system

Abstract

The system according to the embodiment aims to automatically generate a picture book that meets the user's request. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a combination unit. The reception unit receives elements of a picture book theme, characters, and scenario from a user. The generation unit analyzes the information received by the reception unit and generates a story. The generation unit generates illustrations for the picture book based on the story generated by the generation unit. The combination unit combines the story and illustrations generated by the generation unit to automatically generate a picture book that can be read aloud.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to automatically generate picture books that meet users' requests, and there is room for improvement to meet individual needs.

[0005] The system according to the embodiment aims to automatically generate a picture book that meets the user's request. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a combination unit. The reception unit receives themes, characters, and scenario elements of a picture book from a user. The generation unit analyzes the information received by the reception unit and generates a story. The generation unit generates illustrations for the picture book based on the story generated by the generation unit. The combination unit combines the story and illustrations generated by the generation unit to automatically generate a picture book that can be read aloud. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a picture book that meets the user's request. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The picture book automatic generation system according to an embodiment of the present invention utilizes an image generation AI and a generation AI to automatically generate a picture book that can be read aloud and supports the development of emotions and interpersonal relationships, such as fostering self-development, empathy, and cooperation. This system allows users to input elements of the picture book's theme, characters, and scenario. The generation AI analyzes the input information to generate a story. The image generation AI then generates illustrations for the picture book based on the generated story. Finally, the system automatically generates a picture book that can be read aloud by combining the generated story and illustrations. For example, if a user inputs themes such as "friendship" and "cooperation" or "animal characters," the generation AI analyzes this information and generates a story in which animals work together to overcome challenges. Next, the image generation AI generates illustrations corresponding to each scene in the generated story, such as an illustration of "animals working together to build a bridge." Finally, the system automatically generates a picture book that can be read aloud by combining the generated story and illustrations. This allows users to easily create picture books that support the development of emotions and interpersonal relationships. This automatic picture book generation system can support children's emotional and interpersonal development.

[0029] The picture book automatic generation system according to the embodiment includes a reception unit, a generation unit, and a combination unit. The reception unit receives a picture book theme, characters, and scenario elements from a user. For example, the user can input themes such as "friendship" or "cooperation" or "animal characters." The generation unit uses a generation AI to analyze the information received by the reception unit and generate a story. For example, the generation AI generates a story about animals working together to overcome difficulties based on the input theme, characters, and scenario elements. The generation unit uses an image generation AI to generate illustrations corresponding to each scene in the generated story. For example, the image generation AI generates an illustration of a "scene in which animals work together to build a bridge." The combination unit combines the generated story and illustrations to automatically generate a picture book that can be read aloud. For example, the combination unit arranges the generated story and illustrations on each page and formats the story into a picture book. As a result, the picture book automatic generation system according to the embodiment can automatically generate a story and illustrations based on user input and provide a picture book that can be read aloud.

[0030] The reception unit can analyze the user's past input history and automatically complete appropriate themes, characters, and scenario elements. The reception unit, for example, suggests new related themes based on themes previously selected by the user. For example, the reception unit can store the user's past input history in a database and analyze it using data mining technology. The reception unit can also suggest new related characters based on characters previously selected by the user. For example, the reception unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The reception unit can also suggest new related scenarios based on elements of scenarios previously selected by the user. For example, the reception unit can classify the user's past input history using clustering technology and suggest similar elements. This makes it possible to provide a more personalized picture book by automatically completing optimal themes, characters, and scenario elements based on the user's past input history.

[0031] The reception unit can filter appropriate themes, characters, and scenario elements based on the user's age and interests. For example, if the user is a young child, the reception unit can suggest simple and easy-to-understand themes and characters. For example, the reception unit can receive the user's age as input data and perform filtering according to the age. Furthermore, if the user is an elementary school student, the reception unit can suggest scenarios with themes of learning and adventure. For example, the reception unit can collect the user's interests in the form of a questionnaire and perform filtering based on the interests. Furthermore, if the user is a junior high school student or older, the reception unit can suggest complex stories and deep themes. For example, the reception unit can set categories according to the user's age group and perform filtering based on the categories. This makes it possible to generate a more appropriate picture book by providing themes, characters, and scenario elements according to the user's age and interests.

[0032] The reception unit can suggest regionally specific themes, characters, and scenario elements based on the user's geographical location information. For example, if the user is in Japan, the reception unit can suggest elements of a picture book themed on Japanese traditions and culture. For example, the reception unit can acquire the user's geographical location information as GPS data and suggest regionally specific themes. Furthermore, if the user is in the United States, the reception unit can suggest elements of a picture book themed on American history or scenery. For example, the reception unit can suggest characters related to regional culture or events based on the user's geographical location information. Furthermore, if the user is in Europe, the reception unit can suggest elements of a picture book themed on European myths and legends. For example, the reception unit can analyze the user's geographical location information and suggest regionally specific scenarios. This makes it possible to generate regionally specific picture books by providing themes, characters, and scenario elements based on the user's geographical location information.

[0033] The reception unit can analyze the user's social media activities and suggest related themes, characters, and scenario elements. For example, the reception unit can suggest related themes based on content that the user frequently shares on social media. For example, the reception unit can link the user's social media accounts and analyze the content of their posts. The reception unit can also suggest related characters based on the content of accounts the user follows. For example, the reception unit can analyze the attributes of the user's followers and suggest characters based on their interests. The reception unit can also suggest related scenarios based on the activities of groups in which the user participates. For example, the reception unit can collect data on the user's group activities and generate related scenarios. This makes it possible to create a more personalized picture book by providing themes, characters, and scenario elements based on the user's social media activities.

[0034] When generating a story, the generation unit can refer to the user's past selection history to generate a more personalized story. The generation unit can generate a new related story based on, for example, a theme selected by the user in the past. For example, the generation unit can store the user's past selection history in a database and analyze it using data mining technology. The generation unit can also generate a new related story based on characters selected by the user in the past. For example, the generation unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The generation unit can also generate a new related story based on elements of scenarios selected by the user in the past. For example, the generation unit can classify the user's past selection history using clustering technology and suggest similar elements. This makes it possible to generate a more personalized picture book by providing a story based on the user's past selection history.

[0035] When generating a story, the generator can combine stories of different genres and styles to generate a new story. For example, the generator can generate a story that combines fantasy and contemporary drama. For example, the generator can select stories of different genres from a database and use an algorithm to combine them. The generator can also generate a story that combines adventure and mystery. For example, the generator can analyze stories of different styles, find common themes, and combine them. The generator can also generate a story that combines comedy and romance. For example, the generator can randomly combine stories of different genres and styles and use an algorithm to generate a new story. This allows for the generation of a wider variety of picture books by providing new stories that combine different genres and styles.

[0036] When generating stories, the generation unit can determine the priority of stories based on the time of submission by the user. For example, if the user is in a hurry, the generation unit prioritizes stories that can be generated in the shortest time. For example, the generation unit can receive the time of submission by the user as input data and use an algorithm to determine the priority. Also, if the user needs a story for a specific event, the generation unit can prioritize the story that is best suited to that event. For example, the generation unit can store the time of submission by the user in a database and determine the order in which stories are generated based on the time of submission. Also, if the user is relaxed, the generation unit can take its time to generate detailed stories. For example, the generation unit can analyze the time of submission by the user and determine the priority of stories according to the time of submission. This makes it possible to generate a more appropriate picture book by providing the priority of stories based on the time of submission by the user.

[0037] When generating a story, the generation unit can generate a new story by referring to a related past story of the user. The generation unit, for example, generates a sequel to a story previously created by the user. For example, the generation unit can store the user's past stories in a database and use an algorithm that references related stories. The generation unit can also reintroduce characters from stories previously created by the user. For example, the generation unit can analyze the user's past stories and suggest the reintroduction of characters. The generation unit can also reuse themes from stories previously created by the user. For example, the generation unit can classify the user's past stories using clustering technology and suggest similar themes. This allows for the generation of a more personalized picture book by providing new stories based on the user's past stories.

[0038] When generating illustrations, the generation unit can add special effects to emphasize important scenes in the story. For example, the generation unit adds light effects to climax scenes to emphasize them. For example, the generation unit can use an algorithm that uses image generation AI to add effects to specific scenes. The generation unit can also add tear effects to moving scenes to enhance emotions. For example, the generation unit can analyze the importance of a scene and determine the timing to add effects. The generation unit can also add movement effects to action scenes to enhance realism. For example, the generation unit can use an algorithm that analyzes the movement of a scene and adds effects. This makes it possible to create a more impressive picture book by providing effects that emphasize important scenes in the story.

[0039] When generating illustrations, the generation unit can combine different art styles to create new visual expressions. For example, the generation unit can generate illustrations that combine watercolor paintings and digital art. For example, the generation unit can select illustrations of different art styles from a database and use an algorithm to combine them. The generation unit can also generate illustrations that combine manga and realistic styles. For example, the generation unit can analyze illustrations of different styles, find common themes, and combine them. The generation unit can also generate illustrations that combine abstract and figurative paintings. For example, the generation unit can use an algorithm that randomly combines illustrations of different art styles to create new visual expressions. This allows for the generation of more diverse picture books by providing new visual expressions that combine different art styles.

[0040] When generating illustrations, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation unit can generate illustrations incorporating Japanese scenery and culture. For example, the generation unit can acquire the user's geographical location information as GPS data and incorporate region-specific elements. Also, if the user is in the United States, the generation unit can generate illustrations incorporating American scenery and culture. For example, the generation unit can suggest visual elements related to regional culture and events based on the user's geographical location information. Also, if the user is in Europe, the generation unit can generate illustrations incorporating European scenery and culture. For example, the generation unit can analyze the user's geographical location information and suggest region-specific visual elements. This makes it possible to generate a more personalized picture book by providing region-specific elements based on the user's geographical location information.

[0041] The generation unit can analyze the user's social media activities and incorporate relevant visual elements when generating illustrations. The generation unit incorporates relevant visual elements, for example, based on content that the user frequently shares on social media. For example, the generation unit can link the user's social media accounts and analyze the content of posts. The generation unit can also incorporate relevant visual elements based on the content of accounts the user follows. For example, the generation unit can analyze the attributes of the user's followers and suggest visual elements based on their interests. The generation unit can also incorporate relevant visual elements based on the activities of groups the user participates in. For example, the generation unit can collect data on the user's group activities and generate relevant visual elements. This makes it possible to generate a more personalized picture book by providing visual elements based on the user's social media activities.

[0042] When combining, the combination unit can generate a more personalized picture book by referring to the user's past selection history. The combination unit can generate a new related picture book based on, for example, a theme selected by the user in the past. For example, the combination unit can store the user's past selection history in a database and analyze it using data mining technology. The combination unit can also generate a new related picture book based on characters selected by the user in the past. For example, the combination unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The combination unit can also generate a new related picture book based on elements of scenarios selected by the user in the past. For example, the combination unit can classify the user's past selection history using clustering technology and suggest similar elements. This makes it possible to generate a more personalized picture book by providing a picture book based on the user's past selection history.

[0043] When combining stories and illustrations, the combination unit can try different combinations of stories and illustrations to find a suitable combination. For example, the combination unit can randomly combine different stories and illustrations to find the optimal combination. For example, the combination unit can select different stories and illustrations from a database and use an algorithm to randomly combine them. The combination unit can also combine different stories and illustrations based on the user's preferences. For example, the combination unit can analyze the user's past selection history and suggest combinations based on the preferences. The combination unit can also combine different stories and illustrations based on the user's past selection history. For example, the combination unit can classify the user's past selection history using clustering technology and combine similar elements. This allows a more optimal picture book to be generated by trying out different combinations of stories and illustrations.

[0044] When combining, the combination unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the combination unit generates a picture book incorporating Japanese scenery and culture. For example, the combination unit can acquire the user's geographical location information as GPS data and incorporate region-specific elements. Also, if the user is in the United States, the combination unit can generate a picture book incorporating American scenery and culture. For example, the combination unit can suggest elements related to regional culture and events based on the user's geographical location information. Also, if the user is in Europe, the combination unit can generate a picture book incorporating European scenery and culture. For example, the combination unit can analyze the user's geographical location information and suggest region-specific elements. This makes it possible to generate a more personalized picture book by providing region-specific elements based on the user's geographical location information.

[0045] The combination unit can analyze the user's social media activities and incorporate related elements when combining. The combination unit incorporates related elements, for example, based on content that the user frequently shares on social media. For example, the combination unit can link the user's social media accounts and analyze the content of posts. The combination unit can also incorporate related elements based on the content of accounts the user follows. For example, the combination unit can analyze the attributes of the user's followers and suggest elements based on their interests. The combination unit can also incorporate related elements based on the activity content of groups in which the user participates. For example, the combination unit can collect data on the user's group activities and generate related elements. This makes it possible to generate a more personalized picture book by providing elements based on the user's social media activities.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can also suggest related educational elements based on the user's input. For example, if the user inputs "animal characters," the reception unit can suggest scenarios that include educational information about the ecology and habits of animals. If the user selects the theme of "space," the reception unit can suggest scenarios that include basic knowledge about space and information about astronomy. If the user selects the theme of "history," the reception unit can suggest scenarios that include educational information about historical events and people. This allows the automatic picture book generation system to provide picture books that incorporate educational elements as well as emotional and interpersonal development.

[0048] The reception unit can also analyze the user's past input history and suggest themes, characters, and scenario elements that correspond to the user's growth and changes. For example, if the theme the user previously selected was "friendship," the reception unit can next suggest related themes such as "cooperation" or "trust." If the character the user previously selected was "animal," the reception unit can next suggest new characters such as "fantasy creatures" or "historical figures." Furthermore, if the scenario element the user previously selected was "adventure," the reception unit can next suggest new scenarios such as "mystery" or "learning." This makes it possible to generate a more personalized picture book by providing themes, characters, and scenario elements that correspond to the user's growth and changes.

[0049] The reception unit not only filters suitable themes, characters, and scenario elements based on the user's age and interests, but also makes suggestions according to the user's learning style. For example, if the user is a visual learner, the reception unit can suggest a scenario that includes many visually appealing illustrations and diagrams. If the user is an auditory learner, the reception unit can suggest a scenario that incorporates a lot of audio and music. Furthermore, if the user is an experiential learner, the reception unit can suggest a scenario that includes interactive elements. In this way, a more effective picture book can be generated by providing themes, characters, and scenario elements that match the user's learning style.

[0050] The reception unit can also suggest themes, characters, and scenario elements that match the seasons and climates of the region based on the user's geographical location information. For example, if the user is in winter in Japan, the reception unit can suggest picture book elements with a theme of snow and winter scenery. If the user is in summer in America, the reception unit can suggest picture book elements with a theme of beaches and summer activities. Furthermore, if the user is in autumn in Europe, the reception unit can suggest picture book elements with a theme of autumn leaves and autumn harvests. In this way, a more personalized picture book can be generated by providing themes, characters, and scenario elements that match the seasons and climates based on the user's geographical location information.

[0051] The reception unit can also analyze the user's social media activity and suggest themes, characters, and scenario elements based on social issues and trends that interest the user. For example, if the user is interested in environmental issues, the reception unit can suggest elements for a picture book with an environmental protection theme. If the user is interested in diversity and inclusion, the reception unit can suggest scenarios that incorporate diverse characters and cultures. Furthermore, if the user is interested in technology and future trends, the reception unit can suggest elements for a picture book with a future technology or invention theme. This makes it possible to generate a more personalized picture book by providing themes, characters, and scenario elements that match social issues and trends based on the user's social media activity.

[0052] When generating a story, the generator can refer to the user's past selection history and incorporate surprise elements based on the user's preferences and interests. For example, a surprise element such as a reappearance of a character previously selected by the user can be incorporated. A new development related to a theme previously selected by the user can also be incorporated. Furthermore, an unexpected ending can be incorporated based on elements of a scenario previously selected by the user. In this way, a more personalized picture book can be generated by providing surprise elements based on the user's past selection history.

[0053] When generating a story, the generator not only generates a new story by combining stories of different genres and styles, but also branches the story's progression according to the user's selection. For example, if the user selects a story that combines fantasy and contemporary drama, the generator can present the user with choices during the story, allowing the story to proceed in different directions depending on the choices. If the user selects a story that combines adventure and mystery, the generator can present the user with puzzle-solving choices during the story, allowing the story to unfold differently depending on the choices. Furthermore, if the user selects a story that combines comedy and romance, the generator can present the user with emotional choices during the story, allowing the story to reach different endings depending on the choices. This not only provides new stories that combine different genres and styles, but also provides branching stories according to the user's selection, thereby generating a more interactive picture book.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives the theme, characters, and scenario elements of the picture book from the user. For example, the user can input themes such as "friendship" or "cooperation" or "animal characters." Step 2: The generator uses the AI ​​to analyze the information received by the reception unit and generate a story. For example, the AI ​​generates a story about animals working together to overcome challenges based on the input theme, characters, and scenario elements. Step 3: The generator uses image generation AI to generate illustrations corresponding to each scene in the generated story. For example, the image generation AI generates an illustration of a scene in which animals work together to build a bridge. Step 4: The combination unit combines the generated story and illustrations to automatically generate a picture book that can be read aloud. For example, the combination unit arranges the generated story and illustrations on each page to form a picture book.

[0056] (Example 2) The picture book automatic generation system according to an embodiment of the present invention utilizes an image generation AI and a generation AI to automatically generate a picture book that can be read aloud and supports the development of emotions and interpersonal relationships, such as fostering self-development, empathy, and cooperation. This system allows users to input elements of the picture book's theme, characters, and scenario. The generation AI analyzes the input information to generate a story. The image generation AI then generates illustrations for the picture book based on the generated story. Finally, the system automatically generates a picture book that can be read aloud by combining the generated story and illustrations. For example, if a user inputs themes such as "friendship" and "cooperation" or "animal characters," the generation AI analyzes this information and generates a story in which animals work together to overcome challenges. Next, the image generation AI generates illustrations corresponding to each scene in the generated story, such as an illustration of "animals working together to build a bridge." Finally, the system automatically generates a picture book that can be read aloud by combining the generated story and illustrations. This allows users to easily create picture books that support the development of emotions and interpersonal relationships. This automatic picture book generation system can support children's emotional and interpersonal development.

[0057] The picture book automatic generation system according to the embodiment includes a reception unit, a generation unit, and a combination unit. The reception unit receives a picture book theme, characters, and scenario elements from a user. For example, the user can input themes such as "friendship" or "cooperation" or "animal characters." The generation unit uses a generation AI to analyze the information received by the reception unit and generate a story. For example, the generation AI generates a story about animals working together to overcome difficulties based on the input theme, characters, and scenario elements. The generation unit uses an image generation AI to generate illustrations corresponding to each scene in the generated story. For example, the image generation AI generates an illustration of a "scene in which animals work together to build a bridge." The combination unit combines the generated story and illustrations to automatically generate a picture book that can be read aloud. For example, the combination unit arranges the generated story and illustrations on each page and formats the story into a picture book. As a result, the picture book automatic generation system according to the embodiment can automatically generate a story and illustrations based on user input and provide a picture book that can be read aloud.

[0058] The reception unit can estimate the user's emotions and suggest picture book themes, characters, and scenario elements based on the estimated user emotions. For example, if the user is sad, the reception unit can suggest picture book elements with themes of encouragement and hope. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is happy, the reception unit can suggest picture book elements with themes of fun adventures and friendship. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Also, if the user is feeling anxious, the reception unit can suggest characters and scenarios that inspire a sense of security. For example, the reception unit can analyze the user's text input and estimate the emotion using text analysis technology. This allows for the provision of a more personalized picture book by suggesting themes, characters, and scenario elements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0059] The reception unit can analyze the user's past input history and automatically complete appropriate themes, characters, and scenario elements. The reception unit, for example, suggests new related themes based on themes previously selected by the user. For example, the reception unit can store the user's past input history in a database and analyze it using data mining technology. The reception unit can also suggest new related characters based on characters previously selected by the user. For example, the reception unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The reception unit can also suggest new related scenarios based on elements of scenarios previously selected by the user. For example, the reception unit can classify the user's past input history using clustering technology and suggest similar elements. This makes it possible to provide a more personalized picture book by automatically completing optimal themes, characters, and scenario elements based on the user's past input history.

[0060] The reception unit can filter appropriate themes, characters, and scenario elements based on the user's age and interests. For example, if the user is a young child, the reception unit can suggest simple and easy-to-understand themes and characters. For example, the reception unit can receive the user's age as input data and perform filtering according to the age. Furthermore, if the user is an elementary school student, the reception unit can suggest scenarios with themes of learning and adventure. For example, the reception unit can collect the user's interests in the form of a questionnaire and perform filtering based on the interests. Furthermore, if the user is a junior high school student or older, the reception unit can suggest complex stories and deep themes. For example, the reception unit can set categories according to the user's age group and perform filtering based on the categories. This makes it possible to generate a more appropriate picture book by providing themes, characters, and scenario elements according to the user's age and interests.

[0061] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. For example, the reception unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and adjust the color of the interface. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. For example, the reception unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the interface design. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. For example, the reception unit can analyze the user's text input, estimate the user's emotions using text analysis technology, and adjust the interface layout. This can improve the user experience by providing an input interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The reception unit can suggest regionally specific themes, characters, and scenario elements based on the user's geographical location information. For example, if the user is in Japan, the reception unit can suggest elements of a picture book themed on Japanese traditions and culture. For example, the reception unit can acquire the user's geographical location information as GPS data and suggest regionally specific themes. Furthermore, if the user is in the United States, the reception unit can suggest elements of a picture book themed on American history or scenery. For example, the reception unit can suggest characters related to regional culture or events based on the user's geographical location information. Furthermore, if the user is in Europe, the reception unit can suggest elements of a picture book themed on European myths and legends. For example, the reception unit can analyze the user's geographical location information and suggest regionally specific scenarios. This makes it possible to generate regionally specific picture books by providing themes, characters, and scenario elements based on the user's geographical location information.

[0063] The reception unit can analyze the user's social media activities and suggest related themes, characters, and scenario elements. For example, the reception unit can suggest related themes based on content that the user frequently shares on social media. For example, the reception unit can link the user's social media accounts and analyze the content of their posts. The reception unit can also suggest related characters based on the content of accounts the user follows. For example, the reception unit can analyze the attributes of the user's followers and suggest characters based on their interests. The reception unit can also suggest related scenarios based on the activities of groups in which the user participates. For example, the reception unit can collect data on the user's group activities and generate related scenarios. This makes it possible to create a more personalized picture book by providing themes, characters, and scenario elements based on the user's social media activities.

[0064] The generation unit can estimate the user's emotions and adjust the tone and atmosphere of the story based on the estimated user emotions. For example, if the user is sad, the generation unit can generate a story with a theme of encouragement or hope. For example, the generation unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the tone of the story. Alternatively, if the user is happy, the generation unit can generate a story with a theme of fun adventure or friendship. For example, the generation unit can record the user's voice, estimate the emotion using voice analysis technology, and adjust the atmosphere of the story. Alternatively, if the user is feeling anxious, the generation unit can generate a story that provides a sense of security. For example, the generation unit can analyze the user's text input, estimate the emotion using text analysis technology, and adjust the tone of the story. This allows for the creation of a picture book that is more in tune with the user's emotions by providing a story with a tone and atmosphere that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0065] When generating a story, the generation unit can refer to the user's past selection history to generate a more personalized story. The generation unit can generate a new related story based on, for example, a theme selected by the user in the past. For example, the generation unit can store the user's past selection history in a database and analyze it using data mining technology. The generation unit can also generate a new related story based on characters selected by the user in the past. For example, the generation unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The generation unit can also generate a new related story based on elements of scenarios selected by the user in the past. For example, the generation unit can classify the user's past selection history using clustering technology and suggest similar elements. This makes it possible to generate a more personalized picture book by providing a story based on the user's past selection history.

[0066] When generating a story, the generator can combine stories of different genres and styles to generate a new story. For example, the generator can generate a story that combines fantasy and contemporary drama. For example, the generator can select stories of different genres from a database and use an algorithm to combine them. The generator can also generate a story that combines adventure and mystery. For example, the generator can analyze stories of different styles, find common themes, and combine them. The generator can also generate a story that combines comedy and romance. For example, the generator can randomly combine stories of different genres and styles and use an algorithm to generate a new story. This allows for the generation of a wider variety of picture books by providing new stories that combine different genres and styles.

[0067] The generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise story. For example, the generation unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and adjust the length of the story. Furthermore, if the user is relaxed, the generation unit can generate a longer story with detailed explanations. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the length of the story. Furthermore, if the user is excited, the generation unit can generate a story with visually stimulating effects. For example, the generation unit can analyze the user's text input, estimate the user's emotions using text analysis technology, and adjust the length of the story. This allows for the creation of a more appropriate picture book by providing a story length that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When generating stories, the generation unit can determine the priority of stories based on the time of submission by the user. For example, if the user is in a hurry, the generation unit prioritizes stories that can be generated in the shortest time. For example, the generation unit can receive the time of submission by the user as input data and use an algorithm to determine the priority. Also, if the user needs a story for a specific event, the generation unit can prioritize the story that is best suited to that event. For example, the generation unit can store the time of submission by the user in a database and determine the order in which stories are generated based on the time of submission. Also, if the user is relaxed, the generation unit can take its time to generate detailed stories. For example, the generation unit can analyze the time of submission by the user and determine the priority of stories according to the time of submission. This makes it possible to generate a more appropriate picture book by providing the priority of stories based on the time of submission by the user.

[0069] When generating a story, the generation unit can generate a new story by referring to a related past story of the user. The generation unit, for example, generates a sequel to a story previously created by the user. For example, the generation unit can store the user's past stories in a database and use an algorithm that references related stories. The generation unit can also reintroduce characters from stories previously created by the user. For example, the generation unit can analyze the user's past stories and suggest the reintroduction of characters. The generation unit can also reuse themes from stories previously created by the user. For example, the generation unit can classify the user's past stories using clustering technology and suggest similar themes. This allows for the generation of a more personalized picture book by providing new stories based on the user's past stories.

[0070] The generation unit can estimate the user's emotions and adjust the color tone and style of the illustrations based on the estimated user emotions. For example, if the user is sad, the generation unit can generate an illustration with warm colors that conveys a sense of security. For example, the generation unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the color tone of the illustration. Alternatively, if the user is happy, the generation unit can generate an illustration with bright colors that conveys a cheerful atmosphere. For example, the generation unit can record the user's voice, estimate the emotion using voice analysis technology, and adjust the style of the illustration. Alternatively, if the user is anxious, the generation unit can generate an illustration with calm colors that conveys a sense of security. For example, the generation unit can analyze the user's text input, estimate the emotion using text analysis technology, and adjust the color tone of the illustration. This allows for the creation of a picture book that is more in line with the user's emotions by providing illustrations with colors and styles that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] When generating illustrations, the generation unit can add special effects to emphasize important scenes in the story. For example, the generation unit adds light effects to climax scenes to emphasize them. For example, the generation unit can use an algorithm that uses image generation AI to add effects to specific scenes. The generation unit can also add tear effects to moving scenes to enhance emotions. For example, the generation unit can analyze the importance of a scene and determine the timing to add effects. The generation unit can also add movement effects to action scenes to enhance realism. For example, the generation unit can use an algorithm that analyzes the movement of a scene and adds effects. This makes it possible to create a more impressive picture book by providing effects that emphasize important scenes in the story.

[0072] When generating illustrations, the generation unit can combine different art styles to create new visual expressions. For example, the generation unit can generate illustrations that combine watercolor paintings and digital art. For example, the generation unit can select illustrations of different art styles from a database and use an algorithm to combine them. The generation unit can also generate illustrations that combine manga and realistic styles. For example, the generation unit can analyze illustrations of different styles, find common themes, and combine them. The generation unit can also generate illustrations that combine abstract and figurative paintings. For example, the generation unit can use an algorithm that randomly combines illustrations of different art styles to create new visual expressions. This allows for the generation of more diverse picture books by providing new visual expressions that combine different art styles.

[0073] The generation unit can estimate the user's emotions and adjust the level of detail of the illustrations based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates simple, to-the-point illustrations. For example, the generation unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and adjust the level of detail of the illustrations. Furthermore, if the user is relaxed, the generation unit can generate illustrations with detailed depictions. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the level of detail of the illustrations. Furthermore, if the user is excited, the generation unit can generate illustrations with visually stimulating effects. For example, the generation unit can analyze the user's text input, estimate the user's emotions using text analysis technology, and adjust the level of detail of the illustrations. This allows for the creation of more appropriate picture books by providing illustrations with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] When generating illustrations, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation unit can generate illustrations incorporating Japanese scenery and culture. For example, the generation unit can acquire the user's geographical location information as GPS data and incorporate region-specific elements. Also, if the user is in the United States, the generation unit can generate illustrations incorporating American scenery and culture. For example, the generation unit can suggest visual elements related to regional culture and events based on the user's geographical location information. Also, if the user is in Europe, the generation unit can generate illustrations incorporating European scenery and culture. For example, the generation unit can analyze the user's geographical location information and suggest region-specific visual elements. This makes it possible to generate a more personalized picture book by providing region-specific elements based on the user's geographical location information.

[0075] The generation unit can analyze the user's social media activities and incorporate relevant visual elements when generating illustrations. The generation unit incorporates relevant visual elements, for example, based on content that the user frequently shares on social media. For example, the generation unit can link the user's social media accounts and analyze the content of posts. The generation unit can also incorporate relevant visual elements based on the content of accounts the user follows. For example, the generation unit can analyze the attributes of the user's followers and suggest visual elements based on their interests. The generation unit can also incorporate relevant visual elements based on the activities of groups the user participates in. For example, the generation unit can collect data on the user's group activities and generate relevant visual elements. This makes it possible to generate a more personalized picture book by providing visual elements based on the user's social media activities.

[0076] The combination unit can estimate the user's emotions and adjust the combination method of the story and illustrations based on the estimated user emotions. For example, if the user is sad, the combination unit can combine stories and illustrations themed around encouragement or hope. For example, the combination unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the combination method of the story and illustrations. Alternatively, if the user is happy, the combination unit can combine stories and illustrations themed around fun adventures or friendship. For example, the combination unit can record the user's voice, estimate the emotion using voice analysis technology, and adjust the combination method of the story and illustrations. Alternatively, if the user is anxious, the combination unit can combine stories and illustrations that inspire a sense of security. For example, the combination unit can analyze the user's text input, estimate the emotion using text analysis technology, and adjust the combination method of the story and illustrations. This allows for the creation of a picture book that is more in tune with the user's emotions by providing a combination method of stories and illustrations that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] When combining, the combination unit can generate a more personalized picture book by referring to the user's past selection history. The combination unit can generate a new related picture book based on, for example, a theme selected by the user in the past. For example, the combination unit can store the user's past selection history in a database and analyze it using data mining technology. The combination unit can also generate a new related picture book based on characters selected by the user in the past. For example, the combination unit can analyze the user's past selection history using a machine learning algorithm to extract patterns. The combination unit can also generate a new related picture book based on elements of scenarios selected by the user in the past. For example, the combination unit can classify the user's past selection history using clustering technology and suggest similar elements. This makes it possible to generate a more personalized picture book by providing a picture book based on the user's past selection history.

[0078] When combining stories and illustrations, the combination unit can try different combinations of stories and illustrations to find a suitable combination. For example, the combination unit can randomly combine different stories and illustrations to find the optimal combination. For example, the combination unit can select different stories and illustrations from a database and use an algorithm to randomly combine them. The combination unit can also combine different stories and illustrations based on the user's preferences. For example, the combination unit can analyze the user's past selection history and suggest combinations based on the preferences. The combination unit can also combine different stories and illustrations based on the user's past selection history. For example, the combination unit can classify the user's past selection history using clustering technology and combine similar elements. This allows a more optimal picture book to be generated by trying out different combinations of stories and illustrations.

[0079] The combination unit can estimate the user's emotions and adjust the page layout of the picture book based on the estimated user emotions. For example, if the user is nervous, the combination unit provides a simple, highly visible layout. For example, the combination unit can capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and adjust the page layout. Furthermore, if the user is relaxed, the combination unit can provide a layout with detailed information. For example, the combination unit can record the user's voice, estimate the emotions using voice analysis technology, and adjust the page layout. Furthermore, if the user is in a hurry, the combination unit can provide a layout that focuses on the main points. For example, the combination unit can analyze the user's text input, estimate the emotions using text analysis technology, and adjust the page layout. This allows for the creation of a more appropriate picture book by providing a page layout that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When combining, the combination unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the combination unit generates a picture book incorporating Japanese scenery and culture. For example, the combination unit can acquire the user's geographical location information as GPS data and incorporate region-specific elements. Also, if the user is in the United States, the combination unit can generate a picture book incorporating American scenery and culture. For example, the combination unit can suggest elements related to regional culture and events based on the user's geographical location information. Also, if the user is in Europe, the combination unit can generate a picture book incorporating European scenery and culture. For example, the combination unit can analyze the user's geographical location information and suggest region-specific elements. This makes it possible to generate a more personalized picture book by providing region-specific elements based on the user's geographical location information.

[0081] The combination unit can analyze the user's social media activities and incorporate related elements when combining. The combination unit incorporates related elements, for example, based on content that the user frequently shares on social media. For example, the combination unit can link the user's social media accounts and analyze the content of posts. The combination unit can also incorporate related elements based on the content of accounts the user follows. For example, the combination unit can analyze the attributes of the user's followers and suggest elements based on their interests. The combination unit can also incorporate related elements based on the activity content of groups in which the user participates. For example, the combination unit can collect data on the user's group activities and generate related elements. This makes it possible to generate a more personalized picture book by providing elements based on the user's social media activities. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, image generation unit, and combination unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives elements of the theme, characters, and scenario of the picture book from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a story. The image generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to each scene of the generated story using an image generation AI. The combination unit is realized, for example, by the control unit 46A of the smart device 14 and automatically generates a picture book by combining the generated story and illustrations. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, image generation unit, and combination unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives elements of the theme, characters, and scenario of the picture book from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a story. The image generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to each scene of the generated story using an image generation AI. The combination unit is realized, for example, by the control unit 46A of the smart glasses 214 and automatically generates a picture book by combining the generated story and illustrations. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, image generation unit, and combination unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives elements of the theme, characters, and scenario of the picture book from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a story. The image generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to each scene of the generated story using an image generation AI. The combination unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and automatically generates a picture book by combining the generated story and illustrations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, image generation unit, and combination unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives elements of the theme, characters, and scenario of the picture book from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a story. The image generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to each scene of the generated story using an image generation AI. The combination unit is realized, for example, by the control unit 46A of the robot 414 and automatically generates a picture book by combining the generated story and illustrations.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can also suggest related educational elements based on the user's input. For example, if the user inputs "animal characters," the reception unit can suggest scenarios that include educational information about the ecology and habits of animals. If the user selects the theme of "space," the reception unit can suggest scenarios that include basic knowledge about space and information about astronomy. If the user selects the theme of "history," the reception unit can suggest scenarios that include educational information about historical events and people. This allows the automatic picture book generation system to provide picture books that incorporate educational elements as well as emotional and interpersonal development.

[0084] The reception unit can also estimate the user's emotions and suggest relaxing music and sound effects based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can suggest relaxing music and play it in the background while the picture book is being read aloud. If the user is having fun, the reception unit can suggest fun sound effects and play them in sync with a scene in the picture book. Furthermore, if the user is sad, the reception unit can suggest comforting sound effects and play them in sync with a moving scene in the picture book. In this way, by providing music and sound effects that correspond to the user's emotions, a picture book that is more in tune with the user's emotions can be generated.

[0085] The reception unit can also analyze the user's past input history and suggest themes, characters, and scenario elements that correspond to the user's growth and changes. For example, if the theme the user previously selected was "friendship," the reception unit can next suggest related themes such as "cooperation" or "trust." If the character the user previously selected was "animal," the reception unit can next suggest new characters such as "fantasy creatures" or "historical figures." Furthermore, if the scenario element the user previously selected was "adventure," the reception unit can next suggest new scenarios such as "mystery" or "learning." This makes it possible to generate a more personalized picture book by providing themes, characters, and scenario elements that correspond to the user's growth and changes.

[0086] The reception unit not only filters suitable themes, characters, and scenario elements based on the user's age and interests, but also makes suggestions according to the user's learning style. For example, if the user is a visual learner, the reception unit can suggest a scenario that includes many visually appealing illustrations and diagrams. If the user is an auditory learner, the reception unit can suggest a scenario that incorporates a lot of audio and music. Furthermore, if the user is an experiential learner, the reception unit can suggest a scenario that includes interactive elements. In this way, a more effective picture book can be generated by providing themes, characters, and scenario elements that match the user's learning style.

[0087] The reception unit can also estimate the user's emotions and adjust the tone and pace of the narration when reading a picture book based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can suggest a narration with a calm tone and a slow pace. If the user is excited, the reception unit can suggest a narration with a lively tone and a fast pace. Furthermore, if the user is sad, the reception unit can suggest a narration with a comforting tone and a slow pace. In this way, a picture book that is more in tune with the user's emotions can be generated by providing narration that corresponds to the user's emotions.

[0088] The reception unit can also suggest themes, characters, and scenario elements that match the seasons and climates of the region based on the user's geographical location information. For example, if the user is in winter in Japan, the reception unit can suggest picture book elements with a theme of snow and winter scenery. If the user is in summer in America, the reception unit can suggest picture book elements with a theme of beaches and summer activities. Furthermore, if the user is in autumn in Europe, the reception unit can suggest picture book elements with a theme of autumn leaves and autumn harvests. In this way, a more personalized picture book can be generated by providing themes, characters, and scenario elements that match the seasons and climates based on the user's geographical location information.

[0089] The reception unit can also analyze the user's social media activity and suggest themes, characters, and scenario elements based on social issues and trends that interest the user. For example, if the user is interested in environmental issues, the reception unit can suggest elements for a picture book with an environmental protection theme. If the user is interested in diversity and inclusion, the reception unit can suggest scenarios that incorporate diverse characters and cultures. Furthermore, if the user is interested in technology and future trends, the reception unit can suggest elements for a picture book with a future technology or invention theme. This makes it possible to generate a more personalized picture book by providing themes, characters, and scenario elements that match social issues and trends based on the user's social media activity.

[0090] The generator can also estimate the user's emotions and adjust the ending of the story based on the estimated user's emotions. For example, if the user is sad, the generator can generate a story with a happy ending based on themes of hope and encouragement. If the user is happy, the generator can generate an ending based on themes of fun adventure and success. Furthermore, if the user is anxious, the generator can generate an ending that gives a sense of security. In this way, a picture book that is more in tune with the user's emotions can be generated by providing an ending that corresponds to the user's emotions.

[0091] When generating a story, the generator can refer to the user's past selection history and incorporate surprise elements based on the user's preferences and interests. For example, a surprise element such as a reappearance of a character previously selected by the user can be incorporated. A new development related to a theme previously selected by the user can also be incorporated. Furthermore, an unexpected ending can be incorporated based on elements of a scenario previously selected by the user. In this way, a more personalized picture book can be generated by providing surprise elements based on the user's past selection history.

[0092] When generating a story, the generator not only generates a new story by combining stories of different genres and styles, but also branches the story's progression according to the user's selection. For example, if the user selects a story that combines fantasy and contemporary drama, the generator can present the user with choices during the story, allowing the story to proceed in different directions depending on the choices. If the user selects a story that combines adventure and mystery, the generator can present the user with puzzle-solving choices during the story, allowing the story to unfold differently depending on the choices. Furthermore, if the user selects a story that combines comedy and romance, the generator can present the user with emotional choices during the story, allowing the story to reach different endings depending on the choices. This not only provides new stories that combine different genres and styles, but also provides branching stories according to the user's selection, thereby generating a more interactive picture book.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit receives the theme, characters, and scenario elements of the picture book from the user. For example, the user can input themes such as "friendship" or "cooperation" or "animal characters." Step 2: The generator uses the AI ​​to analyze the information received by the reception unit and generate a story. For example, the AI ​​generates a story about animals working together to overcome challenges based on the input theme, characters, and scenario elements. Step 3: The generator uses image generation AI to generate illustrations corresponding to each scene in the generated story. For example, the image generation AI generates an illustration of a scene in which animals work together to build a bridge. Step 4: The combination unit combines the generated story and illustrations to automatically generate a picture book that can be read aloud. For example, the combination unit arranges the generated story and illustrations on each page to form a picture book.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a theme, characters, and scenario elements of a picture book from a user; a generation unit that analyzes the information received by the reception unit and generates a story; a generation unit that generates illustrations for a picture book based on the story generated by the generation unit; a combination unit that combines the story and illustrations generated by the generation unit to automatically generate a picture book that can be read aloud. A system characterized by:

2. The reception unit Estimates the user's emotions and suggests themes, characters, and scenario elements for picture books based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyzes the user's input history and automatically completes appropriate themes, characters, and scenario elements. The system of claim 1 .

4. The reception unit Filtering suitable themes, characters, and story elements based on the user's age and interests The system of claim 1 .

5. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions. The system of claim 1 .

6. The reception unit Suggesting localized themes, characters, and story elements based on the user's geographic location The system of claim 1 .

7. The reception unit Analyzes users' social media activity and suggests relevant themes, characters, and story elements The system of claim 1 .

8. The generation unit Inferring user emotions and adjusting the tone and mood of the story based on the inferred user emotions The system of claim 1 .

Images