system

Abstract

The system according to the embodiment aims to allow children to express their own imaginative stories and characters in the form of picture books. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a setting unit, a generation unit, a display unit, and a provision unit. The input unit allows children to input stories and characters that they have imagined. The analysis unit analyzes the information input by the input unit. The setting unit sets the storyline and characters based on the results of the analysis by the analysis unit. The generation unit generates a story and illustrations for the picture book based on the content set by the setting unit. The display unit allows children to read the picture book generated by the generation unit. The provision unit allows adults to understand the content.

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for children to express the stories and characters they imagine in picture books, and there is room for improvement.

[0005] The system according to the embodiment aims to allow children to express their own imaginative stories and characters in the form of picture books. [Means for solving the problem]

[0006] The system according to the embodiment comprises an input unit, an analysis unit, a setting unit, a generation unit, a display unit, and a provision unit. The input unit allows children to input stories and characters that they have imagined. The analysis unit analyzes the information input by the input unit. The setting unit sets the storyline and characters based on the results of the analysis by the analysis unit. The generation unit generates a story and illustrations for the picture book based on the content set by the setting unit. The display unit allows children to read the picture book generated by the generation unit. The provision unit allows adults to understand the content. [Effects of the Invention]

[0007] The system according to the embodiment allows children to express their own imaginative stories and characters as picture books. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A picture book generation system according to an embodiment of the present invention allows children to express the worlds they imagine in picture books. In this picture book generation system, children input stories and characters of their own imagination, and a generation AI analyzes the input to generate a story and illustrations for the picture book. The generated picture book is not only readable by children, but also accessible to adults. This allows for understanding children's creativity and emotions and providing appropriate support. For example, if a child inputs a story about animals adventuring in the forest, the generation AI generates a picture book based on that story. The generated picture book depicts animals adventuring in the forest, allowing children to enjoy the worlds they imagine. Furthermore, adults can understand children's interests and emotions through the picture book and provide appropriate support. This system allows children to freely express their imaginations and enjoy reading. It also allows adults to obtain valuable information for supporting children's development. This picture book generation system fosters children's creativity and makes reading enjoyable. It also allows adults to obtain valuable information for supporting children's development.

[0029] A picture book creation system according to an embodiment includes an input unit, an analysis unit, a setting unit, a generation unit, a display unit, and a provision unit. The input unit allows children to input stories and characters they imagine. Content input by children includes, but is not limited to, fantasy, adventure, and animal characters. The input unit can specifically input children's imaginations using methods such as text input, voice input, and image input. The analysis unit analyzes the information input by the input unit. The analysis unit, for example, analyzes text using natural language processing technology and images using image analysis technology. The analysis unit generates data for setting the story development and characters based on the input information. The setting unit sets a specific storyline and characters based on the results of the analysis by the analysis unit. The setting unit sets, for example, the character's personality and the story development. The generation unit generates a story and illustrations for the picture book based on the content set by the setting unit. The generation unit generates the story text and illustrations for the story using a generation AI. The generation AI generates a story and illustrations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the picture book generated by the generation unit for children to read. The display unit displays the generated picture book using, for example, a digital display or a printed display. The provision unit provides the content of the picture book generated by the generation unit so that adults can understand it. The provision unit provides the content of the generated picture book using, for example, a method such as sending it by email or sharing it in cloud storage. In this way, the picture book generation system according to the embodiment enables children to express the worlds they imagine in picture books, making reading more enjoyable. In addition, adults can obtain valuable information to support children's growth.

[0030] The input unit can analyze the child's past input history and suggest an appropriate input method. For example, the input unit can automatically display characters or story elements that the child has frequently used in the past as candidates. The input unit can also, for example, prioritize suggesting input methods (voice, text, etc.) that the child has used in the past. The input unit can also, for example, predict and suggest characters or story elements to be used at a specific time period based on the child's past input history. This makes input work more efficient by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0031] The input unit can filter the input content based on the child's age and interests at the time of input. For example, the input unit can suggest stories and characters with an appropriate level of difficulty depending on the child's age. The input unit can also preferentially display related themes and characters based on the child's interests, for example. The input unit can also automatically filter inappropriate content based on the child's age and interests, for example. This makes the input process smoother by providing appropriate content according to the child's age and interests. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the child's age and interest data to the generation AI and have the generation AI filter the input content.

[0032] During input, the input unit can prioritize acquiring highly relevant input content based on the child's geographical location information. The input unit, for example, suggests characters and stories related to the area where the child lives. The input unit can also prioritize displaying related themes and settings based on the child's current location, for example. The input unit can also suggest stories that reflect the local culture and scenery based on the child's geographical location information. This improves the efficiency of input work by providing highly relevant content based on the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the child's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant input content.

[0033] The input unit can analyze the child's social media activity at the time of input and acquire related input content. The input unit can, for example, suggest characters or themes that the child has shown interest in on social media. The input unit can also, for example, preferentially display related story elements from the child's social media activity. The input unit can also, for example, suggest interesting input content based on the child's social media activity. This makes input work more efficient by providing highly relevant content based on social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the child's social media activity data to the generation AI and cause the generation AI to acquire related input content.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. The analysis unit, for example, performs a detailed analysis of important characters or story elements. The analysis unit can also perform a simplified analysis of less important elements. The analysis unit can also determine the priority of the analysis based on the importance of the input content. This makes the analysis work more efficient by performing the analysis based on the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specific analysis algorithm to a fantasy story. The analysis unit can also apply a different analysis algorithm to a realistic story. The analysis unit can also apply a dedicated analysis algorithm to an educational story. This improves the accuracy of analysis by applying an analysis algorithm depending on the category of the input content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the input content to the generation AI and cause the generation AI to apply different analysis algorithms.

[0036] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input content. For example, the analysis unit prioritizes analysis of the most recently input content. The analysis unit can also postpone content that was submitted earlier, for example. The analysis unit can also adjust the analysis schedule based on the submission time, for example. This allows for setting priorities based on the submission time, thereby making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit prioritizes analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content, for example. The analysis unit can also determine the order of analysis based on the relevance of the input content. This allows for setting the analysis order based on the relevance, thereby making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input content to the generation AI and have the generation AI adjust the order of analysis.

[0038] During setting, the setting unit can adjust the level of detail of the setting based on the importance of the analysis results. The setting unit, for example, performs detailed settings for important characters or story elements. The setting unit can also perform simplified settings for less important elements. The setting unit can also determine the priority of settings, for example, according to the importance of the analysis results. This makes the setting work more efficient by performing settings according to the importance of the analysis results. Some or all of the above-described processing in the setting unit may be performed using, or without, AI. For example, the setting unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the setting.

[0039] During setting, the setting unit can apply different setting algorithms depending on the story category. For example, the setting unit applies a specific setting algorithm to a fantasy story. The setting unit can also apply a different setting algorithm to a realistic story. The setting unit can also apply a dedicated setting algorithm to an educational story. This improves setting accuracy by applying a setting algorithm depending on the story category. Some or all of the above-described processing in the setting unit may be performed using AI, for example, or may be performed without using AI. For example, the setting unit can input story category data to the generation AI and cause the generation AI to apply different setting algorithms.

[0040] During the setting process, the setting unit can determine the priority of the settings based on the submission date of the stories. For example, the setting unit can prioritize the most recently submitted stories. The setting unit can also postpone, for example, stories that were submitted earlier. The setting unit can also adjust the setting schedule based on, for example, the submission date. This allows for setting priorities based on the submission date, thereby making the setting process more efficient. Some or all of the above-described processing in the setting unit may be performed using, or without, AI. For example, the setting unit can input submission date data into a generation AI and have the generation AI determine the priority of the settings.

[0041] The setting unit can adjust the order of settings based on the relevance of stories during setting. The setting unit, for example, prioritizes setting highly relevant stories. The setting unit can also postpone less relevant stories, for example. The setting unit can also determine the order of settings based on the relevance of stories, for example. This allows the setting order to be set based on the relevance, thereby making the setting work more efficient. Some or all of the above-described processing in the setting unit may be performed using AI, for example, or may be performed without using AI. For example, the setting unit can input story relevance data into a generation AI and cause the generation AI to adjust the order of settings.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the story during generation. The generation unit, for example, generates detailed illustrations for important characters or story elements. The generation unit can also generate simplified illustrations for less important elements. The generation unit can also determine the priority of generation based on the importance of the story. This makes the generation work more efficient by generating illustrations based on the importance of the story. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input story importance data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0043] The generation unit can apply different generation algorithms depending on the story category during generation. For example, the generation unit can apply a specific generation algorithm to a fantasy story. The generation unit can also apply a different generation algorithm to a realistic story. The generation unit can also apply a dedicated generation algorithm to an educational story. This improves generation accuracy by applying a generation algorithm depending on the story category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input story category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] The generation unit can determine the generation priority based on the submission date of the story at the time of generation. For example, the generation unit prioritizes the generation of recently submitted stories. The generation unit can also postpone, for example, stories that were submitted earlier. The generation unit can also adjust the generation schedule based on, for example, the submission date. This allows for setting priorities based on the submission date, thereby making the generation work more efficient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the order of generation based on the relevance of the stories during generation. For example, the generation unit prioritizes the generation of highly relevant stories. The generation unit can also postpone, for example, stories with low relevance. The generation unit can also determine the order of generation based on the relevance of the stories. This allows the generation order to be set based on the relevance, thereby making the generation work more efficient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input story relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0046] When displaying, the display unit can select an appropriate display method by referring to the child's past browsing history. For example, the display unit can prioritize displaying characters or story elements that the child has frequently viewed in the past. The display unit can also prioritize suggesting display methods (audio, text, etc.) that the child has used in the past. The display unit can also predict and display characters or story elements to be used during a specific time period, for example, based on the child's past browsing history. This makes the display process more efficient by providing an optimal display method based on the past browsing history. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input past browsing history data into a generation AI and have the generation AI select an appropriate display method.

[0047] The display unit can filter the display content based on the child's age and interests when displaying the content. The display unit, for example, displays stories and characters with an appropriate level of difficulty depending on the child's age. The display unit can also prioritize displaying related themes and characters based on the child's interests, for example. The display unit can also automatically filter inappropriate content based on the child's age and interests, for example. This allows for smooth display operations by providing appropriate content according to the child's age and interests. Some or all of the above-described processing in the display unit may be performed using, or without, AI, for example. For example, the display unit can input the child's age and interest data into the generation AI and have the generation AI filter the display content.

[0048] When displaying, the display unit can prioritize displaying highly relevant display content taking into account the child's geographical location information. The display unit, for example, displays characters and stories related to the area where the child lives. The display unit can also prioritize displaying related themes and settings based on the child's current location, for example. The display unit can also display stories that reflect the local culture and scenery based on the child's geographical location information. This improves the efficiency of display work by providing highly relevant content based on the geographical location information. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the child's geographical location information data to a generation AI and cause the generation AI to obtain highly relevant display content.

[0049] The display unit can analyze the child's social media activity and provide relevant display content when displaying the content. The display unit can, for example, display characters or themes that the child has shown interest in on social media. The display unit can also, for example, prioritize displaying relevant story elements from the child's social media activity. The display unit can also, for example, provide interesting display content based on the child's social media activity. This improves the efficiency of display work by providing highly relevant content based on social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the child's social media activity data into a generation AI and cause the generation AI to obtain related display content.

[0050] The providing unit can adjust the level of detail of the provided information based on the importance of the content of the picture book when providing the information. The providing unit, for example, provides detailed information for important characters or story elements. The providing unit can also provide simplified information for elements with low importance, for example. The providing unit can also determine the priority of the information provided based on the importance of the content of the picture book. This makes the providing work more efficient by providing information based on the importance of the content of the picture book. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the content of the picture book to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0051] The providing unit can apply different providing algorithms depending on the category of the picture book when providing the picture book. For example, the providing unit applies a specific providing algorithm to a fantasy picture book. The providing unit can also apply a different providing algorithm to a realistic picture book. The providing unit can also apply a dedicated providing algorithm to an educational picture book. This improves the accuracy of providing by applying a providing algorithm depending on the category of the picture book. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input picture book category data to the generation AI and cause the generation AI to apply different providing algorithms.

[0052] The providing unit can determine the priority of provision based on the submission date of the picture book at the time of provision. For example, the providing unit can provide picture books that have been submitted recently with priority. The providing unit can also postpone picture books that have been submitted recently, for example. The providing unit can also adjust the provision schedule based on the submission date, for example. This makes the provision work more efficient by setting the priority based on the submission date. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input submission date data into the generation AI and have the generation AI determine the provision priority.

[0053] The providing unit can adjust the order of providing the picture books based on the relevance of the picture books when providing them. For example, the providing unit can provide highly relevant picture books preferentially. The providing unit can also postpone less relevant picture books, for example. The providing unit can also determine the order of providing the picture books based on the relevance of the picture books. This allows the providing order to be set based on the relevance, thereby making the providing work more efficient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the picture books into the generating AI and cause the generating AI to adjust the order of providing the books.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analyzer can automatically add relevant educational elements based on a child's input. For example, if a child inputs an adventure story, the analyzer can add geographical knowledge and historical background related to the story. If a child inputs an animal character, the analyzer can add information about the animal's ecology and behavior. Furthermore, if a child inputs a fantasy story, the analyzer can add scientific concepts and technologies related to the worldview. This allows children to learn while having fun, increasing the educational value.

[0056] The setting unit can analyze the child's past input history and make suggestions to maintain the consistency of the story. For example, it can make a suggestion to reintroduce a character created by the child in the past. It can also suggest a new story direction based on past story developments. Furthermore, it can make suggestions based on the past input history to continue using a specific theme or setting. This makes it possible to maintain the consistency of the story while respecting the child's creativity.

[0057] The generator can adjust the difficulty and content of the picture book to be generated based on the child's age and interests. For example, a picture book with many simple and colorful illustrations and short sentences can be generated for young children. A picture book with detailed illustrations and a complex story can also be generated for elementary school students. Furthermore, for children with specific interests, a picture book with specialized content related to those interests can be generated. This makes it possible to provide the most suitable picture book according to the child's age and interests.

[0058] The analysis unit can automatically add relevant music and sound effects based on the child's input. For example, it can add tension-building music to adventure stories, and nature sounds to scenes featuring animal characters. It can also add magical sound effects to fantasy stories. It can also add music and sound effects to express specific emotions. This makes the content of picture books more immersive and attracts children's attention.

[0059] The input unit can analyze the child's social media activity and acquire related input content. For example, it can suggest characters or themes that the child has shown interest in on social media. It can also prioritize the display of related story elements from the child's social media activity. It can also suggest interesting input content based on the child's social media activity. This makes input work more efficient by providing highly relevant content based on social media activity.

[0060] The generator can add interactive elements to the picture book it generates. For example, it can add animations that move characters every time a child turns a page. It can also add interactive elements that allow the story to branch depending on the options a child makes in a particular scene. It can also add a mechanism that plays audio and sound effects when a child touches something. This makes the picture book more interactive and fun, and can attract children's interest.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The input unit allows children to input stories and characters that they have imagined. The content that children input may include, but is not limited to, fantasy, adventure, animal characters, etc. The input unit can specifically input children's imaginations using methods such as text input, voice input, and image input. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit analyzes text using natural language processing technology and images using image analysis technology. Based on the input information, the analysis unit generates data for developing the story and setting up characters. Step 3: The setting unit creates a specific storyline and character settings based on the results of the analysis by the analysis unit. For example, the setting unit sets the character's personality and the development of the story. Step 4: The generation unit generates the story and illustrations for the picture book based on the content set by the setting unit. The generation unit uses a generation AI to generate the story text and illustrations. The generation AI generates the story and illustrations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The display unit displays the picture book generated by the generation unit for children to read. The display unit displays the generated picture book using a method such as digital display or print display. Step 6: The providing unit provides the content of the picture book generated by the generating unit so that an adult can understand it. The providing unit provides the content of the generated picture book by using a method such as sending it by email or sharing it in cloud storage.

[0063] (Example 2) A picture book generation system according to an embodiment of the present invention allows children to express the worlds they imagine in picture books. In this picture book generation system, children input stories and characters of their own imagination, and a generation AI analyzes the input to generate a story and illustrations for the picture book. The generated picture book is not only readable by children, but also accessible to adults. This allows for understanding children's creativity and emotions and providing appropriate support. For example, if a child inputs a story about animals adventuring in the forest, the generation AI generates a picture book based on that story. The generated picture book depicts animals adventuring in the forest, allowing children to enjoy the worlds they imagine. Furthermore, adults can understand children's interests and emotions through the picture book and provide appropriate support. This system allows children to freely express their imaginations and enjoy reading. It also allows adults to obtain valuable information for supporting children's development. This picture book generation system fosters children's creativity and makes reading enjoyable. It also allows adults to obtain valuable information for supporting children's development.

[0064] A picture book creation system according to an embodiment includes an input unit, an analysis unit, a setting unit, a generation unit, a display unit, and a provision unit. The input unit allows children to input stories and characters they imagine. Content input by children includes, but is not limited to, fantasy, adventure, and animal characters. The input unit can specifically input children's imaginations using methods such as text input, voice input, and image input. The analysis unit analyzes the information input by the input unit. The analysis unit, for example, analyzes text using natural language processing technology and images using image analysis technology. The analysis unit generates data for setting the story development and characters based on the input information. The setting unit sets a specific storyline and characters based on the results of the analysis by the analysis unit. The setting unit sets, for example, the character's personality and the story development. The generation unit generates a story and illustrations for the picture book based on the content set by the setting unit. The generation unit generates the story text and illustrations for the story using a generation AI. The generation AI generates a story and illustrations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the picture book generated by the generation unit for children to read. The display unit displays the generated picture book using, for example, a digital display or a printed display. The provision unit provides the content of the picture book generated by the generation unit so that adults can understand it. The provision unit provides the content of the generated picture book using, for example, a method such as sending it by email or sharing it in cloud storage. In this way, the picture book generation system according to the embodiment enables children to express the worlds they imagine in picture books, making reading more enjoyable. In addition, adults can obtain valuable information to support children's growth.

[0065] The input unit can estimate the child's emotions and adjust the design of the input interface based on the estimated child's emotions. For example, if the child is excited, the input unit can provide a brightly colored and dynamic interface, making the input task more enjoyable. Furthermore, for example, if the child is calm, the input unit can provide a simple, highly visible interface, making the input task smoother. Furthermore, for example, if the child is tired, the input unit can provide a calming interface with a simple input method. This makes the input task more enjoyable by providing an interface that corresponds to the child's emotions. The emotion estimation is realized 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. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The input unit can analyze the child's past input history and suggest an appropriate input method. For example, the input unit can automatically display characters or story elements that the child has frequently used in the past as candidates. The input unit can also, for example, prioritize suggesting input methods (voice, text, etc.) that the child has used in the past. The input unit can also, for example, predict and suggest characters or story elements to be used at a specific time period based on the child's past input history. This makes input work more efficient by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0067] The input unit can filter the input content based on the child's age and interests at the time of input. For example, the input unit can suggest stories and characters with an appropriate level of difficulty depending on the child's age. The input unit can also preferentially display related themes and characters based on the child's interests, for example. The input unit can also automatically filter inappropriate content based on the child's age and interests, for example. This makes the input process smoother by providing appropriate content according to the child's age and interests. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the child's age and interest data to the generation AI and have the generation AI filter the input content.

[0068] The input unit can estimate the child's emotions and prioritize input content based on the estimated child's emotions. For example, if the child is excited, the input unit can prioritize input of fun elements. Furthermore, for example, if the child is calm, the input unit can prioritize input of detailed story settings. Furthermore, for example, if the child is tired, the input unit can prioritize input of simple content. This allows for setting priorities according to the child's emotions, thereby streamlining input work. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the child's emotion data to the generation AI and cause the generation AI to determine the priority of the input content.

[0069] During input, the input unit can prioritize acquiring highly relevant input content based on the child's geographical location information. The input unit, for example, suggests characters and stories related to the area where the child lives. The input unit can also prioritize displaying related themes and settings based on the child's current location, for example. The input unit can also suggest stories that reflect the local culture and scenery based on the child's geographical location information. This improves the efficiency of input work by providing highly relevant content based on the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the child's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant input content.

[0070] The input unit can analyze the child's social media activity at the time of input and acquire related input content. The input unit can, for example, suggest characters or themes that the child has shown interest in on social media. The input unit can also, for example, preferentially display related story elements from the child's social media activity. The input unit can also, for example, suggest interesting input content based on the child's social media activity. This makes input work more efficient by providing highly relevant content based on social media activity. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the child's social media activity data to the generation AI and cause the generation AI to acquire related input content.

[0071] The analysis unit can estimate the child's emotions and adjust the analysis algorithm based on the estimated child's emotions. For example, if the child is excited, the analysis unit analyzes the story development that reflects the emotions. For example, if the child is calm, the analysis unit can perform a detailed story analysis. For example, if the child is tired, the analysis unit can perform a simple story analysis. This improves the accuracy of the analysis by applying an analysis algorithm according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. The analysis unit, for example, performs a detailed analysis of important characters or story elements. The analysis unit can also perform a simplified analysis of less important elements. The analysis unit can also determine the priority of the analysis based on the importance of the input content. This makes the analysis work more efficient by performing the analysis based on the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specific analysis algorithm to a fantasy story. The analysis unit can also apply a different analysis algorithm to a realistic story. The analysis unit can also apply a dedicated analysis algorithm to an educational story. This improves the accuracy of analysis by applying an analysis algorithm depending on the category of the input content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the input content to the generation AI and cause the generation AI to apply different analysis algorithms.

[0074] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. For example, if the child is excited, the analysis unit can provide a visually stimulating display method. For example, if the child is calm, the analysis unit can provide a simple, highly visible display method. For example, if the child is tired, the analysis unit can provide a simple, easy-to-understand display method. This provides a display method that corresponds to the child's emotions, thereby deepening understanding of the analysis results. 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. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input content. For example, the analysis unit prioritizes analysis of the most recently input content. The analysis unit can also postpone content that was submitted earlier, for example. The analysis unit can also adjust the analysis schedule based on the submission time, for example. This allows for setting priorities based on the submission time, thereby making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit prioritizes analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content, for example. The analysis unit can also determine the order of analysis based on the relevance of the input content. This allows for setting the analysis order based on the relevance, thereby making the analysis work more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input content to the generation AI and have the generation AI adjust the order of analysis.

[0077] The setting unit can estimate the child's emotions and adjust the storyline and character settings based on the estimated child's emotions. For example, if the child is excited, the setting unit can set a storyline that includes many action elements. For example, if the child is calm, the setting unit can set a calm storyline. For example, if the child is tired, the setting unit can set a simple and easy-to-understand character setting. This makes the content of the picture book more appealing by setting a storyline and characters according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the setting unit can be performed using an AI, for example, or without an AI. For example, the setting unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the storyline and character settings.

[0078] During setting, the setting unit can adjust the level of detail of the setting based on the importance of the analysis results. The setting unit, for example, performs detailed settings for important characters or story elements. The setting unit can also perform simplified settings for less important elements. The setting unit can also determine the priority of settings, for example, according to the importance of the analysis results. This makes the setting work more efficient by performing settings according to the importance of the analysis results. Some or all of the above-described processing in the setting unit may be performed using, or without, AI. For example, the setting unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the setting.

[0079] During setting, the setting unit can apply different setting algorithms depending on the story category. For example, the setting unit applies a specific setting algorithm to a fantasy story. The setting unit can also apply a different setting algorithm to a realistic story. The setting unit can also apply a dedicated setting algorithm to an educational story. This improves setting accuracy by applying a setting algorithm depending on the story category. Some or all of the above-described processing in the setting unit may be performed using AI, for example, or may be performed without using AI. For example, the setting unit can input story category data to the generation AI and cause the generation AI to apply different setting algorithms.

[0080] The setting unit can estimate the child's emotions and determine the priority of the setting contents based on the estimated child's emotions. For example, if the child is excited, the setting unit can prioritize fun elements. For example, if the child is calm, the setting unit can prioritize detailed story settings. For example, if the child is tired, the setting unit can prioritize simple settings. This makes the setting work more efficient by setting priorities according to the child'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the setting unit can be performed using AI, for example, or without AI. For example, the setting unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of the setting contents.

[0081] During the setting process, the setting unit can determine the priority of the settings based on the submission date of the stories. For example, the setting unit can prioritize the most recently submitted stories. The setting unit can also postpone, for example, stories that were submitted earlier. The setting unit can also adjust the setting schedule based on, for example, the submission date. This allows for setting priorities based on the submission date, thereby making the setting process more efficient. Some or all of the above-described processing in the setting unit may be performed using, or without, AI. For example, the setting unit can input submission date data into a generation AI and have the generation AI determine the priority of the settings.

[0082] The setting unit can adjust the order of settings based on the relevance of stories during setting. The setting unit, for example, prioritizes setting highly relevant stories. The setting unit can also postpone less relevant stories, for example. The setting unit can also determine the order of settings based on the relevance of stories, for example. This allows the setting order to be set based on the relevance, thereby making the setting work more efficient. Some or all of the above-described processing in the setting unit may be performed using AI, for example, or may be performed without using AI. For example, the setting unit can input story relevance data into a generation AI and cause the generation AI to adjust the order of settings.

[0083] The generation unit can estimate the child's emotions and adjust the expression method of the generated picture book based on the estimated child's emotions. For example, if the child is excited, the generation unit can generate visually stimulating illustrations. For example, if the child is calm, the generation unit can generate illustrations with calm colors. For example, if the child is tired, the generation unit can generate simple, highly visible illustrations. This makes the content of the picture book more appealing by providing an expression method that corresponds to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the expression method of the picture book.

[0084] The generation unit can adjust the level of detail of the generation based on the importance of the story during generation. The generation unit, for example, generates detailed illustrations for important characters or story elements. The generation unit can also generate simplified illustrations for less important elements. The generation unit can also determine the priority of generation based on the importance of the story. This makes the generation work more efficient by generating illustrations based on the importance of the story. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input story importance data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0085] The generation unit can apply different generation algorithms depending on the story category during generation. For example, the generation unit can apply a specific generation algorithm to a fantasy story. The generation unit can also apply a different generation algorithm to a realistic story. The generation unit can also apply a dedicated generation algorithm to an educational story. This improves generation accuracy by applying a generation algorithm depending on the story category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input story category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0086] The generation unit can estimate the child's emotions and adjust the length of the picture book to be generated based on the estimated child's emotions. For example, if the child is excited, the generation unit can generate a short, concise picture book. For example, if the child is relaxed, the generation unit can generate a longer picture book with detailed explanations. For example, if the child is tired, the generation unit can generate a simple, short picture book. This improves readability by providing a picture book with a length that matches the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the length of the picture book.

[0087] The generation unit can determine the generation priority based on the submission date of the story at the time of generation. For example, the generation unit prioritizes the generation of recently submitted stories. The generation unit can also postpone, for example, stories that were submitted earlier. The generation unit can also adjust the generation schedule based on, for example, the submission date. This allows for setting priorities based on the submission date, thereby making the generation work more efficient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0088] The generation unit can adjust the order of generation based on the relevance of the stories during generation. For example, the generation unit prioritizes the generation of highly relevant stories. The generation unit can also postpone, for example, stories with low relevance. The generation unit can also determine the order of generation based on the relevance of the stories. This allows the generation order to be set based on the relevance, thereby making the generation work more efficient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input story relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0089] The display unit can estimate the child's emotions and adjust the design of the display interface based on the estimated child's emotions. For example, if the child is excited, the display unit can provide a brightly colored and dynamic interface, making the display task more enjoyable. Furthermore, for example, if the child is calm, the display unit can provide a simple, highly visible interface, making the display task smoother. Furthermore, for example, if the child is tired, the display unit can provide a calmly colored and easy-to-operate interface. This makes the display task more enjoyable by providing an interface that corresponds to the child's emotions. The emotion estimation is realized 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. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the child's emotion data into the generation AI and have the generation AI adjust the design of the display interface.

[0090] When displaying, the display unit can select an appropriate display method by referring to the child's past browsing history. For example, the display unit can prioritize displaying characters or story elements that the child has frequently viewed in the past. The display unit can also prioritize suggesting display methods (audio, text, etc.) that the child has used in the past. The display unit can also predict and display characters or story elements to be used during a specific time period, for example, based on the child's past browsing history. This makes the display process more efficient by providing an optimal display method based on the past browsing history. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input past browsing history data into a generation AI and have the generation AI select an appropriate display method.

[0091] The display unit can filter the display content based on the child's age and interests when displaying the content. The display unit, for example, displays stories and characters with an appropriate level of difficulty depending on the child's age. The display unit can also prioritize displaying related themes and characters based on the child's interests, for example. The display unit can also automatically filter inappropriate content based on the child's age and interests, for example. This allows for smooth display operations by providing appropriate content according to the child's age and interests. Some or all of the above-described processing in the display unit may be performed using, or without, AI, for example. For example, the display unit can input the child's age and interest data into the generation AI and have the generation AI filter the display content.

[0092] The display unit can estimate the child's emotions and determine the priority of display content based on the estimated child's emotions. For example, if the child is excited, the display unit can prioritize displaying fun elements. For example, if the child is calm, the display unit can prioritize displaying detailed story settings. For example, if the child is tired, the display unit can prioritize displaying simple display content. This allows for setting priorities according to the child's emotions, thereby improving the efficiency of display work. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of display content.

[0093] When displaying, the display unit can prioritize displaying highly relevant display content taking into account the child's geographical location information. The display unit, for example, displays characters and stories related to the area where the child lives. The display unit can also prioritize displaying related themes and settings based on the child's current location, for example. The display unit can also display stories that reflect the local culture and scenery based on the child's geographical location information. This improves the efficiency of display work by providing highly relevant content based on the geographical location information. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the child's geographical location information data to a generation AI and cause the generation AI to obtain highly relevant display content.

[0094] The display unit can analyze the child's social media activity and provide relevant display content when displaying the content. The display unit can, for example, display characters or themes that the child has shown interest in on social media. The display unit can also, for example, prioritize displaying relevant story elements from the child's social media activity. The display unit can also, for example, provide interesting display content based on the child's social media activity. This improves the efficiency of display work by providing highly relevant content based on social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the child's social media activity data into a generation AI and cause the generation AI to obtain related display content.

[0095] The providing unit can estimate the child's emotions and adjust the presentation method of the information to be provided based on the estimated child's emotions. For example, if the child is excited, the providing unit can provide visually stimulating information. For example, if the child is calm, the providing unit can provide information in calm colors. For example, if the child is tired, the providing unit can provide simple, highly visible information. This allows the child to understand the information better by providing a presentation method that suits the child's emotions. The emotion estimation is realized 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the information.

[0096] The providing unit can adjust the level of detail of the provided information based on the importance of the content of the picture book when providing the information. The providing unit, for example, provides detailed information for important characters or story elements. The providing unit can also provide simplified information for elements with low importance, for example. The providing unit can also determine the priority of the information provided based on the importance of the content of the picture book. This makes the providing work more efficient by providing information based on the importance of the content of the picture book. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the content of the picture book to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0097] The providing unit can apply different providing algorithms depending on the category of the picture book when providing the picture book. For example, the providing unit applies a specific providing algorithm to a fantasy picture book. The providing unit can also apply a different providing algorithm to a realistic picture book. The providing unit can also apply a dedicated providing algorithm to an educational picture book. This improves the accuracy of providing by applying a providing algorithm depending on the category of the picture book. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input picture book category data to the generation AI and cause the generation AI to apply different providing algorithms.

[0098] The providing unit can estimate the child's emotions and determine the priority of information to be provided based on the estimated child's emotions. For example, if the child is excited, the providing unit can prioritize providing fun elements. For example, if the child is calm, the providing unit can prioritize providing detailed information. For example, if the child is tired, the providing unit can prioritize providing simple information. This allows for setting priorities according to the child's emotions, thereby streamlining the information providing process. 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. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of information.

[0099] The providing unit can determine the priority of provision based on the submission date of the picture book at the time of provision. For example, the providing unit can provide picture books that have been submitted recently with priority. The providing unit can also postpone picture books that have been submitted recently, for example. The providing unit can also adjust the provision schedule based on the submission date, for example. This makes the provision work more efficient by setting the priority based on the submission date. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input submission date data into the generation AI and have the generation AI determine the provision priority.

[0100] The providing unit can adjust the order of providing the picture books based on the relevance of the picture books when providing them. For example, the providing unit can provide highly relevant picture books preferentially. The providing unit can also postpone less relevant picture books, for example. The providing unit can also determine the order of providing the picture books based on the relevance of the picture books. This allows the providing order to be set based on the relevance, thereby making the providing work more efficient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the picture books into the generating AI and cause the generating AI to adjust the order of providing the books. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, setting unit, generation unit, display unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, through which children input stories and characters using text or voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the storyline and characters based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the story and illustrations for the picture book using a generation AI. The display unit is realized, for example, by the output device 40 of the smart device 14, and displays the generated picture book. The provision unit provides the contents of the picture book via, for example, email or cloud storage via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, setting unit, generation unit, display unit, and provision 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 input unit is realized by the microphone 238 of the smart glasses 214, through which children input stories and characters by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the storyline and characters based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the story and illustrations for the picture book using a generation AI. The display unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated picture book by voice. The provision unit provides the contents of the picture book, for example, by email or cloud storage via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, setting unit, generation unit, display unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, through which children input stories and characters by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the storyline and characters based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the story and illustrations for the picture book using a generation AI. The display unit is realized, for example, by the display 343 of the headset-type terminal 314, and displays the generated picture book. The provision unit provides the contents of the picture book, for example, by email or cloud storage via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, setting unit, generation unit, display unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, through which children input stories and characters by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the storyline and characters based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the story and illustrations for the picture book using a generation AI. The display unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated picture book by voice. The provision unit provides the contents of the picture book, for example, by email or cloud storage via the communication I / F 26 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analyzer can automatically add relevant educational elements based on a child's input. For example, if a child inputs an adventure story, the analyzer can add geographical knowledge and historical background related to the story. If a child inputs an animal character, the analyzer can add information about the animal's ecology and behavior. Furthermore, if a child inputs a fantasy story, the analyzer can add scientific concepts and technologies related to the worldview. This allows children to learn while having fun, increasing the educational value.

[0103] The input unit can estimate the child's emotions and provide real-time feedback on the input content based on the estimated emotions. For example, if the child is excited, the input unit can emphasize positive feedback to further stimulate the child's creativity. If the child is calm, the input unit can provide detailed feedback to help add depth to the story. Furthermore, if the child is tired, the input unit can provide simple words of encouragement to maintain motivation to continue the input task. Thus, providing feedback according to the child's emotions makes the input task more enjoyable and effective.

[0104] The setting unit can analyze the child's past input history and make suggestions to maintain the consistency of the story. For example, it can make a suggestion to reintroduce a character created by the child in the past. It can also suggest a new story direction based on past story developments. Furthermore, it can make suggestions based on the past input history to continue using a specific theme or setting. This makes it possible to maintain the consistency of the story while respecting the child's creativity.

[0105] The generator can adjust the difficulty and content of the picture book to be generated based on the child's age and interests. For example, a picture book with many simple and colorful illustrations and short sentences can be generated for young children. A picture book with detailed illustrations and a complex story can also be generated for elementary school students. Furthermore, for children with specific interests, a picture book with specialized content related to those interests can be generated. This makes it possible to provide the most suitable picture book according to the child's age and interests.

[0106] The providing unit can estimate the child's emotions and suggest a way of reading a picture book to the child based on the estimated emotions. For example, if the child is excited, the providing unit can suggest a reading method that increases the tone of the voice and speeds up the tempo. If the child is calm, the providing unit can also suggest a reading method that reads slowly in a calm voice. Furthermore, if the child is tired, the providing unit can also suggest a reading method that takes breaks after each short section. This makes it possible to provide the optimal reading method according to the child's emotions.

[0107] The analysis unit can automatically add relevant music and sound effects based on the child's input. For example, it can add tension-building music to adventure stories, and nature sounds to scenes featuring animal characters. It can also add magical sound effects to fantasy stories. It can also add music and sound effects to express specific emotions. This makes the content of picture books more immersive and attracts children's attention.

[0108] The input unit can analyze the child's social media activity and acquire related input content. For example, it can suggest characters or themes that the child has shown interest in on social media. It can also prioritize the display of related story elements from the child's social media activity. It can also suggest interesting input content based on the child's social media activity. This makes input work more efficient by providing highly relevant content based on social media activity.

[0109] The setting unit can estimate the child's emotions and adjust the story development in real time based on the estimated emotions. For example, if the child is excited, the number of action scenes can be increased and the story tempo can be sped up. If the child is calm, the number of detailed descriptions and dialogue scenes can be increased. Furthermore, if the child is tired, the story development can be adjusted to be simple and easy to understand. This makes it possible to provide an optimal story development according to the child's emotions.

[0110] The generator can add interactive elements to the picture book it generates. For example, it can add animations that move characters every time a child turns a page. It can also add interactive elements that allow the story to branch depending on the options a child makes in a particular scene. It can also add a mechanism that plays audio and sound effects when a child touches something. This makes the picture book more interactive and fun, and can attract children's interest.

[0111] The providing unit can estimate the child's emotions and adjust the way in which the content of the picture book is reported to the parent based on the estimated emotions. For example, if the child is excited, the providing unit can report to the parent that the child's creativity is increasing. If the child is calm, the providing unit can report to the parent that the child is concentrating. Furthermore, if the child is tired, the providing unit can report to the parent that the child is relaxed. This allows the parent to understand the child's emotional state and provide appropriate support.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The input unit allows children to input stories and characters that they have imagined. The content that children input may include, but is not limited to, fantasy, adventure, animal characters, etc. The input unit can specifically input children's imaginations using methods such as text input, voice input, and image input. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit analyzes text using natural language processing technology and images using image analysis technology. Based on the input information, the analysis unit generates data for developing the story and setting up characters. Step 3: The setting unit creates a specific storyline and character settings based on the results of the analysis by the analysis unit. For example, the setting unit sets the character's personality and the development of the story. Step 4: The generation unit generates the story and illustrations for the picture book based on the content set by the setting unit. The generation unit uses a generation AI to generate the story text and illustrations. The generation AI generates the story and illustrations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The display unit displays the picture book generated by the generation unit for children to read. The display unit displays the generated picture book using a method such as digital display or print display. Step 6: The providing unit provides the content of the picture book generated by the generating unit so that an adult can understand it. The providing unit provides the content of the generated picture book by using a method such as sending it by email or sharing it in cloud storage.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section where children can input their own imaginative stories and characters, an analysis unit that analyzes the information input by the input unit; a setting unit that sets a storyline and characters based on the results of the analysis by the analysis unit; a generation unit that generates a story and illustrations for a picture book based on the content set by the setting unit; a display unit for children to read the picture book created by the creation unit; and a presentation unit for adults to understand the content. A system characterized by:

2. The input unit Estimate the child's emotions and adjust the design of the input interface based on the estimated emotions.

2. The system of claim 1.

3. The input unit Analyzes the child's input history and suggests appropriate input methods 2. The system of claim 1.

4. The input unit As you type, filter your input based on your child's age and interests 2. The system of claim 1.

5. The input unit Inferring the child's emotions and prioritizing input content based on the estimated child's emotions 2. The system of claim 1.

6. The input unit As you type, prioritize relevant input based on your child's geographic location 2. The system of claim 1.

7. The input unit Analyze your child's social media activity and capture relevant input as they type 2. The system of claim 1.

8. The analysis unit Inferring the child's emotions and adjusting the analysis algorithm based on the estimated child's emotions 2. The system of claim 1.

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