system

Abstract

The system according to this embodiment aims to generate customized stories and illustrations based on user input and to change the story according to the reader's selection. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a selection unit, a modification unit, and a display unit. The reception unit receives user input. The generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. The provision unit provides the story and illustrations generated by the generation unit. The selection unit receives the reader's selection. The modification unit modifies the story based on the selection received by the selection unit. The display unit displays the story and illustrations generated by the generation unit.

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, and includes 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 the storytelling of picture books is one-way, which cannot fully stimulate children's creativity and limits the interaction between parents and children.

[0005] The system according to the embodiment aims to generate a customized story and illustration based on a user's input and change the story according to the reader's selection.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a selection unit, a modification unit, and a display unit. The reception unit receives user input. The generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. The provision unit provides the story and illustrations generated by the generation unit. The selection unit receives the reader's selection. The modification unit modifies the story based on the selection received by the selection unit. The display unit displays the story and illustrations generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate customized stories and illustrations based on user input and can change the story according to the reader's selection. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The picture book automatic generation system according to an embodiment of the present invention is a system that generates customized stories and illustrations based on user input, and in which the story changes according to the reader's selection. Conventional picture books mainly consist of one-way storytelling, which does not fully draw out children's creativity and limits interaction between parents and children. The present invention uses AI to instantly generate customized stories and illustrations based on user input. It also has a function that changes the story according to the reader's selection. First, the user inputs information such as the theme, characters, and situation of the picture book. For example, the user inputs a theme such as "a story about pirates on an adventure to find treasure" or "a story about animals having a party in the forest." This information is input into a multimodal LLM. Next, the multimodal LLM analyzes the input information and generates customized stories and illustrations. For example, in a story about pirates on an adventure to find treasure, illustrations depicting a pirate ship, a treasure map, and the pirate characters are generated. In a story about animals having a party in the forest, illustrations depicting the forest scenery and the animal characters are generated. Furthermore, it has a function that changes the story according to the reader's selection. For example, in a scene where the reader chooses whether the pirates find treasure or are captured by the enemy, the story unfolds differently depending on the choice. In this way, readers can obtain a personalized learning experience tailored to their interests and level of understanding. This new approach allows children to obtain a personalized learning experience that matches their interests and level of understanding. Furthermore, the open-ended story structure stimulates creativity, and exposure to a rich vocabulary and diverse expressions can improve language skills. In addition, the process of creating a story together as a family contributes to promoting communication and deepening the bond between parents and children. Thus, the picture book automatic generation system can provide a personalized learning experience by generating and delivering customized stories and illustrations based on user input.

[0029] The picture book automatic generation system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a selection unit, a modification unit, and a display unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit allows the user to input information such as the theme, characters, and situation of the picture book using text input. The reception unit can also convert the content dictated by the user into text using voice input and input it. Furthermore, the reception unit can input illustrations or photographs drawn by the user using image input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a customized story and illustrations. For example, the generation unit generates the story using a text generation AI (e.g., LLM). The generation unit can also generate illustrations using an image generation AI. Furthermore, the generation unit can simultaneously generate the story and illustrations using a multimodal generation AI. The provision unit provides the story and illustrations generated by the generation unit to the user. The provision unit displays the story and illustrations, for example, through a web application or a mobile application. The provider unit can also send stories and illustrations via email. Furthermore, the provider unit can print stories and illustrations using a printer. The selection unit accepts the reader's choices. For example, the selection unit can present options and accept the reader's choice at a point in the story where the reader chooses the direction of the story. The modification unit modifies the story based on the choices accepted by the selection unit. For example, the modification unit can change the direction of the story according to the choices made by the reader. The display unit displays the stories and illustrations generated by the generation unit. The display unit can display stories and illustrations through, for example, a web application or a mobile application. Furthermore, the display unit can display stories and illustrations sent via email. Furthermore, the display unit can display stories and illustrations printed using a printer.As a result, the picture book automatic generation system according to the embodiment can provide a personalized learning experience by generating and providing customized stories and illustrations based on user input.

[0030] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk allows users to input information such as the theme, characters, and situations of a picture book using text input. Specifically, users can use a keyboard to input details such as the story's outline, character names, and background settings. The reception desk can also convert what the user dictates into text using voice input and input it. By using speech recognition technology, what the user says can be accurately transcribed into text, enabling input in a natural conversational format. Furthermore, the reception desk can accept illustrations and photographs drawn by the user using image input. For example, a user can scan and upload a hand-drawn character illustration to generate a story based on that illustration. This allows the reception desk to offer diverse input methods and maximize user creativity. In addition, the reception desk centrally manages user input and can collaborate with other departments as needed. For example, the entered data can be stored on a cloud server and made accessible to the generation and modification departments. The reception desk also provides an interface to easily review and correct input, allowing users to review their input and make corrections as needed. This allows the reception desk to efficiently and effectively receive user input, improving the overall performance of the system.

[0031] The generation unit uses a generation AI to analyze information received by the reception unit and generate customized stories and illustrations. For example, the generation unit generates stories using a text generation AI (e.g., LLM). Specifically, it generates narratives in natural language based on themes, characters, and situations entered by the user. The generation AI has learned from a vast dataset and can handle diverse writing styles and genres, enabling it to generate a variety of stories to meet user requests. The generation unit can also generate illustrations using an image generation AI. For example, it can generate realistic or anime-style illustrations based on character characteristics and background settings entered by the user. Furthermore, the generation unit can simultaneously generate stories and illustrations using a multimodal generation AI. This allows for the automatic generation of illustrations that match the progression of the story and provides them to the user. The generation unit centrally manages the generated stories and illustrations and can collaborate with other departments as needed. For example, the generated data is stored on a cloud server, allowing access by the provision and modification departments. The generation unit can also adjust the parameters of the generation AI to improve the efficiency and accuracy of the generation process and obtain optimal generation results. This allows the generation unit to produce high-quality stories and illustrations based on user input, thereby improving the overall system performance.

[0032] The delivery unit provides users with stories and illustrations generated by the generation unit. The delivery unit displays stories and illustrations, for example, through web or mobile applications. Specifically, it provides a user-accessible interface, allowing users to view the generated content. The delivery unit can also send stories and illustrations via email. It sends the generated content as an attachment to an email address specified by the user, making it easily accessible. Furthermore, the delivery unit can print stories and illustrations using a printer. If desired, the generated content can be provided as a high-quality printed material, allowing users to enjoy it as a physical picture book. The delivery unit centrally manages and distributes the provided content, and can collaborate with other departments as needed. For example, the provided data is stored on a cloud server, making it accessible to the selection and modification units. The delivery unit can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. This allows the delivery unit to quickly and reliably provide users with customized stories and illustrations, improving the overall system performance.

[0033] The selection unit accepts the reader's choices. For example, in situations where the reader chooses the direction of the story, the selection unit can present options and accept the reader's choice. Specifically, it displays multiple options during the progression of the story and provides an interface that allows the reader to choose the next development. Options are presented at various points in the story, such as branching points, character actions, and changes in situations. The selection unit can accept user choices in real time and reflect them in the next development. The selection unit can centrally manage the selection content and collaborate with other departments as needed. For example, selected data can be stored on a cloud server and made accessible to the modification and generation departments. Furthermore, the selection unit can devise methods for presenting options and design the interface to allow users to make choices intuitively. In this way, the selection unit can efficiently and effectively accept user choices and improve the overall performance of the system.

[0034] The modification unit modifies the story based on the selections received by the selection unit. For example, the modification unit can change the story's progression depending on the choices made by the reader. Specifically, it reflects changes in the story's progression, character actions, and situations in real time based on the selected choices. The modification unit can use generative AI to generate new stories corresponding to the choices and integrate them into the existing story. This allows for the provision of customized stories based on user choices while maintaining narrative consistency. The modification unit centrally manages the modified stories and can collaborate with other departments as needed. For example, the modified data can be stored on a cloud server and made accessible to the display and delivery units. Furthermore, the modification unit can adjust the parameters of the generative AI to improve the efficiency and accuracy of the modification process and obtain optimal modification results. This allows the modification unit to modify high-quality stories based on user choices and improve the overall system performance.

[0035] The display unit displays the stories and illustrations generated by the generation unit. The display unit can display stories and illustrations through, for example, web applications or mobile applications. Specifically, it provides a user-accessible interface, allowing users to view the generated content. The display unit can also display stories and illustrations sent via email. Users can easily view the generated content by opening the received email. Furthermore, the display unit can display stories and illustrations printed on a printer. If desired, the generated content can be provided as a high-quality printed material, allowing users to enjoy it as a physical picture book. The display unit centrally manages and distributes the displayed content and can collaborate with other departments as needed. For example, the displayed data can be stored on a cloud server, making it accessible to the selection and modification units. The display unit can also collect user feedback and continuously improve the accuracy and effectiveness of the displayed content. This allows the display unit to quickly and reliably display customized stories and illustrations to users, improving the overall system performance.

[0036] The generation unit can generate customized stories and illustrations based on user input. For example, the generation unit can generate stories using text generation AI (e.g., LLM). For example, the generation unit can generate stories based on themes, characters, and situations entered by the user. The generation unit can also generate illustrations using image generation AI. For example, the generation unit can generate illustrations based on themes, characters, and situations entered by the user. Furthermore, the generation unit can simultaneously generate stories and illustrations using multimodal generation AI. For example, the generation unit can simultaneously generate stories and illustrations based on themes, characters, and situations entered by the user. This allows the generation unit to provide a personalized learning experience by generating customized stories and illustrations based on user input.

[0037] The selection section can accept the reader's choice. For example, in a scene where the reader chooses how the story unfolds, the selection section can present options and accept the reader's choice. For example, in a scene where the reader chooses whether "the pirates find the treasure or are captured by the enemy," the selection section can present options and accept the reader's choice. The selection section can also present options and accept the reader's choice in a scene where the reader chooses whether "the animals have a party in the forest or go on an adventure." In this way, by accepting the reader's choice, the selection section can change the story's development according to the reader's choice. Some or all of the above processing in the selection section may be performed using AI, for example, or without AI. For example, the selection section can input the reader's choice into AI and have AI present the options.

[0038] The modification function can alter the story based on the reader's choices. For example, the modification function can change the story's progression depending on the choices made by the reader. For example, if the reader chooses "The pirates find the treasure," the modification function can change the story to one where the treasure is found. Similarly, if the reader chooses "The pirates are captured by the enemy," the modification function can change the story to one where the pirates are captured by the enemy. In this way, the modification function can provide a story progression that suits the reader's interests and level of understanding by altering the story based on the reader's choices. Some or all of the above-described processes in the modification function may be performed using AI, for example, or not. For example, the modification function can input the reader's choices into AI and have the AI ​​perform the story changes.

[0039] The display unit can display the generated story and illustrations. The display unit can display the story and illustrations, for example, through a web application or a mobile application. The display unit can, for example, display the generated story and illustrations on the screen of a web application. The display unit can also display the generated story and illustrations on the screen of a mobile application. Furthermore, the display unit can also display the story and illustrations sent via email. The display unit can, for example, display the story and illustrations sent via email on the screen of an email client. The display unit can also display the story and illustrations printed by a printer. The display unit can, for example, display the story and illustrations printed by a printer on paper. In this way, the display unit allows users to visually enjoy the story by displaying the generated story and illustrations. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated story and illustrations into AI and have AI select the display method.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also automatically display themes and characters that the user has previously entered as suggestions. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the reception desk can improve user convenience by selecting the optimal input method through analysis of the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0041] The input receiving unit can filter input based on the user's current areas of interest. For example, the receiving unit can suggest relevant themes or characters based on keywords the user has recently searched for or their browsing history. It can also narrow down input candidates based on topics the user is currently interested in. Furthermore, the receiving unit can analyze the user's social media activity and display input candidates related to their areas of interest. This allows the receiving unit to receive more relevant input by filtering based on the user's current areas of interest. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputs that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize suggesting themes or characters related to that region. Furthermore, if the user is traveling, the reception desk can prioritize inputs related to stories or illustrations related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize inputs related to their home or daily life. This allows the reception desk to prioritize inputs that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize highly relevant inputs.

[0043] The reception unit can analyze the user's social media activity and accept relevant input when receiving input. For example, the reception unit can suggest relevant themes or characters based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and display relevant input candidates. Furthermore, the reception unit can narrow down input candidates based on the user's interests on social media. In this way, the reception unit can efficiently accept relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform filtering of relevant inputs.

[0044] The generation unit can adjust the level of detail of the generated content based on the importance of the input theme. For example, for an important theme, the generation unit can generate a detailed story and high-resolution illustrations. For a general theme, the generation unit can also generate a concise story and standard-resolution illustrations. Furthermore, for a simple theme, the generation unit can generate a short story and simple illustrations. In this way, the generation unit can provide an appropriate level of content by adjusting the level of detail of the generated content based on the importance of the input theme. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input importance data of the input theme into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated content.

[0045] The generation unit can apply different generation algorithms depending on the attributes of the input character during generation. For example, in the case of an animal character, the generation unit can generate cute illustrations and a simple story. In the case of a hero character, the generation unit can also generate dynamic illustrations and an adventurous story. Furthermore, in the case of a fantasy character, the generation unit can generate fantastical illustrations and a story that includes magical elements. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the attributes of the input character. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the attribute data of the input character into a generation AI and have the generation AI select a generation algorithm.

[0046] The generation unit can determine the generation priority based on the submission deadline of the input theme during the generation process. For example, in the case of an urgent theme, the generation unit can generate the story and illustrations with the highest priority. It can also generate the story and illustrations with a standard priority for regular themes. Furthermore, for long-term themes, the generation unit can postpone the generation of the story and illustrations. This allows the generation unit to provide content at the appropriate time by determining the generation priority based on the submission deadline of the input theme. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the submission deadline data of the input theme into a generation AI and have the generation AI determine the generation priority.

[0047] The generation unit can adjust the generation order based on the relevance of the input themes during generation. For example, if the theme is highly relevant, the generation unit can generate the story and illustrations first. If the theme is moderately relevant, the generation unit can generate the story and illustrations next. Furthermore, if the theme is low relevance, the generation unit can generate the story and illustrations last. In this way, the generation unit can provide more relevant content by adjusting the generation order based on the relevance of the input themes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the input themes into a generation AI and have the generation AI perform the adjustment of the generation order.

[0048] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the format of stories the user has enjoyed reading in the past. It can also select the optimal service delivery method based on the style of illustrations the user has used in the past. Furthermore, the service provider can select the optimal service delivery method for a specific time period based on the user's past usage history. In this way, the service provider can improve user convenience by selecting the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal service delivery method.

[0049] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider can provide relevant stories and illustrations based on keywords the user has recently searched for or their browsing history. It can also customize the content based on topics the user is currently interested in. Furthermore, the service provider can analyze the user's social media activity and provide stories and illustrations related to their areas of interest. This allows the service provider to provide more relevant content by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the customization of the content offered.

[0050] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide stories and illustrations related to that region. Furthermore, if the user is traveling, the service provider can provide stories and illustrations related to their travel destination. Additionally, if the user is at home, the service provider can provide stories and illustrations related to their home and daily life. This allows the service provider to select the optimal delivery method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0051] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can provide relevant stories and illustrations based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and provide relevant stories and illustrations. Furthermore, the service provider can customize the content based on the user's interests on social media. This allows the service provider to efficiently provide relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content offered.

[0052] The selection unit can present the optimal option by referring to the user's past selection history when a selection is made. For example, the selection unit can present the optimal option based on the story development the user has previously selected. It can also present options that include the user's favorite characters or situations based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and present the option that is most interesting to the user. In this way, the selection unit can present the optimal option by referring to the user's past selection history and improve user convenience. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection history data into a generating AI and have the generating AI perform the task of presenting the optimal option.

[0053] The selection unit can customize the options based on the user's current areas of interest when making a selection. For example, the selection unit can present relevant options based on keywords the user has recently searched for or their browsing history. It can also customize options based on topics the user is currently interested in. Furthermore, the selection unit can analyze the user's social media activity and present options related to their areas of interest. This allows the selection unit to provide more relevant options by customizing them based on the user's current areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the options.

[0054] The selection unit can present the most suitable options when a user makes a selection, taking into account the user's geographical location. For example, if the user is in a specific region, the selection unit can present options related to that region. Furthermore, if the user is traveling, the selection unit can present options related to their travel destination. Additionally, if the user is at home, the selection unit can present options related to their home and daily life. This allows the selection unit to present the most suitable options by considering the user's geographical location, thereby improving user convenience. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting the most suitable options.

[0055] The selection unit can analyze the user's social media activity and customize the options at the time of selection. For example, the selection unit can present relevant options based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and present relevant options. Furthermore, the selection unit can customize options based on the user's interests on social media. In this way, the selection unit can efficiently provide relevant options by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of options.

[0056] The modification unit can select the optimal modification method by referring to the user's past selection history when making a change. For example, the modification unit can select the optimal modification method based on the story development the user has previously selected. It can also select a modification method that includes the user's favorite characters or situations from the user's past selection history. Furthermore, the modification unit can analyze the user's past selection history and select the modification method that is most interesting. In this way, the modification unit can select the optimal modification method by referring to the user's past selection history and improve user convenience. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the user's past selection history data into a generating AI and have the generating AI perform the selection of the optimal modification method.

[0057] The modification unit can customize the story based on the user's current areas of interest when making changes. For example, the modification unit can make relevant story changes based on keywords the user has recently searched for or their browsing history. It can also customize the story based on topics the user is currently interested in. Furthermore, the modification unit can analyze the user's social media activity and make story changes related to their areas of interest. This allows the modification unit to provide a more relevant story development by customizing the story based on the user's current areas of interest. Some or all of the above processes in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the user's areas of interest data into a generating AI and have the generating AI perform the story customization.

[0058] The modification unit can select the optimal modification method when making changes, taking into account the user's geographical location information. For example, if the user is in a specific region, the modification unit can make story changes related to that region. Furthermore, if the user is traveling, the modification unit can make story changes related to their travel destination. Additionally, if the user is at home, the modification unit can make story changes related to their home and daily life. This allows the modification unit to select the optimal modification method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal modification method.

[0059] The modification unit can analyze the user's social media activity and customize the story when making changes. For example, the modification unit can make relevant story changes based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and make relevant story changes. Furthermore, the modification unit can customize the story based on the user's interests on social media. This allows the modification unit to efficiently provide relevant stories by analyzing the user's social media activity. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the user's social media activity data into a generating AI and have the generating AI perform the story customization.

[0060] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can select the optimal display method based on the display method the user has preferred to use in the past. Furthermore, the display unit can select the optimal display method for a specific time period based on the user's past operation history. In addition, the display unit can analyze the user's past operation history and select the display method with the highest visibility. This allows the display unit to improve user convenience by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0061] The display unit can customize the displayed content based on the user's current areas of interest when displaying information. For example, the display unit can provide relevant displayed content based on keywords the user has recently searched for or their browsing history. It can also customize the displayed content based on topics the user is currently interested in. Furthermore, the display unit can analyze the user's social media activity and provide displayed content related to their areas of interest. In this way, the display unit can provide more relevant displays by customizing the displayed content based on the user's current areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the displayed content.

[0062] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can improve user convenience by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0063] The display unit can analyze the user's social media activity and customize the displayed content at the time of display. For example, the display unit can provide relevant content based on the user's recent posts and comments. It can also analyze the activity of accounts and groups that the user follows and provide relevant content. Furthermore, the display unit can customize the displayed content based on the user's interests on social media. In this way, the display unit can efficiently provide relevant content by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also automatically display themes and characters that the user has previously entered as suggestions. Furthermore, the reception desk can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, the reception desk can select the most suitable input method by analyzing the user's past input history, thereby improving user convenience.

[0066] The input system can filter input based on the user's current areas of interest. For example, it can suggest relevant themes and characters based on keywords the user has recently searched for and their browsing history. The input system can also narrow down input suggestions based on topics the user is currently interested in. Furthermore, the input system can analyze the user's social media activity and display input suggestions related to their areas of interest. This allows the input system to receive more relevant input by filtering based on the user's current areas of interest.

[0067] The generation unit can adjust the level of detail of the generated content based on the importance of the input theme. For example, for an important theme, it can generate a detailed story and high-resolution illustrations. For a general theme, it can generate a concise story and illustrations of standard resolution. Furthermore, for a simple theme, it can generate a short story and simple illustrations. In this way, the generation unit can provide content at an appropriate level by adjusting the level of detail of the generated content based on the importance of the input theme.

[0068] The service provider can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, it can select the optimal delivery method based on the format of stories the user has enjoyed reading in the past. It can also select the optimal delivery method based on the style of illustrations the user has used in the past. Furthermore, it can select the optimal delivery method for a specific time period based on the user's past usage history. This allows the service provider to select the optimal delivery method by referring to the user's past usage history, thereby improving user convenience.

[0069] The selection function can present the most suitable options by referring to the user's past selection history. For example, it can present the best options based on the story progression the user has previously selected. It can also present options that include the user's preferred characters or situations based on their past selection history. Furthermore, it can analyze the user's past selection history and present the most interesting options. In this way, the selection function can present the most suitable options by referring to the user's past selection history, thereby improving user convenience.

[0070] The display unit can select the optimal display method by considering the user's device information when displaying information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method by considering the user's device information, thereby improving user convenience.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, users can provide information such as the theme, characters, and situation of a picture book via text input. They can also convert dictated content into text using voice input, or input illustrations or photographs using image input. Step 2: The generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. The generation unit can generate stories using text generation AI (e.g., LLM) and illustrations using image generation AI. It is also possible to generate stories and illustrations simultaneously using multimodal generation AI. Step 3: The provider unit provides the user with the story and illustrations generated by the generator unit. The provider unit can display the story and illustrations through web applications or mobile applications. They can also be sent via email or printed using a printer. Step 4: The choice section accepts the reader's choice. The choice section can present options at points in the story where the reader chooses how to proceed, and accept their choice. Step 5: The modification section modifies the story based on the selections received by the selection section. The modification section can change the development of the story depending on the choices made by the reader. Step 6: The display unit displays the story and illustrations generated by the generation unit. The display unit can display the story and illustrations through web applications and mobile applications. It can also display stories and illustrations sent via email or printed from a printer.

[0073] (Example of form 2) The picture book automatic generation system according to an embodiment of the present invention is a system that generates customized stories and illustrations based on user input, and in which the story changes according to the reader's selection. Conventional picture books mainly consist of one-way storytelling, which does not fully draw out children's creativity and limits interaction between parents and children. The present invention uses AI to instantly generate customized stories and illustrations based on user input. It also has a function that changes the story according to the reader's selection. First, the user inputs information such as the theme, characters, and situation of the picture book. For example, the user inputs a theme such as "a story about pirates on an adventure to find treasure" or "a story about animals having a party in the forest." This information is input into a multimodal LLM. Next, the multimodal LLM analyzes the input information and generates customized stories and illustrations. For example, in a story about pirates on an adventure to find treasure, illustrations depicting a pirate ship, a treasure map, and the pirate characters are generated. In a story about animals having a party in the forest, illustrations depicting the forest scenery and the animal characters are generated. Furthermore, it has a function that changes the story according to the reader's selection. For example, in a scene where the reader chooses whether the pirates find treasure or are captured by the enemy, the story unfolds differently depending on the choice. In this way, readers can obtain a personalized learning experience tailored to their interests and level of understanding. This new approach allows children to obtain a personalized learning experience that matches their interests and level of understanding. Furthermore, the open-ended story structure stimulates creativity, and exposure to a rich vocabulary and diverse expressions can improve language skills. In addition, the process of creating a story together as a family contributes to promoting communication and deepening the bond between parents and children. Thus, the picture book automatic generation system can provide a personalized learning experience by generating and delivering customized stories and illustrations based on user input.

[0074] The picture book automatic generation system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a selection unit, a modification unit, and a display unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit allows the user to input information such as the theme, characters, and situation of the picture book using text input. The reception unit can also convert the content dictated by the user into text using voice input and input it. Furthermore, the reception unit can input illustrations or photographs drawn by the user using image input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a customized story and illustrations. For example, the generation unit generates the story using a text generation AI (e.g., LLM). The generation unit can also generate illustrations using an image generation AI. Furthermore, the generation unit can simultaneously generate the story and illustrations using a multimodal generation AI. The provision unit provides the story and illustrations generated by the generation unit to the user. The provision unit displays the story and illustrations, for example, through a web application or a mobile application. The provider unit can also send stories and illustrations via email. Furthermore, the provider unit can print stories and illustrations using a printer. The selection unit accepts the reader's choices. For example, the selection unit can present options and accept the reader's choice at a point in the story where the reader chooses the direction of the story. The modification unit modifies the story based on the choices accepted by the selection unit. For example, the modification unit can change the direction of the story according to the choices made by the reader. The display unit displays the stories and illustrations generated by the generation unit. The display unit can display stories and illustrations through, for example, a web application or a mobile application. Furthermore, the display unit can display stories and illustrations sent via email. Furthermore, the display unit can display stories and illustrations printed using a printer.As a result, the picture book automatic generation system according to the embodiment can provide a personalized learning experience by generating and providing customized stories and illustrations based on user input.

[0075] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk allows users to input information such as the theme, characters, and situations of a picture book using text input. Specifically, users can use a keyboard to input details such as the story's outline, character names, and background settings. The reception desk can also convert what the user dictates into text using voice input and input it. By using speech recognition technology, what the user says can be accurately transcribed into text, enabling input in a natural conversational format. Furthermore, the reception desk can accept illustrations and photographs drawn by the user using image input. For example, a user can scan and upload a hand-drawn character illustration to generate a story based on that illustration. This allows the reception desk to offer diverse input methods and maximize user creativity. In addition, the reception desk centrally manages user input and can collaborate with other departments as needed. For example, the entered data can be stored on a cloud server and made accessible to the generation and modification departments. The reception desk also provides an interface to easily review and correct input, allowing users to review their input and make corrections as needed. This allows the reception desk to efficiently and effectively receive user input, improving the overall performance of the system.

[0076] The generation unit uses a generation AI to analyze information received by the reception unit and generate customized stories and illustrations. For example, the generation unit generates stories using a text generation AI (e.g., LLM). Specifically, it generates narratives in natural language based on themes, characters, and situations entered by the user. The generation AI has learned from a vast dataset and can handle diverse writing styles and genres, enabling it to generate a variety of stories to meet user requests. The generation unit can also generate illustrations using an image generation AI. For example, it can generate realistic or anime-style illustrations based on character characteristics and background settings entered by the user. Furthermore, the generation unit can simultaneously generate stories and illustrations using a multimodal generation AI. This allows for the automatic generation of illustrations that match the progression of the story and provides them to the user. The generation unit centrally manages the generated stories and illustrations and can collaborate with other departments as needed. For example, the generated data is stored on a cloud server, allowing access by the provision and modification departments. The generation unit can also adjust the parameters of the generation AI to improve the efficiency and accuracy of the generation process and obtain optimal generation results. This allows the generation unit to produce high-quality stories and illustrations based on user input, thereby improving the overall system performance.

[0077] The delivery unit provides users with stories and illustrations generated by the generation unit. The delivery unit displays stories and illustrations, for example, through web or mobile applications. Specifically, it provides a user-accessible interface, allowing users to view the generated content. The delivery unit can also send stories and illustrations via email. It sends the generated content as an attachment to an email address specified by the user, making it easily accessible. Furthermore, the delivery unit can print stories and illustrations using a printer. If desired, the generated content can be provided as a high-quality printed material, allowing users to enjoy it as a physical picture book. The delivery unit centrally manages and distributes the provided content, and can collaborate with other departments as needed. For example, the provided data is stored on a cloud server, making it accessible to the selection and modification units. The delivery unit can also collect user feedback and continuously improve the accuracy and effectiveness of the provided content. This allows the delivery unit to quickly and reliably provide users with customized stories and illustrations, improving the overall system performance.

[0078] The selection unit accepts the reader's choices. For example, in situations where the reader chooses the direction of the story, the selection unit can present options and accept the reader's choice. Specifically, it displays multiple options during the progression of the story and provides an interface that allows the reader to choose the next development. Options are presented at various points in the story, such as branching points, character actions, and changes in situations. The selection unit can accept user choices in real time and reflect them in the next development. The selection unit can centrally manage the selection content and collaborate with other departments as needed. For example, selected data can be stored on a cloud server and made accessible to the modification and generation departments. Furthermore, the selection unit can devise methods for presenting options and design the interface to allow users to make choices intuitively. In this way, the selection unit can efficiently and effectively accept user choices and improve the overall performance of the system.

[0079] The modification unit modifies the story based on the selections received by the selection unit. For example, the modification unit can change the story's progression depending on the choices made by the reader. Specifically, it reflects changes in the story's progression, character actions, and situations in real time based on the selected choices. The modification unit can use generative AI to generate new stories corresponding to the choices and integrate them into the existing story. This allows for the provision of customized stories based on user choices while maintaining narrative consistency. The modification unit centrally manages the modified stories and can collaborate with other departments as needed. For example, the modified data can be stored on a cloud server and made accessible to the display and delivery units. Furthermore, the modification unit can adjust the parameters of the generative AI to improve the efficiency and accuracy of the modification process and obtain optimal modification results. This allows the modification unit to modify high-quality stories based on user choices and improve the overall system performance.

[0080] The display unit displays the stories and illustrations generated by the generation unit. The display unit can display stories and illustrations through, for example, web applications or mobile applications. Specifically, it provides a user-accessible interface, allowing users to view the generated content. The display unit can also display stories and illustrations sent via email. Users can easily view the generated content by opening the received email. Furthermore, the display unit can display stories and illustrations printed on a printer. If desired, the generated content can be provided as a high-quality printed material, allowing users to enjoy it as a physical picture book. The display unit centrally manages and distributes the displayed content and can collaborate with other departments as needed. For example, the displayed data can be stored on a cloud server, making it accessible to the selection and modification units. The display unit can also collect user feedback and continuously improve the accuracy and effectiveness of the displayed content. This allows the display unit to quickly and reliably display customized stories and illustrations to users, improving the overall system performance.

[0081] The generation unit can generate customized stories and illustrations based on user input. For example, the generation unit can generate stories using text generation AI (e.g., LLM). For example, the generation unit can generate stories based on themes, characters, and situations entered by the user. The generation unit can also generate illustrations using image generation AI. For example, the generation unit can generate illustrations based on themes, characters, and situations entered by the user. Furthermore, the generation unit can simultaneously generate stories and illustrations using multimodal generation AI. For example, the generation unit can simultaneously generate stories and illustrations based on themes, characters, and situations entered by the user. This allows the generation unit to provide a personalized learning experience by generating customized stories and illustrations based on user input.

[0082] The selection section can accept the reader's choice. For example, in a scene where the reader chooses how the story unfolds, the selection section can present options and accept the reader's choice. For example, in a scene where the reader chooses whether "the pirates find the treasure or are captured by the enemy," the selection section can present options and accept the reader's choice. The selection section can also present options and accept the reader's choice in a scene where the reader chooses whether "the animals have a party in the forest or go on an adventure." In this way, by accepting the reader's choice, the selection section can change the story's development according to the reader's choice. Some or all of the above processing in the selection section may be performed using AI, for example, or without AI. For example, the selection section can input the reader's choice into AI and have AI present the options.

[0083] The modification function can alter the story based on the reader's choices. For example, the modification function can change the story's progression depending on the choices made by the reader. For example, if the reader chooses "The pirates find the treasure," the modification function can change the story to one where the treasure is found. Similarly, if the reader chooses "The pirates are captured by the enemy," the modification function can change the story to one where the pirates are captured by the enemy. In this way, the modification function can provide a story progression that suits the reader's interests and level of understanding by altering the story based on the reader's choices. Some or all of the above-described processes in the modification function may be performed using AI, for example, or not. For example, the modification function can input the reader's choices into AI and have the AI ​​perform the story changes.

[0084] The display unit can display the generated story and illustrations. The display unit can display the story and illustrations, for example, through a web application or a mobile application. The display unit can, for example, display the generated story and illustrations on the screen of a web application. The display unit can also display the generated story and illustrations on the screen of a mobile application. Furthermore, the display unit can also display the story and illustrations sent via email. The display unit can, for example, display the story and illustrations sent via email on the screen of an email client. The display unit can also display the story and illustrations printed by a printer. The display unit can, for example, display the story and illustrations printed by a printer on paper. In this way, the display unit allows users to visually enjoy the story by displaying the generated story and illustrations. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated story and illustrations into AI and have AI select the display method.

[0085] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is excited, the reception unit can immediately display an interface prompting input and accept input quickly. Alternatively, if the user is relaxed, the reception unit can display an interface prompting input at a slower pace and accept input. Furthermore, if the user is stressed, the reception unit can temporarily suspend input, display relaxing content, and then resume input. This allows the reception unit to accept input at a more appropriate time by adjusting the timing of input acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also automatically display themes and characters that the user has previously entered as suggestions. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the reception desk can improve user convenience by selecting the optimal input method through analysis of the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0087] The input receiving unit can filter input based on the user's current areas of interest. For example, the receiving unit can suggest relevant themes or characters based on keywords the user has recently searched for or their browsing history. It can also narrow down input candidates based on topics the user is currently interested in. Furthermore, the receiving unit can analyze the user's social media activity and display input candidates related to their areas of interest. This allows the receiving unit to receive more relevant input by filtering based on the user's current areas of interest. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0088] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is excited, the reception unit can prioritize displaying important input items and receive input quickly. If the user is relaxed, the reception unit can also display detailed input items and receive input slowly. Furthermore, if the user is stressed, the reception unit can prioritize displaying simple input items to reduce the burden of input. In this way, the reception unit can receive input in a more appropriate order by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reception desk can prioritize inputs that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize suggesting themes or characters related to that region. Furthermore, if the user is traveling, the reception desk can prioritize inputs related to stories or illustrations related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize inputs related to their home or daily life. This allows the reception desk to prioritize inputs that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize highly relevant inputs.

[0090] The reception unit can analyze the user's social media activity and accept relevant input when receiving input. For example, the reception unit can suggest relevant themes or characters based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and display relevant input candidates. Furthermore, the reception unit can narrow down input candidates based on the user's interests on social media. In this way, the reception unit can efficiently accept relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform filtering of relevant inputs.

[0091] The generation unit can estimate the user's emotions and adjust the way the story and illustrations are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a story with a calm tone and illustrations with soft colors. If the user is excited, the generation unit can generate an action-packed story and illustrations with vibrant colors. Furthermore, if the user is sad, the generation unit can generate an emotional story and illustrations with calm colors. In this way, the generation unit can provide more appropriate content by adjusting the way the story and illustrations are presented according to 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 processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the story and illustrations are presented.

[0092] The generation unit can adjust the level of detail of the generated content based on the importance of the input theme. For example, for an important theme, the generation unit can generate a detailed story and high-resolution illustrations. For a general theme, the generation unit can also generate a concise story and standard-resolution illustrations. Furthermore, for a simple theme, the generation unit can generate a short story and simple illustrations. In this way, the generation unit can provide an appropriate level of content by adjusting the level of detail of the generated content based on the importance of the input theme. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input importance data of the input theme into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated content.

[0093] The generation unit can apply different generation algorithms depending on the attributes of the input character during generation. For example, in the case of an animal character, the generation unit can generate cute illustrations and a simple story. In the case of a hero character, the generation unit can also generate dynamic illustrations and an adventurous story. Furthermore, in the case of a fantasy character, the generation unit can generate fantastical illustrations and a story that includes magical elements. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the attributes of the input character. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the attribute data of the input character into a generation AI and have the generation AI select a generation algorithm.

[0094] The generation unit can estimate the user's emotions and adjust the length of the stories and illustrations it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise stories and illustrations. If the user is relaxed, the generation unit can generate longer stories and illustrations with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate stories and illustrations with visually stimulating effects. This allows the generation unit to provide more appropriate content by adjusting the length of stories and illustrations according to 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 processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of stories and illustrations.

[0095] The generation unit can determine the generation priority based on the submission deadline of the input theme during the generation process. For example, in the case of an urgent theme, the generation unit can generate the story and illustrations with the highest priority. It can also generate the story and illustrations with a standard priority for regular themes. Furthermore, for long-term themes, the generation unit can postpone the generation of the story and illustrations. This allows the generation unit to provide content at the appropriate time by determining the generation priority based on the submission deadline of the input theme. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the submission deadline data of the input theme into a generation AI and have the generation AI determine the generation priority.

[0096] The generation unit can adjust the generation order based on the relevance of the input themes during generation. For example, if the theme is highly relevant, the generation unit can generate the story and illustrations first. If the theme is moderately relevant, the generation unit can generate the story and illustrations next. Furthermore, if the theme is low relevance, the generation unit can generate the story and illustrations last. In this way, the generation unit can provide more relevant content by adjusting the generation order based on the relevance of the input themes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the input themes into a generation AI and have the generation AI perform the adjustment of the generation order.

[0097] The service provider can estimate the user's emotions and adjust the format of the stories and illustrations provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a story with a calm tone and illustrations with soft colors. If the user is excited, the service provider can provide an action-packed story and illustrations with vibrant colors. Furthermore, if the user is sad, the service provider can provide an emotional story and illustrations with calm colors. In this way, the service provider can provide more appropriate content by adjusting the format of the stories and illustrations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the story and illustration format.

[0098] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the format of stories the user has enjoyed reading in the past. It can also select the optimal service delivery method based on the style of illustrations the user has used in the past. Furthermore, the service provider can select the optimal service delivery method for a specific time period based on the user's past usage history. In this way, the service provider can improve user convenience by selecting the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal service delivery method.

[0099] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider can provide relevant stories and illustrations based on keywords the user has recently searched for or their browsing history. It can also customize the content based on topics the user is currently interested in. Furthermore, the service provider can analyze the user's social media activity and provide stories and illustrations related to their areas of interest. This allows the service provider to provide more relevant content by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the customization of the content offered.

[0100] The service provider can estimate the user's emotions and determine the priority of stories and illustrations to be provided based on the estimated emotions. For example, if the user is excited, the service provider can prioritize providing action-packed stories and illustrations. If the user is relaxed, the service provider can prioritize providing stories and illustrations with a calm tone. Furthermore, if the user is sad, the service provider can prioritize providing emotionally moving stories and illustrations. In this way, the service provider can deliver content in a more appropriate order by determining the priority of stories and illustrations to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of stories and illustrations.

[0101] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide stories and illustrations related to that region. Furthermore, if the user is traveling, the service provider can provide stories and illustrations related to their travel destination. Additionally, if the user is at home, the service provider can provide stories and illustrations related to their home and daily life. This allows the service provider to select the optimal delivery method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0102] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can provide relevant stories and illustrations based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and provide relevant stories and illustrations. Furthermore, the service provider can customize the content based on the user's interests on social media. This allows the service provider to efficiently provide relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content offered.

[0103] The selection unit can estimate the user's emotions and adjust how the options are displayed based on the estimated emotions. For example, if the user is nervous, the selection unit can display simple and highly visible options. If the user is relaxed, the selection unit can display options containing detailed information. Furthermore, if the user is in a hurry, the selection unit can display options that get straight to the point. In this way, the selection unit can provide more appropriate options by adjusting how the options are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into the generative AI and have the generative AI adjust how the options are displayed.

[0104] The selection unit can present the optimal option by referring to the user's past selection history when a selection is made. For example, the selection unit can present the optimal option based on the story development the user has previously selected. It can also present options that include the user's favorite characters or situations based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and present the option that is most interesting to the user. In this way, the selection unit can present the optimal option by referring to the user's past selection history and improve user convenience. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection history data into a generating AI and have the generating AI perform the task of presenting the optimal option.

[0105] The selection unit can customize the options based on the user's current areas of interest when making a selection. For example, the selection unit can present relevant options based on keywords the user has recently searched for or their browsing history. It can also customize options based on topics the user is currently interested in. Furthermore, the selection unit can analyze the user's social media activity and present options related to their areas of interest. This allows the selection unit to provide more relevant options by customizing them based on the user's current areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the options.

[0106] The selection unit can estimate the user's emotions and determine the priority of options based on the estimated emotions. For example, if the user is excited, the selection unit can prioritize displaying action-packed options. If the user is relaxed, the selection unit can prioritize displaying options with a calm tone. Furthermore, if the user is sad, the selection unit can prioritize displaying emotionally moving options. In this way, the selection unit can provide options in a more appropriate order by determining the priority of options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI determine the priority of options.

[0107] The selection unit can present the most suitable options when a user makes a selection, taking into account the user's geographical location. For example, if the user is in a specific region, the selection unit can present options related to that region. Furthermore, if the user is traveling, the selection unit can present options related to their travel destination. Additionally, if the user is at home, the selection unit can present options related to their home and daily life. This allows the selection unit to present the most suitable options by considering the user's geographical location, thereby improving user convenience. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting the most suitable options.

[0108] The selection unit can analyze the user's social media activity and customize the options at the time of selection. For example, the selection unit can present relevant options based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and present relevant options. Furthermore, the selection unit can customize options based on the user's interests on social media. In this way, the selection unit can efficiently provide relevant options by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of options.

[0109] The modification unit can estimate the user's emotions and adjust how the story is modified based on those emotions. For example, if the user is relaxed, the modification unit can make a gentle story change. If the user is excited, the modification unit can make an action-packed story change. Furthermore, if the user is sad, the modification unit can make an emotional story change. In this way, the modification unit can provide a more appropriate story development by adjusting how the story is modified according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the modification unit may be performed using AI or not using AI. For example, the modification unit can input user emotion data into a generative AI and have the generative AI adjust how the story is modified.

[0110] The modification unit can select the optimal modification method by referring to the user's past selection history when making a change. For example, the modification unit can select the optimal modification method based on the story development the user has previously selected. It can also select a modification method that includes the user's favorite characters or situations from the user's past selection history. Furthermore, the modification unit can analyze the user's past selection history and select the modification method that is most interesting. In this way, the modification unit can select the optimal modification method by referring to the user's past selection history and improve user convenience. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the user's past selection history data into a generating AI and have the generating AI perform the selection of the optimal modification method.

[0111] The modification unit can customize the story based on the user's current areas of interest when making changes. For example, the modification unit can make relevant story changes based on keywords the user has recently searched for or their browsing history. It can also customize the story based on topics the user is currently interested in. Furthermore, the modification unit can analyze the user's social media activity and make story changes related to their areas of interest. This allows the modification unit to provide a more relevant story development by customizing the story based on the user's current areas of interest. Some or all of the above processes in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the user's areas of interest data into a generating AI and have the generating AI perform the story customization.

[0112] The modification unit can estimate the user's emotions and determine the priority of story changes based on the estimated emotions. For example, if the user is excited, the modification unit can prioritize action-packed story changes. If the user is relaxed, it can also prioritize story changes with a calm tone. Furthermore, if the user is sad, it can prioritize emotional story changes. In this way, the modification unit can change the story in a more appropriate order by determining the priority of story changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the modification unit may be performed using AI or not using AI. For example, the modification unit can input user emotion data into a generative AI and have the generative AI determine the priority of story changes.

[0113] The modification unit can select the optimal modification method when making changes, taking into account the user's geographical location information. For example, if the user is in a specific region, the modification unit can make story changes related to that region. Furthermore, if the user is traveling, the modification unit can make story changes related to their travel destination. Additionally, if the user is at home, the modification unit can make story changes related to their home and daily life. This allows the modification unit to select the optimal modification method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal modification method.

[0114] The modification unit can analyze the user's social media activity and customize the story when making changes. For example, the modification unit can make relevant story changes based on the user's recent posts and comments. It can also analyze the activity of accounts and groups the user follows and make relevant story changes. Furthermore, the modification unit can customize the story based on the user's interests on social media. This allows the modification unit to efficiently provide relevant stories by analyzing the user's social media activity. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the user's social media activity data into a generating AI and have the generating AI perform the story customization.

[0115] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. If the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a concise display method. In this way, the display unit can provide a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0116] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can select the optimal display method based on the display method the user has preferred to use in the past. Furthermore, the display unit can select the optimal display method for a specific time period based on the user's past operation history. In addition, the display unit can analyze the user's past operation history and select the display method with the highest visibility. This allows the display unit to improve user convenience by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0117] The display unit can customize the displayed content based on the user's current areas of interest when displaying information. For example, the display unit can provide relevant displayed content based on keywords the user has recently searched for or their browsing history. It can also customize the displayed content based on topics the user is currently interested in. Furthermore, the display unit can analyze the user's social media activity and provide displayed content related to their areas of interest. In this way, the display unit can provide more relevant displays by customizing the displayed content based on the user's current areas of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the displayed content.

[0118] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is excited, the display unit can prioritize providing action-packed content. If the user is relaxed, the display unit can prioritize providing content in a calm tone. Furthermore, if the user is sad, the display unit can prioritize providing emotional content. In this way, the display unit can provide content in a more appropriate order by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0119] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can improve user convenience by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0120] The display unit can analyze the user's social media activity and customize the displayed content at the time of display. For example, the display unit can provide relevant content based on the user's recent posts and comments. It can also analyze the activity of accounts and groups that the user follows and provide relevant content. Furthermore, the display unit can customize the displayed content based on the user's interests on social media. In this way, the display unit can efficiently provide relevant content by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0121] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is excited, the display unit can prioritize providing action-packed content. If the user is relaxed, the display unit can prioritize providing content in a calm tone. Furthermore, if the user is sad, the display unit can prioritize providing emotional content. In this way, the display unit can provide content in a more appropriate order by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also automatically display themes and characters that the user has previously entered as suggestions. Furthermore, the reception desk can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, the reception desk can select the most suitable input method by analyzing the user's past input history, thereby improving user convenience.

[0124] The generation unit can estimate the user's emotions and adjust the way the story and illustrations are presented based on those emotions. For example, if the user is relaxed, it can generate a story with a calm tone and illustrations with soft colors. If the user is excited, it can generate an action-packed story and illustrations with vibrant colors. Furthermore, if the user is sad, it can generate an emotional story and illustrations with calm colors. In this way, the generation unit can provide more appropriate content by adjusting the way the story and illustrations are presented according to the user's emotions.

[0125] The selection unit can estimate the user's emotions and adjust how the options are displayed based on those emotions. For example, if the user is nervous, it can display simple, easy-to-read options. If the user is relaxed, it can display options with more detailed information. Furthermore, if the user is in a hurry, it can display options that get straight to the point. In this way, the selection unit can provide more appropriate options by adjusting how the options are displayed according to the user's emotions.

[0126] The modification function can estimate the user's emotions and adjust how the story is changed based on those emotions. For example, if the user is relaxed, the story can be changed to a calmer tone. If the user is excited, the story can be changed to an action-packed one. Furthermore, if the user is sad, the story can be changed to an emotional one. In this way, the modification function can provide a more appropriate story development by adjusting how the story is changed according to the user's emotions.

[0127] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the display unit can provide a more appropriate display by adjusting the display method according to the user's emotions.

[0128] The input system can filter input based on the user's current areas of interest. For example, it can suggest relevant themes and characters based on keywords the user has recently searched for and their browsing history. The input system can also narrow down input suggestions based on topics the user is currently interested in. Furthermore, the input system can analyze the user's social media activity and display input suggestions related to their areas of interest. This allows the input system to receive more relevant input by filtering based on the user's current areas of interest.

[0129] The generation unit can adjust the level of detail of the generated content based on the importance of the input theme. For example, for an important theme, it can generate a detailed story and high-resolution illustrations. For a general theme, it can generate a concise story and illustrations of standard resolution. Furthermore, for a simple theme, it can generate a short story and simple illustrations. In this way, the generation unit can provide content at an appropriate level by adjusting the level of detail of the generated content based on the importance of the input theme.

[0130] The service provider can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, it can select the optimal delivery method based on the format of stories the user has enjoyed reading in the past. It can also select the optimal delivery method based on the style of illustrations the user has used in the past. Furthermore, it can select the optimal delivery method for a specific time period based on the user's past usage history. This allows the service provider to select the optimal delivery method by referring to the user's past usage history, thereby improving user convenience.

[0131] The selection function can present the most suitable options by referring to the user's past selection history. For example, it can present the best options based on the story progression the user has previously selected. It can also present options that include the user's preferred characters or situations based on their past selection history. Furthermore, it can analyze the user's past selection history and present the most interesting options. In this way, the selection function can present the most suitable options by referring to the user's past selection history, thereby improving user convenience.

[0132] The display unit can select the optimal display method by considering the user's device information when displaying information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method by considering the user's device information, thereby improving user convenience.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, users can provide information such as the theme, characters, and situation of a picture book via text input. They can also convert dictated content into text using voice input, or input illustrations or photographs using image input. Step 2: The generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. The generation unit can generate stories using text generation AI (e.g., LLM) and illustrations using image generation AI. It is also possible to generate stories and illustrations simultaneously using multimodal generation AI. Step 3: The provider unit provides the user with the story and illustrations generated by the generator unit. The provider unit can display the story and illustrations through web applications or mobile applications. They can also be sent via email or printed using a printer. Step 4: The choice section accepts the reader's choice. The choice section can present options at points in the story where the reader chooses how to proceed, and accept their choice. Step 5: The modification section modifies the story based on the selections received by the selection section. The modification section can change the development of the story depending on the choices made by the reader. Step 6: The display unit displays the story and illustrations generated by the generation unit. The display unit can display the story and illustrations through web applications and mobile applications. It can also display stories and illustrations sent via email or printed from a printer.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, selection unit, modification unit, and display 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 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized story and illustration. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated story and illustration to the user. The selection unit is implemented by the touch panel 38A of the smart device 14 and receives the reader's selection. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the story based on the selection. The display unit is implemented by the display 40A of the smart device 14 and displays the generated story and illustration. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, selection unit, modification unit, and display unit, is implemented, for example, by 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 and receives voice input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a customized story and illustration. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated story and illustration to the user. The selection unit is implemented, for example, by the touch panel 38A of the smart glasses 214 and receives the reader's selection. The modification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and modifies the story based on the selection. The display unit is implemented, for example, by the display 40A of the smart glasses 214 and displays the generated story and illustration. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, selection unit, modification unit, and display 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 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a customized story and illustration. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated story and illustration to the user. The selection unit is implemented by, for example, the touch panel 38A of the headset terminal 314 and receives the reader's selection. The modification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and modifies the story based on the selection. The display unit is implemented by, for example, the display 40A of the headset terminal 314 and displays the generated story and illustration. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, selection unit, modification unit, and display 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 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a customized story and illustration. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated story and illustration to the user. The selection unit is implemented by, for example, the touch panel 38A of the robot 414 and receives the reader's selection. The modification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and modifies the story based on the selection. The display unit is implemented by, for example, the display 40A of the robot 414 and displays the generated story and illustration. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A reception area that receives user input, A generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. A providing unit that provides the story and illustrations generated by the aforementioned generation unit, A section that accepts reader choices, A modification unit that modifies the story based on the selection received by the selection unit, The system includes a display unit that displays the story and illustrations generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generates customized stories and illustrations based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Accepting reader choices The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modified part is, The story will be changed based on reader choices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Display the generated story and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated story and illustrations to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. 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 determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the way the story and illustrations are presented 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, adjust the level of detail based on the importance of the entered theme. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the attributes of the input character. 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 stories and illustrations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on the submission date of the entered theme. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation order is adjusted based on the relevance of the input themes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the format of the stories and illustrations we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the stories and illustrations to be presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity to customize the content offered. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is It estimates the user's emotions and adjusts how options are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is When a selection is made, the system refers to the user's past selection history to suggest the most suitable option. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is When selecting an option, customize the choices based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is When making a selection, the system will consider the user's geographical location to present the most suitable options. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned selection unit is When making a selection, the system analyzes the user's social media activity to customize the options. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned modified part is, It estimates the user's emotions and adjusts how the story changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned modified part is, When making changes, the system will refer to the user's past selection history to select the most appropriate method of change. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned modified part is, When changes are made, the story is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned modified part is, The system estimates user sentiment and determines story change priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned modified part is, When making changes, the system will select the most appropriate method of change, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned modified part is, When making changes, analyze users' social media activity to customize the story. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned display unit is When displaying content, customize the displayed content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned display unit is When displaying content, the system analyzes the user's social media activity to customize the displayed content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 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 area that receives user input, A generation unit analyzes the information received by the reception unit and generates a customized story and illustrations. A providing unit that provides the story and illustrations generated by the aforementioned generation unit, A section that accepts reader choices, A modification unit that modifies the story based on the selection received by the selection unit, The system includes a display unit that displays the story and illustrations generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generates customized stories and illustrations based on user input. The system according to feature 1.

3. The aforementioned selection unit is Accepting reader choices The system according to feature 1.

4. The aforementioned modified part is, The story will be changed based on reader choices. The system according to feature 1.

5. The aforementioned display unit is Display the generated story and illustrations. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated story and illustrations to the user. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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