can store the user's past input data in a database and refer to it during generation. For example, it can introduce a character into a new story based on the characteristics of a character previously entered by the user. The generation unit can also generate new stories related to themes previously selected by the user. For example, if the user previously created a picture book with the theme of "animal adventures," the next time it will generate a story related to animals. The generation unit can also generate text and illustrations related to specific seasons or events from the user's past input data. For example, during the Christmas season, it will generate a story related to Christmas. This allows for the generation of more personalized text and illustrations based on the user's past input data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's past input data into a generation AI and have the generation AI perform the generation of personalized text and illustrations. This allows the generation unit to generate more personalized text and illustrations based on the user's past input data.

[0040] The generation unit can generate more specific illustrations based on the detailed settings of the character specified by the user during the generation process. For example, the generation unit can store the detailed settings of the character specified by the user in a database and refer to it during generation. For example, it can generate an illustration that accurately reflects the clothing and facial expressions of the character specified by the user. The generation unit can also generate an illustration that accurately depicts the background and environment of the character specified by the user. For example, it can generate an illustration that specifically represents the actions and poses of the character specified by the user. This allows the generation unit to generate more specific illustrations based on the detailed settings of the character specified by the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the detailed settings of the character specified by the user into a generation AI and have the generation AI perform the generation of specific illustrations. This allows the generation unit to generate more specific illustrations based on the detailed settings of the character specified by the user.

[0041] The generation unit can reflect region-related elements in text and illustrations, taking into account the user's geographical location information during generation. For example, the generation unit can obtain the user's geographical location information from GPS data or IP address and reflect region-related elements in text and illustrations based on that information. For example, if the user is in Japan, it will generate text and illustrations that reflect Japanese scenery and culture. The generation unit can also generate text and illustrations that reflect American scenery and culture if the user is in the United States. For example, if the user is in the United States, it will generate a story themed on historical figures and landscapes of America. The generation unit can also generate text and illustrations that reflect European scenery and culture if the user is in Europe. For example, if the user is in Europe, it will generate a story themed on European myths and legends. This allows region-related elements to be reflected in text and illustrations based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the reflection of region-related elements. This allows the generation unit to incorporate region-related elements into text and illustrations based on the user's geographical location information.

[0042] The generation unit can analyze the user's social media activity during generation and reflect relevant elements in the text and illustrations. For example, the generation unit can store the user's social media activity in a database and refer to it during generation. For example, it can generate relevant text and illustrations based on the content the user frequently shares on social media. The generation unit can also analyze the trends of the accounts the user follows and generate text and illustrations that match their interests. For example, if the user follows many animal-related accounts, it can generate a story with animals as the main characters. The generation unit can also generate optimal text and illustrations based on the time of day the user is active on social media. For example, if the user is often active at night, it can generate a story with many nighttime scenes. This allows the generation unit to reflect relevant elements in the text and illustrations based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the reflection of relevant elements. This allows the generation unit to reflect relevant elements in the text and illustrations based on the user's social media activity.

[0043] The editorial team can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial team can store the user's past editing history in a database and refer to it during editing. For example, it can suggest similar editing methods based on the user's past edits. The editorial team can also prioritize displaying editing tools and options that the user has used in the past. For example, it can prioritize displaying fonts and colors that the user has used in the past. Furthermore, the editorial team can analyze specific patterns from the user's past editing history and suggest the optimal editing method. For example, it can suggest the optimal editing method based on the editing options the user has used most often in the past. This allows the editorial team to suggest the optimal editing method based on the user's past editing history. Some or all of the above processes in the editorial team may be performed using, for example, generative AI, or not. For example, the editorial team can input the user's past editing data into a generative AI and have the generative AI suggest the optimal editing method. This allows the editorial team to suggest the optimal editing method based on the user's past editing history.

[0044] The editorial department can provide more specific editing options based on the detailed settings of the user-specified character during editing. For example, the editorial department can store the detailed settings of the user-specified character in a database and refer to it during editing. For example, it can provide options to edit the user-specified character's clothing and facial expressions in detail. The editorial department can also provide options to edit the user-specified character's background and environment in detail. For example, it can provide options to edit the user-specified character's movements and poses in detail. This allows the editorial department to provide more specific editing options based on the detailed settings of the user-specified character. Some or all of the above processing in the editorial department may be performed using, for example, a generative AI, or not using a generative AI. For example, the editorial department can input the detailed settings of the user-specified character into a generative AI and have the generative AI provide specific editing options. This allows the editorial department to provide more specific editing options based on the detailed settings of the user-specified character.

[0045] The editorial team can provide region-related editing options by considering the user's geographical location during the editing process. For example, the editorial team can obtain the user's geographical location from GPS data or IP address and provide region-related editing options based on that information. For instance, if the user is in Japan, the editorial team can provide editing options that reflect Japanese landscapes and culture. Similarly, if the user is in the United States, the editorial team can provide editing options that reflect American landscapes and culture. For example, if the user is in the United States, the editorial team can provide editing options themed around historical figures and landscapes of the United States. Furthermore, if the user is in Europe, the editorial team can provide editing options that reflect European landscapes and culture. For example, if the user is in Europe, the editorial team can provide editing options themed around European myths and legends. This allows the editorial team to provide region-related editing options based on the user's geographical location. Some or all of the above processing by the editorial team may be performed using, for example, generative AI, or without generative AI. For example, the editorial team can input the user's geographical location information into a generative AI and have the generative AI provide region-related editing options. This allows the editorial team to provide region-related editing options based on the user's geographical location.

[0046] The editorial team can analyze users' social media activity during the editing process and provide relevant editing options. For example, the editorial team can store users' social media activity in a database and refer to it during editing. For instance, they can provide relevant editing options based on what users frequently share on social media. The editorial team can also analyze the trends of accounts users follow and provide editing options that match their interests. For example, if a user follows many animal-related accounts, they can offer the option to edit a story featuring animals. The editorial team can also provide optimal editing options based on the time of day users are active on social media. For example, if a user is mostly active at night, they can offer the option to edit a story with many nighttime scenes. This allows the editorial team to provide relevant editing options based on users' social media activity. Some or all of the above processes in the editorial team may be performed using, for example, generative AI, or not. For example, the editorial team can input user social media data into a generative AI and have the generative AI provide relevant editing options. This allows the editorial team to provide relevant editing options based on users' social media activity.

[0047] The storage unit can suggest the optimal storage method by referring to the user's past storage history when saving. For example, the storage unit can store the user's past storage history in a database and refer to it when saving. For example, if the user has previously saved in digital format, it will suggest saving in the same format. The storage unit can also suggest saving in the same format if the user has previously saved in print format. For example, if the user has previously saved in hardcover format, it will suggest saving in hardcover format again next time. The storage unit can also suggest storage methods tailored to specific events or seasons based on the user's past storage history. For example, during Christmas, it will suggest Christmas-related storage methods. This allows the storage unit to suggest the optimal storage method based on the user's past storage history. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the storage unit can input the user's past storage data into a generating AI and have the generating AI suggest the optimal storage method. This allows the storage unit to suggest the optimal storage method based on the user's past storage history.

[0048] The storage unit can provide more specific saving options based on the user-specified format during saving. For example, the storage unit can store the user-specified format in a database and refer to it during saving. For example, it can provide saving options based on the user-specified format (e.g., PDF or JPEG). The storage unit can also provide saving options based on the user-specified resolution and size. For example, it can provide the optimal saving option based on the user-specified resolution and size. The storage unit can also provide saving options based on the user-specified print format (e.g., hardcover or softcover). For example, it can provide the optimal saving option based on the user-specified print format. This allows the storage unit to provide more specific saving options based on the user-specified format. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the storage unit can input the user-specified format into a generating AI and have the generating AI provide specific saving options. This allows the storage unit to provide more specific saving options based on the user-specified format.

[0049] The storage unit can provide region-related storage options when saving, taking into account the user's geographical location information. For example, the storage unit can obtain the user's geographical location information from GPS data or IP address and provide region-related storage options based on that information. For example, if the user is in Japan, it can provide storage options using Japanese printing companies. The storage unit can also provide storage options using American printing companies if the user is in the United States. For example, if the user is in the United States, it can provide storage options using American printing companies. The storage unit can also provide storage options using European printing companies if the user is in Europe. For example, if the user is in Europe, it can provide storage options using European printing companies. This allows the storage unit to provide region-related storage options based on the user's geographical location information. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the user's geographical location information into a generative AI and have the generative AI perform the provision of region-related storage options. This allows the storage unit to provide region-related storage options based on the user's geographical location information.

[0050] The storage unit can analyze the user's social media activity and provide relevant storage options when saving. For example, the storage unit can store the user's social media activity in a database and refer to it when saving. For example, it can provide relevant storage options based on the content the user frequently shares on social media. The storage unit can also analyze the trends of the accounts the user follows and provide storage options that match their interests. For example, if the user follows many animal-related accounts, it can provide an option to save stories featuring animals. The storage unit can also provide optimal storage options based on the time of day the user is active on social media. For example, if the user is often active at night, it can provide an option to save stories with many nighttime scenes. This allows the storage unit to provide relevant storage options based on the user's social media activity. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit can input the user's social media data into a generative AI and have the generative AI provide relevant storage options. This allows the storage unit to provide relevant storage options based on the user's social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The input system can suggest quotes from relevant literary works and films based on the user's input. For example, if a user enters the theme "A Tale of the Enchanted Forest," the input system can suggest quotes from related works such as "Harry Potter" and "The Lord of the Rings." Similarly, if a user enters the theme "Animals' Adventures," the input system can suggest quotes from works such as "The Lion King" and "The Jungle Book." This allows users to gain inspiration for creating richer stories. Furthermore, the input system can also suggest quotes from relevant characters based on the character entered by the user. For example, if a user enters the character "Peter the Rabbit," the input system can suggest quotes from "Peter Rabbit." This allows users to gain reference information to make their character's setting more concrete.

[0053] The generation unit can generate music and sound effects that correspond to the story's progression based on user input. For example, if a user inputs a story about "Peter's treasure hunt adventure with his friends," the generation unit can generate exciting music to match the adventure scenes and sound effects for the moment the treasure is found. Similarly, if a user inputs a story about "Emily overcoming difficulties using her magical powers," the generation unit can generate mystical music to match the magic scenes and sound effects for the moment magic is used. This allows users to create more immersive picture books. Furthermore, the generation unit can also generate theme music for characters based on the characters entered by the user. For example, if a user inputs the character "Peter the rabbit," the generation unit can generate Peter's theme music. This allows users to further highlight the character's personality.

[0054] The editorial team can implement a feature that allows users to receive feedback on the text and illustrations they generate. For example, users can share parts of their picture books with other users and receive comments and ratings. The editorial team can also suggest improvements to the text and illustrations based on feedback from other users. This allows users to incorporate the opinions of others and create better picture books. Furthermore, the editorial team can analyze feedback from other users and extract common areas for improvement. For example, if many users are requesting improvements to the same part, the editorial team can suggest focusing on improving that part. This allows users to improve their picture books efficiently.

[0055] The editorial team can provide a feature that allows users to apply different art styles to user-generated illustrations. For example, users can select a user-generated illustration and apply different art styles such as anime, realistic, or comic book style. The editorial team can also provide tools to change the color tone and texture of user-generated illustrations. For example, users can change the color tone or add textures to their user-generated illustrations. This allows users to customize their illustrations to their liking. Furthermore, the editorial team can provide options to apply filters and effects to user-generated illustrations. For example, users can apply blur or glitch effects to their user-generated illustrations. This allows users to add their own unique style to their illustrations.

[0056] The storage function can include a feature that allows users to save completed picture books to cloud storage. For example, users can upload their completed picture books to cloud storage and access them anytime, anywhere. The storage function can also generate a link to share the picture books saved in cloud storage with other users. For example, users can send the generated link to their picture books to friends and family for easy sharing. This allows users to easily save and share their picture books. Furthermore, the storage function can also provide a version control function for picture books saved in cloud storage. For example, each time a user edits a picture book, a new version is saved, and they can revert to previous versions. This allows users to manage the editing history of their picture books and refer to past versions as needed.

[0057] The input system can analyze a user's past input history and suggest relevant literary works and film quotations. For example, if a user previously entered the theme "A Tale of the Enchanted Forest," the input system can suggest quotations from related works such as "Harry Potter" or "The Lord of the Rings." Similarly, if a user previously entered the theme "Animals' Adventures," the input system can suggest quotations from works such as "The Lion King" or "The Jungle Book." This allows users to gain inspiration for creating richer stories based on their past input history. Furthermore, the input system can also provide advice on character development and story progression based on the user's past input history. For example, if a user previously entered the character "Peter the Rabbit," the input system can suggest quotations from "Peter Rabbit." This provides users with reference information to make their character settings more concrete.

[0058] The reception desk can provide relevant educational content based on the user's age or interests as they input information. For example, if the user is a child, the reception desk can provide simple and easy-to-understand educational content. If the user is an adult, the reception desk can also provide educational content related to complex and in-depth themes. This ensures that users receive educational content tailored to their age and interests. Furthermore, the reception desk can also provide educational content related to the user's interests, such as animals or magic. For example, if the user is interested in animals, it can provide educational content about animal ecology and habits. This ensures that users receive educational content that matches their interests.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts input from users regarding the picture book's theme, characters, and story outline. For example, a user can enter a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." For the story outline, they can enter something like "An adventure where Peter and his friends go on a treasure hunt" or "A story where Emily overcomes difficulties using her magical powers." Step 2: The generation unit uses a generation AI to generate the text and illustrations for the picture book based on the information received by the reception unit. The generation AI analyzes the user's input using, for example, a text generation AI (e.g., LLM) or an image generation AI, and automatically creates appropriate text and illustrations. For example, based on the story "Peter's adventure to find treasure with his friends," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. Step 3: The editorial department provides users with the ability to edit the picture books generated by the generation department. For example, users can change parts of the generated text or adjust the colors and placement of illustrations. This allows users to create their own original picture books. Step 4: The storage section provides the functionality to save the picture books edited by the editorial department in print or digital format. For example, the completed picture book can be printed and read with children, and the digital version can be viewed on smartphones and tablets.

[0061] (Example of form 2) The picture book creation support system according to an embodiment of the present invention is a system in which a user inputs the theme, characters, and story outline of a picture book, a generating AI generates the text and illustrations of the picture book based on that input, the user edits them, and finally saves them. In this system, the user inputs the theme, characters, and story outline of a picture book, and the generating AI generates the text and illustrations of the picture book based on that input. The generated picture book can be edited and modified by the user, and the final completed picture book can be printed or saved in digital format. For example, the user can input a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." As a story outline, the user can input something like "An adventure in which Peter and his friends go on a treasure hunt" or "A story in which Emily overcomes difficulties using her magical powers." Next, the generating AI generates the text and illustrations of the picture book based on the input theme, characters, and story outline. The generating AI analyzes the user's input and automatically creates appropriate text and illustrations. For example, based on the story "An adventure in which Peter and his friends go on a treasure hunt," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. The generated picture books can be edited and modified by the user. For example, users can change parts of the generated text or adjust the colors and placement of illustrations. This allows users to create their own original picture books. Finally, the completed picture book can be saved in print or digital format. Printed picture books can be read together with children, and digital picture books can be viewed on smartphones and tablets. This allows families to create many favorite picture books together. This service allows parents and children to spend enjoyable time together while nurturing creativity. Furthermore, because it utilizes generation AI, it is possible to easily create high-quality picture books, and it is expected to be used by many families. In short, the picture book creation support system allows users to easily create and save high-quality picture books.

[0062] The picture book creation support system according to this embodiment comprises a reception unit, a generation unit, an editing unit, and a storage unit. The reception unit receives input from the user regarding the theme, characters, and story outline of the picture book. For example, the user can input a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." As a story outline, the user can input something like "An adventure in which Peter and his friends go on a treasure hunt" or "A story in which Emily overcomes difficulties using her magical powers." The generation unit uses a generation AI to generate the text and illustrations of the picture book based on the information received by the reception unit. The generation AI analyzes the user's input using, for example, a text generation AI (e.g., LLM) or an image generation AI, and automatically creates appropriate text and illustrations. For example, based on the story "An adventure in which Peter and his friends go on a treasure hunt," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. The editing unit has a function that allows the user to edit the picture book generated by the generation unit. For example, the user can change parts of the generated text or adjust the color and placement of the illustrations. This allows users to create their own original picture books. The storage section includes the ability to print or save the picture books edited by the editorial department in digital format. For example, the completed picture book can be printed and read with children, and the digital version can be viewed on smartphones and tablets. In this way, the picture book creation support system allows users to easily create and save high-quality picture books.

[0063] The generation unit can generate picture book text and illustrations using a generation AI. For example, the generation unit uses a text generation AI (e.g., LLM) to generate picture book text based on user input. For example, based on a story entered by the user, such as "Peter's adventure to find treasure with his friends," the generation AI generates appropriate text. The generation unit also uses an image generation AI to generate picture book illustrations based on user input. For example, it can generate illustrations of Peter and his friends searching for treasure, or the moment they find the treasure. In this way, by using a generation AI, picture book text and illustrations can be automatically generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can analyze user input and generate text and illustrations using an algorithm for generating appropriate text and illustrations. In this way, the generation unit can automatically generate picture book text and illustrations based on user input.

[0064] The generation unit can analyze user input using a generation AI and automatically create text and illustrations. For example, the generation unit analyzes user input using natural language processing technology. For instance, based on a story input by the user, such as "Peter's treasure hunt adventure with his friends," the generation AI generates appropriate text. The generation unit also uses image analysis technology to generate illustrations for the picture book based on user input. For example, it can generate illustrations of Peter and his friends searching for treasure, or the moment they find it. This allows the generation AI to analyze user input and automatically create appropriate text and illustrations. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can generate text and illustrations using an algorithm that analyzes user input and generates appropriate text and illustrations. This allows the generation unit to automatically generate picture book text and illustrations based on user input.

[0065] The editorial department may have a function that allows users to modify parts of the generated text. For example, the editorial department may provide an interface for modifying parts of the generated text. For example, users may select and modify parts of the generated text. The editorial department may also provide a tool for modifying parts of the generated text. For example, it may provide an editor for users to modify parts of the generated text. This allows users to modify parts of the generated text. Some or all of the above processing in the editorial department may be performed using, for example, a generation AI, or not using a generation AI. For example, the editorial department may modify the text using an algorithm for modifying parts of the generated text. This allows the editorial department to enable users to modify parts of the generated text.

[0066] The editorial department may have a function to adjust the color or placement of the generated illustrations. For example, the editorial department may provide an interface for adjusting the color of the generated illustrations. For example, the user can select and change the color of the generated illustrations. The editorial department may also provide a tool for adjusting the placement of the generated illustrations. For example, it may provide an editor for the user to change the placement of the generated illustrations. This allows the user to adjust the color and placement of the generated illustrations. Some or all of the above processing in the editorial department may be performed using, for example, a generation AI, or not using a generation AI. For example, the editorial department may adjust the illustrations using an algorithm for adjusting the color and placement of the generated illustrations. This allows the editorial department to adjust the color and placement of the generated illustrations.

[0067] The storage unit may have the functionality to print or digitally save the completed picture book. For example, the storage unit may provide an interface for printing the completed picture book. For example, the user can select an option to print the completed picture book and then print it. The storage unit may also provide a tool for digitally saving the completed picture book. For example, the user can select an option to save the completed picture book in PDF or JPEG format and then save it. This allows the user to print or digitally save the completed picture book. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit may perform the saving process using an algorithm for saving the completed picture book. This allows the storage unit to allow the user to print or digitally save the completed picture book.

[0068] The reception unit can estimate the user's emotions and suggest themes or characters based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the user is having fun, it will suggest bright and cheerful themes or characters. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is sad, it will suggest encouraging or comforting themes or characters. The reception unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is excited, it will suggest adventurous or action-packed themes or characters. This allows the reception unit to suggest appropriate themes or characters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input the user's facial expression data into a generating AI and have the AI ​​perform emotion estimation. This allows the reception desk to suggest appropriate themes and characters based on the user's emotions.

[0069] The reception desk can analyze the user's past input history and assist in selecting a theme or character. For example, the reception desk can store the user's past input history in a database and use an analysis algorithm to suggest the most suitable theme or character. For example, it can suggest similar themes or characters based on themes or characters the user has selected in the past. The reception desk can also analyze the trends in picture books the user has created in the past and suggest related themes or characters. For example, if the theme of a picture book the user has created in the past is "animal adventures," it will suggest an animal-related theme for the next time. The reception desk can also suggest themes or characters related to specific seasons or events based on the user's past input history. For example, during the Christmas season, it will suggest Christmas-related themes or characters. This allows the reception desk to suggest the most suitable theme or character based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input data into a generative AI and have the generative AI suggest the most suitable theme or character. This allows the reception desk to suggest the most suitable theme or character based on the user's past input history.

[0070] The reception desk can provide story templates based on the user's age or interests when they input data. For example, the reception desk can obtain the user's age and interests from their profile information and provide an appropriate story template based on that. For example, if the user is a child, it can provide a simple and easy-to-understand story template. The reception desk can also provide story templates with complex and deep themes if the user is an adult. For example, if the user is an adult, it can provide a story template related to history or philosophy. The reception desk can also provide story templates related to animals or magic based on the user's interests. For example, if the user is interested in animals, it can provide a story template with an animal as the main character. This allows the reception desk to provide an appropriate story template based on the user's age and interests. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's profile information into a generative AI and have the generative AI provide an appropriate story template. This allows the reception desk to provide an appropriate story template based on the user's age and interests.

[0071] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, if the user is tense, it can provide an interface with calming colors to reduce visual stress. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is having fun, it can provide an interface with bright colors to make the input process more enjoyable. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is tired, it can provide a simple and highly visible interface to facilitate the input process. This allows the design of the input interface to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation. This allows the reception area to adjust the design of the input interface based on the user's emotions.

[0072] The reception desk can suggest themes and characters related to the user's region, taking into account the user's geographical location. For example, the reception desk can obtain the user's geographical location from GPS data or IP address and suggest themes and characters related to the region based on that information. For example, if the user is in Japan, it will suggest themes and characters related to Japanese traditions and culture. The reception desk can also suggest themes and characters related to American history and landscapes if the user is in the United States. For example, if the user is in the United States, it will suggest stories themed around historical figures and landscapes of America. The reception desk can also suggest themes and characters related to European myths and legends if the user is in Europe. For example, if the user is in Europe, it will suggest stories themed around European myths and legends. This allows the reception desk to suggest appropriate themes and characters based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI suggest themes and characters related to the region. This allows the reception desk to suggest appropriate themes and characters based on the user's geographical location.

[0073] The reception desk can analyze a user's social media activity and suggest relevant themes and characters. For example, the reception desk can store the user's social media activity in a database and suggest the most suitable themes and characters using an analysis algorithm. For instance, it can suggest relevant themes and characters based on the content the user frequently shares on social media. The reception desk can also analyze the trends of the accounts the user follows and suggest themes and characters that match their interests. For example, if a user follows many animal-related accounts, it can suggest a story with animals as the main characters. Furthermore, the reception desk can suggest the most suitable themes and characters based on the time of day the user is active on social media. For example, if a user is mostly active at night, it can suggest a story with many nighttime scenes. This allows the reception desk to suggest appropriate themes and characters based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk can input the user's social media data into a generative AI and have the generative AI suggest the most suitable themes and characters. This allows the reception desk to suggest appropriate themes and characters based on the user's social media activity.

[0074] The generation unit can estimate the user's emotions and adjust the tone of text and illustrations based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the user is relaxed, it will generate text and illustrations in a calm and gentle tone. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is excited, it will generate text and illustrations in a lively tone. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is sad, it will generate text and illustrations in a comforting and encouraging tone. This allows the tone of text and illustrations to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation. This allows the generation unit to adjust the tone of text and illustrations based on the user's emotions.

[0075] The generation unit can generate more personalized text and illustrations by referencing the user's past input data during generation. For example, the generation unit can store the user's past input data in a database and refer to it during generation. For example, it can introduce a character into a new story based on the characteristics of a character previously entered by the user. The generation unit can also generate new stories related to themes previously selected by the user. For example, if the user previously created a picture book with the theme of "animal adventures," the next time it will generate a story related to animals. The generation unit can also generate text and illustrations related to specific seasons or events from the user's past input data. For example, during the Christmas season, it will generate a story related to Christmas. This allows for the generation of more personalized text and illustrations based on the user's past input data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's past input data into a generation AI and have the generation AI perform the generation of personalized text and illustrations. This allows the generation unit to generate more personalized text and illustrations based on the user's past input data.

[0076] The generation unit can generate more specific illustrations based on the detailed settings of the character specified by the user during the generation process. For example, the generation unit can store the detailed settings of the character specified by the user in a database and refer to it during generation. For example, it can generate an illustration that accurately reflects the clothing and facial expressions of the character specified by the user. The generation unit can also generate an illustration that accurately depicts the background and environment of the character specified by the user. For example, it can generate an illustration that specifically represents the actions and poses of the character specified by the user. This allows the generation unit to generate more specific illustrations based on the detailed settings of the character specified by the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the detailed settings of the character specified by the user into a generation AI and have the generation AI perform the generation of specific illustrations. This allows the generation unit to generate more specific illustrations based on the detailed settings of the character specified by the user.

[0077] The generation unit can estimate the user's emotions and adjust the length of the generated text based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is in a hurry, it will generate short, concise text. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, if the user is relaxed, it will generate longer text with detailed explanations. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, it will generate text with visually stimulating effects. This allows the length of the generated text to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, allowing the AI ​​to perform emotion estimation. This enables the generation unit to adjust the length of the text it generates based on the user's emotions.

[0078] The generation unit can reflect region-related elements in text and illustrations, taking into account the user's geographical location information during generation. For example, the generation unit can obtain the user's geographical location information from GPS data or IP address and reflect region-related elements in text and illustrations based on that information. For example, if the user is in Japan, it will generate text and illustrations that reflect Japanese scenery and culture. The generation unit can also generate text and illustrations that reflect American scenery and culture if the user is in the United States. For example, if the user is in the United States, it will generate a story themed on historical figures and landscapes of America. The generation unit can also generate text and illustrations that reflect European scenery and culture if the user is in Europe. For example, if the user is in Europe, it will generate a story themed on European myths and legends. This allows region-related elements to be reflected in text and illustrations based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the reflection of region-related elements. This allows the generation unit to incorporate region-related elements into text and illustrations based on the user's geographical location information.

[0079] The generation unit can analyze the user's social media activity during generation and reflect relevant elements in the text and illustrations. For example, the generation unit can store the user's social media activity in a database and refer to it during generation. For example, it can generate relevant text and illustrations based on the content the user frequently shares on social media. The generation unit can also analyze the trends of the accounts the user follows and generate text and illustrations that match their interests. For example, if the user follows many animal-related accounts, it can generate a story with animals as the main characters. The generation unit can also generate optimal text and illustrations based on the time of day the user is active on social media. For example, if the user is often active at night, it can generate a story with many nighttime scenes. This allows the generation unit to reflect relevant elements in the text and illustrations based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the reflection of relevant elements. This allows the generation unit to reflect relevant elements in the text and illustrations based on the user's social media activity.

[0080] The editorial team can estimate the user's emotions and adjust the design of the editing interface based on those emotions. For example, the editorial team can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, if the user is tense, they can provide an interface with calming colors to reduce visual stress. The editorial team can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is enjoying themselves, they can provide an interface with bright colors to make the editing process more enjoyable. Furthermore, the editorial team can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is tired, they can provide a simple and highly visible interface to facilitate the editing process. This allows the design of the editing interface to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the editorial department may be performed using, for example, a generative AI, or without a generative AI. For example, the editorial department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation. This allows the editorial department to adjust the design of the editorial interface based on the user's emotions.

[0081] The editorial team can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial team can store the user's past editing history in a database and refer to it during editing. For example, it can suggest similar editing methods based on the user's past edits. The editorial team can also prioritize displaying editing tools and options that the user has used in the past. For example, it can prioritize displaying fonts and colors that the user has used in the past. Furthermore, the editorial team can analyze specific patterns from the user's past editing history and suggest the optimal editing method. For example, it can suggest the optimal editing method based on the editing options the user has used most often in the past. This allows the editorial team to suggest the optimal editing method based on the user's past editing history. Some or all of the above processes in the editorial team may be performed using, for example, generative AI, or not. For example, the editorial team can input the user's past editing data into a generative AI and have the generative AI suggest the optimal editing method. This allows the editorial team to suggest the optimal editing method based on the user's past editing history.

[0082] The editorial department can provide more specific editing options based on the detailed settings of the user-specified character during editing. For example, the editorial department can store the detailed settings of the user-specified character in a database and refer to it during editing. For example, it can provide options to edit the user-specified character's clothing and facial expressions in detail. The editorial department can also provide options to edit the user-specified character's background and environment in detail. For example, it can provide options to edit the user-specified character's movements and poses in detail. This allows the editorial department to provide more specific editing options based on the detailed settings of the user-specified character. Some or all of the above processing in the editorial department may be performed using, for example, a generative AI, or not using a generative AI. For example, the editorial department can input the detailed settings of the user-specified character into a generative AI and have the generative AI provide specific editing options. This allows the editorial department to provide more specific editing options based on the detailed settings of the user-specified character.

[0083] The editorial team can estimate the user's emotions and determine editing priorities based on those estimated emotions. For example, the editorial team can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the user is in a hurry, important editing items can be displayed preferentially. The editorial team can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is relaxed, detailed editing options can be provided. The editorial team can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the user is excited, visually stimulating editing options can be provided. This allows for the determination of editing priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using, for example, generative AI, or not using generative AI. For example, the editorial team can input user facial expression data into a generative AI and have the AI ​​perform emotion estimation. This allows the editorial team to determine editing priorities based on the user's emotions.

[0084] The editorial team can provide region-related editing options by considering the user's geographical location during the editing process. For example, the editorial team can obtain the user's geographical location from GPS data or IP address and provide region-related editing options based on that information. For instance, if the user is in Japan, the editorial team can provide editing options that reflect Japanese landscapes and culture. Similarly, if the user is in the United States, the editorial team can provide editing options that reflect American landscapes and culture. For example, if the user is in the United States, the editorial team can provide editing options themed around historical figures and landscapes of the United States. Furthermore, if the user is in Europe, the editorial team can provide editing options that reflect European landscapes and culture. For example, if the user is in Europe, the editorial team can provide editing options themed around European myths and legends. This allows the editorial team to provide region-related editing options based on the user's geographical location. Some or all of the above processing by the editorial team may be performed using, for example, generative AI, or without generative AI. For example, the editorial team can input the user's geographical location information into a generative AI and have the generative AI provide region-related editing options. This allows the editorial team to provide region-related editing options based on the user's geographical location.

[0085] The editorial team can analyze users' social media activity during the editing process and provide relevant editing options. For example, the editorial team can store users' social media activity in a database and refer to it during editing. For instance, they can provide relevant editing options based on what users frequently share on social media. The editorial team can also analyze the trends of accounts users follow and provide editing options that match their interests. For example, if a user follows many animal-related accounts, they can offer the option to edit a story featuring animals. The editorial team can also provide optimal editing options based on the time of day users are active on social media. For example, if a user is mostly active at night, they can offer the option to edit a story with many nighttime scenes. This allows the editorial team to provide relevant editing options based on users' social media activity. Some or all of the above processes in the editorial team may be performed using, for example, generative AI, or not. For example, the editorial team can input user social media data into a generative AI and have the generative AI provide relevant editing options. This allows the editorial team to provide relevant editing options based on users' social media activity.

[0086] The storage unit can estimate the user's emotions and suggest a storage format based on the estimated emotions. For example, the storage unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, it may suggest saving in digital format. The storage unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, if the user is excited, it may suggest saving in print format. The storage unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the user is sad, it may suggest a special storage format (e.g., gift packaging). This allows the storage unit to suggest an appropriate storage format based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using, for example, generative AI, or without generative AI. For example, the storage unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation. This allows the storage unit to suggest an appropriate storage format based on the user's emotions.

[0087] The storage unit can suggest the optimal storage method by referring to the user's past storage history when saving. For example, the storage unit can store the user's past storage history in a database and refer to it when saving. For example, if the user has previously saved in digital format, it will suggest saving in the same format. The storage unit can also suggest saving in the same format if the user has previously saved in print format. For example, if the user has previously saved in hardcover format, it will suggest saving in hardcover format again next time. The storage unit can also suggest storage methods tailored to specific events or seasons based on the user's past storage history. For example, during Christmas, it will suggest Christmas-related storage methods. This allows the storage unit to suggest the optimal storage method based on the user's past storage history. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the storage unit can input the user's past storage data into a generating AI and have the generating AI suggest the optimal storage method. This allows the storage unit to suggest the optimal storage method based on the user's past storage history.

[0088] The storage unit can provide more specific saving options based on the user-specified format during saving. For example, the storage unit can store the user-specified format in a database and refer to it during saving. For example, it can provide saving options based on the user-specified format (e.g., PDF or JPEG). The storage unit can also provide saving options based on the user-specified resolution and size. For example, it can provide the optimal saving option based on the user-specified resolution and size. The storage unit can also provide saving options based on the user-specified print format (e.g., hardcover or softcover). For example, it can provide the optimal saving option based on the user-specified print format. This allows the storage unit to provide more specific saving options based on the user-specified format. Some or all of the above processing in the storage unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the storage unit can input the user-specified format into a generating AI and have the generating AI provide specific saving options. This allows the storage unit to provide more specific saving options based on the user-specified format.

[0089] The storage unit can estimate the user's emotions and determine the priority of saving based on the estimated emotions. For example, the storage unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is in a hurry, it will prioritize providing options that allow for quick saving. The storage unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, if the user is relaxed, it will provide detailed saving options. The storage unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the user is excited, it will provide special saving options (e.g., gift packaging). This allows the storage unit to determine the priority of saving based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the storage unit may be performed using, for example, generative AI, or not using generative AI. For example, the storage unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation. This allows the storage unit to determine the priority of saving data based on the user's emotions.

[0090] The storage unit can provide region-related storage options when saving, taking into account the user's geographical location information. For example, the storage unit can obtain the user's geographical location information from GPS data or IP address and provide region-related storage options based on that information. For example, if the user is in Japan, it can provide storage options using Japanese printing companies. The storage unit can also provide storage options using American printing companies if the user is in the United States. For example, if the user is in the United States, it can provide storage options using American printing companies. The storage unit can also provide storage options using European printing companies if the user is in Europe. For example, if the user is in Europe, it can provide storage options using European printing companies. This allows the storage unit to provide region-related storage options based on the user's geographical location information. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit can input the user's geographical location information into a generative AI and have the generative AI perform the provision of region-related storage options. This allows the storage unit to provide region-related storage options based on the user's geographical location information.

[0091] The storage unit can analyze the user's social media activity and provide relevant storage options when saving. For example, the storage unit can store the user's social media activity in a database and refer to it when saving. For example, it can provide relevant storage options based on the content the user frequently shares on social media. The storage unit can also analyze the trends of the accounts the user follows and provide storage options that match their interests. For example, if the user follows many animal-related accounts, it can provide an option to save stories featuring animals. The storage unit can also provide optimal storage options based on the time of day the user is active on social media. For example, if the user is often active at night, it can provide an option to save stories with many nighttime scenes. This allows the storage unit to provide relevant storage options based on the user's social media activity. Some or all of the above processing in the storage unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the storage unit can input the user's social media data into a generative AI and have the generative AI provide relevant storage options. This allows the storage unit to provide relevant storage options based on the user's social media activity.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The input system can suggest quotes from relevant literary works and films based on the user's input. For example, if a user enters the theme "A Tale of the Enchanted Forest," the input system can suggest quotes from related works such as "Harry Potter" and "The Lord of the Rings." Similarly, if a user enters the theme "Animals' Adventures," the input system can suggest quotes from works such as "The Lion King" and "The Jungle Book." This allows users to gain inspiration for creating richer stories. Furthermore, the input system can also suggest quotes from relevant characters based on the character entered by the user. For example, if a user enters the character "Peter the Rabbit," the input system can suggest quotes from "Peter Rabbit." This allows users to gain reference information to make their character's setting more concrete.

[0094] The generation unit can generate music and sound effects that correspond to the story's progression based on user input. For example, if a user inputs a story about "Peter's treasure hunt adventure with his friends," the generation unit can generate exciting music to match the adventure scenes and sound effects for the moment the treasure is found. Similarly, if a user inputs a story about "Emily overcoming difficulties using her magical powers," the generation unit can generate mystical music to match the magic scenes and sound effects for the moment magic is used. This allows users to create more immersive picture books. Furthermore, the generation unit can also generate theme music for characters based on the characters entered by the user. For example, if a user inputs the character "Peter the rabbit," the generation unit can generate Peter's theme music. This allows users to further highlight the character's personality.

[0095] The generation unit can estimate the user's emotions and adjust the story's development based on those emotions. For example, if the user is having fun, the generation unit can generate a bright and cheerful story. Conversely, if the user is sad, the generation unit can generate a touching and heartwarming story. This allows the system to provide a story that matches the user's emotions. Furthermore, the generation unit can also adjust the characters' actions and dialogue based on the user's emotions. For example, if the user is excited, the generation unit can have the characters take adventurous actions. Conversely, if the user is relaxed, the generation unit can have the characters take calm actions. This allows the system to provide characters' actions and dialogue that match the user's emotions.

[0096] The editorial team can implement a feature that allows users to receive feedback on the text and illustrations they generate. For example, users can share parts of their picture books with other users and receive comments and ratings. The editorial team can also suggest improvements to the text and illustrations based on feedback from other users. This allows users to incorporate the opinions of others and create better picture books. Furthermore, the editorial team can analyze feedback from other users and extract common areas for improvement. For example, if many users are requesting improvements to the same part, the editorial team can suggest focusing on improving that part. This allows users to improve their picture books efficiently.

[0097] The editorial team can provide a feature that allows users to apply different art styles to user-generated illustrations. For example, users can select a user-generated illustration and apply different art styles such as anime, realistic, or comic book style. The editorial team can also provide tools to change the color tone and texture of user-generated illustrations. For example, users can change the color tone or add textures to their user-generated illustrations. This allows users to customize their illustrations to their liking. Furthermore, the editorial team can provide options to apply filters and effects to user-generated illustrations. For example, users can apply blur or glitch effects to their user-generated illustrations. This allows users to add their own unique style to their illustrations.

[0098] The storage function can include a feature that allows users to save completed picture books to cloud storage. For example, users can upload their completed picture books to cloud storage and access them anytime, anywhere. The storage function can also generate a link to share the picture books saved in cloud storage with other users. For example, users can send the generated link to their picture books to friends and family for easy sharing. This allows users to easily save and share their picture books. Furthermore, the storage function can also provide a version control function for picture books saved in cloud storage. For example, each time a user edits a picture book, a new version is saved, and they can revert to previous versions. This allows users to manage the editing history of their picture books and refer to past versions as needed.

[0099] The reception desk can estimate the user's emotions and provide advice on the input based on those emotions. For example, if the user is enjoying themselves, the reception desk might suggest, "How about adding some more adventurous elements?" If the user is sad, the reception desk might suggest, "How about thinking about a touching ending?" This allows the user to receive advice tailored to their emotions, making the story more engaging. Furthermore, the reception desk can also suggest revisions to the input based on the user's emotions. For example, if the user is nervous, the reception desk might suggest, "How about relaxing and progressing through the story a little more slowly?" This allows the user to receive revision suggestions tailored to their emotions, making the story progress more naturally.

[0100] The input system can analyze a user's past input history and suggest relevant literary works and film quotations. For example, if a user previously entered the theme "A Tale of the Enchanted Forest," the input system can suggest quotations from related works such as "Harry Potter" or "The Lord of the Rings." Similarly, if a user previously entered the theme "Animals' Adventures," the input system can suggest quotations from works such as "The Lion King" or "The Jungle Book." This allows users to gain inspiration for creating richer stories based on their past input history. Furthermore, the input system can also provide advice on character development and story progression based on the user's past input history. For example, if a user previously entered the character "Peter the Rabbit," the input system can suggest quotations from "Peter Rabbit." This provides users with reference information to make their character settings more concrete.

[0101] The reception desk can provide relevant educational content based on the user's age or interests as they input information. For example, if the user is a child, the reception desk can provide simple and easy-to-understand educational content. If the user is an adult, the reception desk can also provide educational content related to complex and in-depth themes. This ensures that users receive educational content tailored to their age and interests. Furthermore, the reception desk can also provide educational content related to the user's interests, such as animals or magic. For example, if the user is interested in animals, it can provide educational content about animal ecology and habits. This ensures that users receive educational content that matches their interests.

[0102] The reception desk can estimate the user's emotions and adjust the voice assistant of the input interface based on those emotions. For example, if the user is nervous, the reception desk can provide a voice assistant with a calm voice to help the user relax. Conversely, if the user is having fun, the reception desk can provide a voice assistant with a cheerful voice to make the input process more enjoyable. This allows the tone of the voice assistant to be adjusted based on the user's emotions. Furthermore, the reception desk can also adjust the speaking speed and content of the voice assistant based on the user's emotions. For example, if the user is tired, the reception desk can provide a voice assistant that speaks slowly to reduce the user's burden. This allows the speaking speed and content of the voice assistant to be adjusted based on the user's emotions.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk accepts input from users regarding the picture book's theme, characters, and story outline. For example, a user can enter a theme such as "Animals' Adventure" or "A Story of a Magical Forest," and specify characters such as "Peter the Rabbit" or "Emily the Witch." For the story outline, they can enter something like "An adventure where Peter and his friends go on a treasure hunt" or "A story where Emily overcomes difficulties using her magical powers." Step 2: The generation unit uses a generation AI to generate the text and illustrations for the picture book based on the information received by the reception unit. The generation AI analyzes the user's input using, for example, a text generation AI (e.g., LLM) or an image generation AI, and automatically creates appropriate text and illustrations. For example, based on the story "Peter's adventure to find treasure with his friends," illustrations of Peter and his friends searching for treasure and the moment they find the treasure are generated. Step 3: The editorial department provides users with the ability to edit the picture books generated by the generation department. For example, users can change parts of the generated text or adjust the colors and placement of illustrations. This allows users to create their own original picture books. Step 4: The storage section provides the functionality to save the picture books edited by the editorial department in print or digital format. For example, the completed picture book can be printed and read with children, and the digital version can be viewed on smartphones and tablets.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the reception unit, generation unit, editing unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input the theme, characters, and story outline of the picture book. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, using a generation AI to generate the text and illustrations of the picture book. The editing unit is implemented by the control unit 46A of the smart device 14, for example, allowing the user to edit the generated picture book. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to print or save the edited picture book in digital format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, generation unit, editing unit, and storage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input the theme, characters, and story outline of the picture book by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates the text and illustrations of the picture book using a generation AI. The editing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which allows the user to edit the generated picture book. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which saves the edited picture book in print or digital format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, editing unit, and storage unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input the theme, characters, and story outline of the picture book by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates the text and illustrations of the picture book using a generation AI. The editing unit is implemented by, for example, the control unit 46A of the headset terminal 314, allowing the user to edit the generated picture book. The storage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which saves the edited picture book in print or digital format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, editing unit, and storage unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input the theme, characters, and story outline of the picture book by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates the text and illustrations of the picture book using a generation AI. The editing unit is implemented by, for example, the control unit 46A of the robot 414, allowing the user to edit the generated picture book. The storage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which saves the edited picture book in print or digital format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A reception desk that accepts input from users regarding the theme or characters of the picture book, and a summary of the story. A generation unit generates the text and illustrations of a picture book based on the information received by the reception unit, The editing unit allows the user to edit the picture book generated by the generation unit, The system includes a storage unit for storing picture books edited by the aforementioned editorial department in print or digital format. A system characterized by the following features. (Note 2) The generating unit is Generating text and illustrations for picture books using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​analyzes user input and automatically creates text and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editorial department, It includes a feature that allows users to modify parts of the generated text. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned editorial department, It includes a feature that allows users to adjust the color or placement of the generated illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned storage unit is It includes the ability to print or save the completed picture book in digital format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and suggests themes or characters based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history to help them select a theme or character. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When the user enters information, a story template is provided based on their age or interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is We suggest themes and characters relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is We analyze users' social media activity and suggest relevant themes and characters. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the tone of text and illustrations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system references the user's past input data to generate more personalized text and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, a more detailed illustration is generated based on the user's specified character settings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system takes the user's geographical location into account and reflects region-related elements in the text and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the system analyzes the user's social media activity and reflects relevant elements in the text and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editorial department, It estimates the user's emotions and adjusts the design of the editing interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editorial department, During editing, the system refers to the user's past editing history and suggests the optimal editing method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editorial department, During editing, the system provides more specific editing options based on the detailed settings of the character specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editorial department, It estimates the user's emotions and determines editing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned editorial department, When editing, the system takes the user's geographical location into consideration and provides region-specific editing options. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned editorial department, During editing, the system analyzes the user's social media activity and provides relevant editing options. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned storage unit is It estimates the user's emotions and suggests a storage format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned storage unit is When saving, the system refers to the user's past save history and suggests the optimal saving method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned storage unit is When saving, it provides more specific save options based on the format specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned storage unit is The system estimates the user's emotions and determines the priority of saving based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is When saving, the system takes the user's geographical location into account and provides region-specific saving options. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is When saving, the system analyzes the user's social media activity and provides relevant saving options. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input from users regarding the theme or characters of the picture book, and a summary of the story. A generation unit generates the text and illustrations of a picture book based on the information received by the reception unit, The editing unit allows the user to edit the picture book generated by the generation unit, The system includes a storage unit for storing picture books edited by the aforementioned editorial department in print or digital format. A system characterized by the following features.

2. The generating unit is Generative AI generates text and illustrations for picture books. The system according to feature 1.

3. The generating unit is The AI ​​analyzes user input and automatically creates text and illustrations. The system according to feature 1.

4. The aforementioned editorial department, It includes a feature that allows users to modify parts of the generated text. The system according to feature 1.

5. The aforementioned editorial department, It includes a feature that allows users to adjust the color or placement of the generated illustrations. The system according to feature 1.

6. The aforementioned storage unit is It includes the ability to print or save the completed picture book in digital format. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and suggests themes or characters based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history to help them select a theme or character. The system according to feature 1.

9. The aforementioned reception unit is When the user enters information, a story template is provided based on their age or interests. The system according to feature 1.

Images