system

Abstract

The system according to this embodiment aims to generate stories tailored to each child and to address their individual needs. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives information about the child. The analysis unit analyzes the information received by the reception unit. The generation unit generates a story based on the information analyzed by the analysis unit. The display unit displays the story generated by the generation unit as a digital picture book.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to generate a story tailored to each child and it is impossible to meet individual needs.

[0005] The system according to the embodiment aims to generate a story tailored to each child and meet individual needs.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit inputs information about a child. The analysis unit analyzes the information input by the reception unit. The generation unit generates a story based on the information analyzed by the analysis unit. The display unit displays the story generated by the generation unit as a digital picture book. [Effects of the Invention]

[0007] The system according to this embodiment can generate stories tailored to each child and respond to their individual needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The digital picture book service according to an embodiment of the present invention is a system in which AI automatically generates stories tailored to each child, fostering self-formation, empathy, and cooperation while having fun. This digital picture book service works by having the user input information about the child (age, interests, personality, favorite characters and themes, etc.), and the AI ​​analyzes this information to generate a story best suited to the child. The generated story is displayed as a digital picture book, which the child can enjoy reading. Through this service, children can develop self-formation, empathy, and cooperation. For example, the user inputs information about the child, such as age, interests, personality, favorite characters and themes. This information is input to the AI. Next, the AI ​​analyzes the input information. Based on the child's age, interests, personality, favorite characters and themes, the AI ​​generates a story best suited to the child. For example, for a child who likes animals, the AI ​​will generate a story featuring animals. It can also generate a story that includes scenes of cooperation with friends to foster cooperation. The generated story is displayed as a digital picture book. The child can read the story on a device such as a tablet or smartphone. The story includes illustrations and animations, making it enjoyable for the child to read. This service allows children to develop self-identity, empathy, and cooperation. For example, reading stories where the protagonist overcomes difficulties can promote self-development. Similarly, reading stories where children cooperate with friends can foster empathy and cooperation. In this way, the digital picture book service automatically generates stories tailored to each child, allowing them to develop self-identity, empathy, and cooperation while having fun.

[0029] The digital picture book service according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives information about the child. This information includes, but is not limited to, age, interests, personality, favorite characters, and themes. The reception unit receives information entered by the user, for example. The reception unit can also support multiple input methods, such as voice input and touch input. For example, the reception unit uses speech recognition technology to convert what the user says into text data. The analysis unit analyzes the information entered by the reception unit. The analysis unit uses, for example, a machine learning algorithm to analyze information such as the child's age, interests, personality, favorite characters, and themes. The analysis unit can also use natural language processing technology to analyze the entered text data. For example, the analysis unit extracts keywords from the text data and performs analysis based on them. The generation unit generates a story based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a story that is optimal for the child. The generation unit can also use a multimodal generation AI to generate the story content. For example, the generation unit uses text generation AI to generate the storyline of a narrative and multimodal generation AI to generate illustrations and animations. The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on a device such as a tablet or smartphone. The display unit also includes interactive elements so that children can enjoy reading the story. For example, the display unit supports touch and voice operations, allowing children to interact with characters in the story. As a result, the digital picture book service according to this embodiment automatically generates a story tailored to each child, fostering self-formation, empathy, and cooperation while having fun.

[0030] The reception desk receives information about the child. This information may include, but is not limited to, age, interests, personality, and favorite characters or themes. The reception desk also accepts information entered by the user. Furthermore, the reception desk can support multiple input methods, such as voice input and touch input. For example, the reception desk uses speech recognition technology to convert what the user says into text data. Specifically, the speech recognition technology analyzes the user's speech in real time and converts the voice data into text data. In this process, noise cancellation technology is used to remove ambient noise and achieve accurate speech recognition. With touch input, the user enters information by touching the keyboard or selections on the screen. This allows the user to operate intuitively and reduces the effort required for input. In addition, the reception desk has a function to save the user's input history and simplify subsequent inputs. For example, it reduces the burden on the user by automatically completing previously entered information. The reception desk also protects privacy by encrypting and securely storing the entered information. In this way, the reception desk can provide an environment in which users can enter information with peace of mind and promote the use of the digital picture book service.

[0031] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses machine learning algorithms to analyze information such as the child's age, interests, personality, and favorite characters and themes. The analysis unit can also analyze the entered text data using natural language processing technology. For example, the analysis unit extracts keywords from the text data and performs analysis based on them. Specifically, the machine learning algorithm builds a model that predicts the child's interests and personality based on past data. This model analyzes the entered information and identifies the elements of a story that are best suited to the child. Furthermore, natural language processing technology analyzes the text data and understands the context and meaning to achieve more accurate analysis. For example, the analysis unit extracts keywords such as "dinosaurs" and "adventure" from the entered text data and determines the theme of the story based on these. In addition, the analysis unit can integrate data from multiple sources and perform comprehensive analysis. For example, by considering past usage history and feedback from other users in addition to the child's age and personality, it can provide more personalized stories. In this way, the analysis unit can provide a foundation for generating optimal stories tailored to each child and improve the quality of the digital picture book service.

[0032] The generation unit generates stories based on information analyzed by the analysis unit. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate stories optimized for children. The generation unit can also generate story content using a multimodal generation AI. For example, the generation unit can use the text generation AI to generate the storyline and the multimodal generation AI to generate illustrations and animations. Specifically, the text generation AI generates the story plot and character dialogue based on keywords and themes provided by the analysis unit. In this process, the AI ​​refers to a database of past stories to construct a natural and engaging narrative. The multimodal generation AI generates corresponding illustrations and animations based on the text data. For example, it can automatically generate character movements and backgrounds to match story scenes, creating a visually appealing digital picture book. Furthermore, the generation unit can modify the story content in real time based on user feedback. For example, if a user likes a particular character or scene, it can generate a story that emphasizes those elements. The generation unit also supports multiple languages ​​and can generate stories in different languages. This allows the generation unit to provide personalized stories tailored to each child, enhancing the appeal of the digital picture book service.

[0033] The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on devices such as tablets and smartphones. The display unit also includes interactive elements so that children can enjoy reading the story. For example, the display unit supports touch and voice control, allowing children to interact with characters in the story. Specifically, by touching the characters displayed on the tablet or smartphone screen, the characters move and speak. In addition, the voice control function allows the characters to respond when the child speaks. This allows children to interact with the characters in the story and become more deeply immersed in the story. Furthermore, the display unit plays background music and sound effects in accordance with the progress of the story, stimulating not only sight but also hearing to provide a richer experience. The display unit also has a function to record the progress of the story and automatically save the starting position for the next reading. This allows children to continue reading from where they left off if they interrupt the story. Furthermore, the display unit also provides a function that allows parents and educators to check the child's reading history and progress, supporting the child's development. This allows the display unit to provide an optimal environment for children to learn and grow while having fun, thereby enhancing the value of the digital picture book service.

[0034] The reception desk can receive information such as age, interests, personality, and favorite characters or themes. For example, the reception desk can receive information entered by the user. The reception desk can also support multiple input methods, such as voice input and touch input. For example, the reception desk can use speech recognition technology to convert what the user says into text data. This allows for the generation of more personalized stories by receiving detailed information about the child. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user-entered information into an AI, which then analyzes and accepts the information.

[0035] The analysis unit includes algorithms for generating stories best suited to children. For example, the analysis unit uses machine learning algorithms to analyze information such as the child's age, interests, personality, favorite characters, and themes. The analysis unit can also analyze input text data using natural language processing techniques. For instance, it extracts keywords from text data and performs analysis based on those keywords. This enables analysis to generate stories best suited to children. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs user-generated information into the AI, which then analyzes the information and provides data for generating the optimal story.

[0036] The generation unit includes scenarios and scenes within the story that foster self-formation, empathy, and cooperation. The generation unit generates stories optimized for children, for example, using text generation AI (e.g., LLM). It can also generate story content using multimodal generation AI. For example, the generation unit uses text generation AI to generate the storyline and multimodal generation AI to generate illustrations and animations. This allows for the development of children's self-formation, empathy, and cooperation through storytelling. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs user information into the AI, which analyzes the information and generates the optimal story.

[0037] The reception desk can analyze a child's past reading history and provide guidance for optimal information input. For example, the reception desk can automatically suggest relevant information based on themes and characters from stories the child has read in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the child has used in the past. Furthermore, the reception desk can predict and suggest themes and characters to be used at specific times of the day based on the child's past reading history. This allows for the generation of more appropriate stories by guiding the child to optimal information input based on their past reading history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's past reading history into an AI, which can then analyze the information and provide guidance for optimal information input.

[0038] The reception desk can customize input fields based on the child's current learning situation and interests when information is entered. For example, the reception desk prioritizes inputting information related to the topic the child is currently studying. It can also automatically suggest relevant characters and themes based on the child's interests. Furthermore, the reception desk can input information on appropriate difficulty levels according to the child's learning progress. This enables information input that is tailored to the child's learning situation and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's current learning situation and interests into the AI, which can then analyze the information and customize the input fields.

[0039] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location during data entry. For example, the reception desk can automatically suggest themes and characters related to the child's current location. It can also suggest relevant story settings based on the child's geographical location. Furthermore, the reception desk can input appropriate background information considering the child's location. This enables information input that takes geographical location into account. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's geographical location into an AI, which can then analyze the information and prioritize inputting highly relevant information.

[0040] The reception desk can analyze a child's social media activity and input relevant information when data is entered. For example, the reception desk can automatically suggest relevant information based on themes and characters the child has shown interest in on social media. The reception desk can also analyze a child's social media activity and suggest relevant story settings. Furthermore, the reception desk can input appropriate background information considering the child's social media activity. This enables information input based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input a child's social media activity into an AI, which can then analyze the information and input relevant information.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the child's past reading history. For example, the analysis unit analyzes relevant information based on the themes and characters of stories the child has read in the past. The analysis unit can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, the analysis unit can predict themes and characters that the child will use at specific times of day based on their past reading history and incorporate this into the analysis. This makes it possible to improve the accuracy of analysis based on past reading history. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's past reading history into the AI, which can then analyze the information and provide data to generate the most suitable story.

[0042] The analysis unit can customize its analysis methods based on the child's current learning situation during the analysis process. For example, the analysis unit prioritizes analyzing information related to the topic the child is currently studying. It can also analyze information of appropriate difficulty level according to the child's learning progress. Furthermore, the analysis unit can analyze relevant characters and themes based on the child's interests. This allows for the customization of the analysis method according to the learning situation. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's current learning situation into the AI, which can then analyze the information and customize the analysis method.

[0043] The analysis unit can perform analysis while considering the child's geographical location. For example, the analysis unit can perform analysis based on themes and characters related to the child's current location. The analysis unit can also analyze the setting of a relevant story based on the child's geographical location. Furthermore, the analysis unit can analyze appropriate background information while considering the child's location. This enables analysis that takes geographical location into account. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's geographical location into the AI, which can then analyze the information and perform analysis based on relevant themes and characters.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the child during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the child has read in the past. The analysis unit can also analyze relevant literature related to the child and provide data to generate the optimal story. Furthermore, the analysis unit can predict specific themes and characters from the child's relevant literature and incorporate them into the analysis. This makes it possible to improve the accuracy of the analysis based on relevant literature. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input relevant literature related to the child into the AI, which can then analyze the information and provide data to generate the optimal story.

[0045] The generation unit can generate the most suitable story by referring to the child's past reading history. For example, it can generate a related story based on themes and characters from stories the child has read in the past. The generation unit can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, the generation unit can predict themes and characters the child will use at specific times of the day based on their past reading history and reflect this in the story. This makes it possible to generate the most suitable story based on past reading history. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's past reading history into the AI, which can then analyze the information and generate the most suitable story.

[0046] The generation unit can customize the content of stories based on the child's current learning situation when generating them. For example, the generation unit can prioritize generating stories related to themes the child is currently studying. It can also generate stories of appropriate difficulty levels according to the child's learning progress. Furthermore, the generation unit can incorporate relevant characters and themes into the stories based on the child's interests. This allows for the customization of story content according to the learning situation. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's current learning situation into the AI, which can then analyze the information and customize the story content.

[0047] The generation unit can generate an optimal story by considering the child's geographical location during story generation. For example, the generation unit can generate a story based on themes and characters related to the child's current location. It can also generate relevant story settings based on the child's geographical location. Furthermore, the generation unit can reflect appropriate background information into the story, taking the child's location into consideration. This makes it possible to generate an optimal story that takes geographical location into account. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's geographical location into the AI, which can then analyze the information and generate an optimal story.

[0048] The generation unit can analyze a child's social media activity and adjust the story content during story generation. For example, the generation unit can generate a story based on themes and characters that the child has shown interest in on social media. It can also analyze a child's social media activity and generate relevant story settings. Furthermore, the generation unit can consider the child's social media activity and incorporate appropriate background information into the story. This makes it possible to adjust the story content based on social media activity. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input a child's social media activity into the AI, which can then analyze the information and adjust the story content.

[0049] The display unit can select the optimal display method when displaying a digital picture book by referring to the child's past reading history. For example, the display unit can provide relevant display methods based on the themes and characters of stories the child has read in the past. The display unit can also analyze the child's past reading history and select the optimal display method. Furthermore, the display unit can predict themes and characters to be used at specific times of the day based on the child's past reading history and reflect this in the display method. This makes it possible to select the optimal display method based on past reading history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the child's past reading history into AI, and the AI ​​can analyze the information and select the optimal display method.

[0050] The display unit can customize the displayed content based on the child's current learning situation when displaying a digital picture book. For example, the display unit can prioritize displaying content related to the theme the child is currently learning about. It can also provide displaying content of an appropriate difficulty level according to the child's learning progress. Furthermore, the display unit can reflect relevant characters and themes in the displayed content based on the child's interests. This makes it possible to customize the displayed content according to the learning situation. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the child's current learning situation into the AI, which can then analyze the information and customize the displayed content.

[0051] The display unit can select the optimal display method when displaying a digital picture book, taking into account the child's device information. For example, if the child is using a tablet, the display unit can provide a display method optimized for a large screen. It can also provide a display method adapted to the screen size if the child is using a smartphone. Furthermore, if the child is using a smartwatch, the display unit can provide a simple and highly visible display method. This enables the selection of the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the child's device information into the AI, which can then analyze the information and select the optimal display method.

[0052] The display unit can analyze a child's social media activity and adjust the displayed content when displaying a digital picture book. For example, the display unit can adjust the displayed content based on themes and characters that the child has shown interest in on social media. The display unit can also analyze a child's social media activity and provide relevant displayed content. Furthermore, the display unit can take into account the child's social media activity and reflect appropriate background information in the displayed content. This makes it possible to adjust the displayed content based on social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input a child's social media activity into AI, which can then analyze the information and adjust the displayed content.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a child's past reading history and provide guidance for optimal information input. For example, it can automatically suggest relevant information based on themes and characters from stories the child has read in the past. It can also prioritize suggesting input methods the child has used in the past (voice, text, etc.). Furthermore, it can predict and suggest themes and characters that the child might use at specific times of day based on their past reading history. This allows for the generation of more appropriate stories by guiding the child to input optimal information based on their past reading history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's past reading history into an AI, which can then analyze the information and provide guidance for optimal information input.

[0055] The analysis unit can improve the accuracy of its analysis by referring to the child's past reading history. For example, it can analyze relevant information based on the themes and characters of stories the child has read in the past. It can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, it can predict themes and characters the child will use at specific times of day based on their past reading history and incorporate this into the analysis. This makes it possible to improve the accuracy of analysis based on past reading history. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's past reading history into the AI, which can then analyze the information and provide data to generate the most suitable story.

[0056] The generation unit can generate the most suitable story by referring to the child's past reading history. For example, it can generate a related story based on themes and characters from stories the child has read in the past. It can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, it can predict themes and characters the child will use at specific times of the day based on their past reading history and reflect this in the story. This makes it possible to generate the most suitable story based on past reading history. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's past reading history into the AI, which can then analyze the information and generate the most suitable story.

[0057] The display unit can select the optimal display method when displaying a digital picture book by referring to the child's past reading history. For example, it can provide a relevant display method based on the themes and characters of stories the child has read in the past. It can also analyze the child's past reading history and select the optimal display method. Furthermore, it can predict themes and characters used at specific times of the day based on the child's past reading history and reflect this in the display method. This makes it possible to select the optimal display method based on past reading history. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the child's past reading history into AI, which can then analyze the information and select the optimal display method.

[0058] The reception desk can customize input fields based on the child's current learning situation and interests when information is entered. For example, it can prioritize inputting information related to the topic the child is currently studying. It can also automatically suggest relevant characters and themes based on the child's interests. Furthermore, it can input information on appropriate difficulty levels according to the child's learning progress. This makes it possible to input information that is tailored to the child's learning situation and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's current learning situation and interests into the AI, which can then analyze the information and customize the input fields.

[0059] The analysis unit can customize its analysis methods based on the child's current learning situation. For example, it can prioritize analyzing information related to the topic the child is currently studying. It can also analyze information of appropriate difficulty level according to the child's learning progress. Furthermore, it can analyze relevant characters and themes based on the child's interests. This allows for the customization of analysis methods according to the learning situation. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's current learning situation into the AI, which can then analyze the information and customize the analysis method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk enters the child's information. This information includes, for example, age, interests, personality, favorite characters or themes. The reception desk accepts the information entered by the user. It can also support multiple input methods, such as voice input and touch input. For example, it can use speech recognition technology to convert what the user says into text data. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses machine learning algorithms and natural language processing techniques to analyze information such as the child's age, interests, personality, favorite characters and themes. For example, it extracts keywords from text data and performs analysis based on those keywords. Step 3: The generation unit generates a story based on the information analyzed by the analysis unit. The generation unit uses text generation AI (e.g., LLM) and multimodal generation AI to generate a story best suited for children. For example, it uses text generation AI to generate the storyline and multimodal generation AI to generate illustrations and animations. Step 4: The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on a device such as a tablet or smartphone, and includes interactive elements so that children can enjoy reading the story. For example, it supports touch and voice control, allowing children to interact with the characters in the story.

[0062] (Example of form 2) The digital picture book service according to an embodiment of the present invention is a system in which AI automatically generates stories tailored to each child, fostering self-formation, empathy, and cooperation while having fun. This digital picture book service works by having the user input information about the child (age, interests, personality, favorite characters and themes, etc.), and the AI ​​analyzes this information to generate a story best suited to the child. The generated story is displayed as a digital picture book, which the child can enjoy reading. Through this service, children can develop self-formation, empathy, and cooperation. For example, the user inputs information about the child, such as age, interests, personality, favorite characters and themes. This information is input to the AI. Next, the AI ​​analyzes the input information. Based on the child's age, interests, personality, favorite characters and themes, the AI ​​generates a story best suited to the child. For example, for a child who likes animals, the AI ​​will generate a story featuring animals. It can also generate a story that includes scenes of cooperation with friends to foster cooperation. The generated story is displayed as a digital picture book. The child can read the story on a device such as a tablet or smartphone. The story includes illustrations and animations, making it enjoyable for the child to read. This service allows children to develop self-identity, empathy, and cooperation. For example, reading stories where the protagonist overcomes difficulties can promote self-development. Similarly, reading stories where children cooperate with friends can foster empathy and cooperation. In this way, the digital picture book service automatically generates stories tailored to each child, allowing them to develop self-identity, empathy, and cooperation while having fun.

[0063] The digital picture book service according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives information about the child. This information includes, but is not limited to, age, interests, personality, favorite characters, and themes. The reception unit receives information entered by the user, for example. The reception unit can also support multiple input methods, such as voice input and touch input. For example, the reception unit uses speech recognition technology to convert what the user says into text data. The analysis unit analyzes the information entered by the reception unit. The analysis unit uses, for example, a machine learning algorithm to analyze information such as the child's age, interests, personality, favorite characters, and themes. The analysis unit can also use natural language processing technology to analyze the entered text data. For example, the analysis unit extracts keywords from the text data and performs analysis based on them. The generation unit generates a story based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a story that is optimal for the child. The generation unit can also use a multimodal generation AI to generate the story content. For example, the generation unit uses text generation AI to generate the storyline of a narrative and multimodal generation AI to generate illustrations and animations. The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on a device such as a tablet or smartphone. The display unit also includes interactive elements so that children can enjoy reading the story. For example, the display unit supports touch and voice operations, allowing children to interact with characters in the story. As a result, the digital picture book service according to this embodiment automatically generates a story tailored to each child, fostering self-formation, empathy, and cooperation while having fun.

[0064] The reception desk receives information about the child. This information may include, but is not limited to, age, interests, personality, and favorite characters or themes. The reception desk also accepts information entered by the user. Furthermore, the reception desk can support multiple input methods, such as voice input and touch input. For example, the reception desk uses speech recognition technology to convert what the user says into text data. Specifically, the speech recognition technology analyzes the user's speech in real time and converts the voice data into text data. In this process, noise cancellation technology is used to remove ambient noise and achieve accurate speech recognition. With touch input, the user enters information by touching the keyboard or selections on the screen. This allows the user to operate intuitively and reduces the effort required for input. In addition, the reception desk has a function to save the user's input history and simplify subsequent inputs. For example, it reduces the burden on the user by automatically completing previously entered information. The reception desk also protects privacy by encrypting and securely storing the entered information. In this way, the reception desk can provide an environment in which users can enter information with peace of mind and promote the use of the digital picture book service.

[0065] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses machine learning algorithms to analyze information such as the child's age, interests, personality, and favorite characters and themes. The analysis unit can also analyze the entered text data using natural language processing technology. For example, the analysis unit extracts keywords from the text data and performs analysis based on them. Specifically, the machine learning algorithm builds a model that predicts the child's interests and personality based on past data. This model analyzes the entered information and identifies the elements of a story that are best suited to the child. Furthermore, natural language processing technology analyzes the text data and understands the context and meaning to achieve more accurate analysis. For example, the analysis unit extracts keywords such as "dinosaurs" and "adventure" from the entered text data and determines the theme of the story based on these. In addition, the analysis unit can integrate data from multiple sources and perform comprehensive analysis. For example, by considering past usage history and feedback from other users in addition to the child's age and personality, it can provide more personalized stories. In this way, the analysis unit can provide a foundation for generating optimal stories tailored to each child and improve the quality of the digital picture book service.

[0066] The generation unit generates stories based on information analyzed by the analysis unit. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate stories optimized for children. The generation unit can also generate story content using a multimodal generation AI. For example, the generation unit can use the text generation AI to generate the storyline and the multimodal generation AI to generate illustrations and animations. Specifically, the text generation AI generates the story plot and character dialogue based on keywords and themes provided by the analysis unit. In this process, the AI ​​refers to a database of past stories to construct a natural and engaging narrative. The multimodal generation AI generates corresponding illustrations and animations based on the text data. For example, it can automatically generate character movements and backgrounds to match story scenes, creating a visually appealing digital picture book. Furthermore, the generation unit can modify the story content in real time based on user feedback. For example, if a user likes a particular character or scene, it can generate a story that emphasizes those elements. The generation unit also supports multiple languages ​​and can generate stories in different languages. This allows the generation unit to provide personalized stories tailored to each child, enhancing the appeal of the digital picture book service.

[0067] The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on devices such as tablets and smartphones. The display unit also includes interactive elements so that children can enjoy reading the story. For example, the display unit supports touch and voice control, allowing children to interact with characters in the story. Specifically, by touching the characters displayed on the tablet or smartphone screen, the characters move and speak. In addition, the voice control function allows the characters to respond when the child speaks. This allows children to interact with the characters in the story and become more deeply immersed in the story. Furthermore, the display unit plays background music and sound effects in accordance with the progress of the story, stimulating not only sight but also hearing to provide a richer experience. The display unit also has a function to record the progress of the story and automatically save the starting position for the next reading. This allows children to continue reading from where they left off if they interrupt the story. Furthermore, the display unit also provides a function that allows parents and educators to check the child's reading history and progress, supporting the child's development. This allows the display unit to provide an optimal environment for children to learn and grow while having fun, thereby enhancing the value of the digital picture book service.

[0068] The reception desk can receive information such as age, interests, personality, and favorite characters or themes. For example, the reception desk can receive information entered by the user. The reception desk can also support multiple input methods, such as voice input and touch input. For example, the reception desk can use speech recognition technology to convert what the user says into text data. This allows for the generation of more personalized stories by receiving detailed information about the child. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user-entered information into an AI, which then analyzes and accepts the information.

[0069] The analysis unit includes algorithms for generating stories best suited to children. For example, the analysis unit uses machine learning algorithms to analyze information such as the child's age, interests, personality, favorite characters, and themes. The analysis unit can also analyze input text data using natural language processing techniques. For instance, it extracts keywords from text data and performs analysis based on those keywords. This enables analysis to generate stories best suited to children. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs user-generated information into the AI, which then analyzes the information and provides data for generating the optimal story.

[0070] The generation unit includes scenarios and scenes within the story that foster self-formation, empathy, and cooperation. The generation unit generates stories optimized for children, for example, using text generation AI (e.g., LLM). It can also generate story content using multimodal generation AI. For example, the generation unit uses text generation AI to generate the storyline and multimodal generation AI to generate illustrations and animations. This allows for the development of children's self-formation, empathy, and cooperation through storytelling. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs user information into the AI, which analyzes the information and generates the optimal story.

[0071] The reception desk can estimate the child's emotions and adjust the information input method based on the estimated emotions. For example, if the child is excited, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the child is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the child is tired, the reception desk can prioritize voice input to allow for quick information entry. This allows for more appropriate information to be entered by providing an information input method that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input image data of the child taken by a camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0072] The reception desk can analyze a child's past reading history and provide guidance for optimal information input. For example, the reception desk can automatically suggest relevant information based on themes and characters from stories the child has read in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the child has used in the past. Furthermore, the reception desk can predict and suggest themes and characters to be used at specific times of the day based on the child's past reading history. This allows for the generation of more appropriate stories by guiding the child to optimal information input based on their past reading history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's past reading history into an AI, which can then analyze the information and provide guidance for optimal information input.

[0073] The reception desk can customize input fields based on the child's current learning situation and interests when information is entered. For example, the reception desk prioritizes inputting information related to the topic the child is currently studying. It can also automatically suggest relevant characters and themes based on the child's interests. Furthermore, the reception desk can input information on appropriate difficulty levels according to the child's learning progress. This enables information input that is tailored to the child's learning situation and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's current learning situation and interests into the AI, which can then analyze the information and customize the input fields.

[0074] The reception unit can estimate the child's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the child is excited, the reception unit will input important information first. If the child is relaxed, the reception unit can also prioritize inputting detailed information. Furthermore, if the child is tired, the reception unit can also prioritize inputting simple information. This allows for the input of more appropriate information by prioritizing information according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input image data of the child taken by a camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0075] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location during data entry. For example, the reception desk can automatically suggest themes and characters related to the child's current location. It can also suggest relevant story settings based on the child's geographical location. Furthermore, the reception desk can input appropriate background information considering the child's location. This enables information input that takes geographical location into account. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's geographical location into an AI, which can then analyze the information and prioritize inputting highly relevant information.

[0076] The reception desk can analyze a child's social media activity and input relevant information when data is entered. For example, the reception desk can automatically suggest relevant information based on themes and characters the child has shown interest in on social media. The reception desk can also analyze a child's social media activity and suggest relevant story settings. Furthermore, the reception desk can input appropriate background information considering the child's social media activity. This enables information input based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input a child's social media activity into an AI, which can then analyze the information and input relevant information.

[0077] The analysis unit can estimate a child's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the child is relaxed, the analysis unit can perform a detailed analysis and generate a highly accurate story. If the child is in a hurry, the analysis unit can perform a simplified analysis and quickly generate a story. Furthermore, if the child is excited, the analysis unit can perform an analysis to generate a story that includes visually stimulating elements. This allows for adjustment of the analysis algorithm according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input image data of the child captured by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0078] The analysis unit can improve the accuracy of its analysis by referring to the child's past reading history. For example, the analysis unit analyzes relevant information based on the themes and characters of stories the child has read in the past. The analysis unit can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, the analysis unit can predict themes and characters that the child will use at specific times of day based on their past reading history and incorporate this into the analysis. This makes it possible to improve the accuracy of analysis based on past reading history. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's past reading history into the AI, which can then analyze the information and provide data to generate the most suitable story.

[0079] The analysis unit can customize its analysis methods based on the child's current learning situation during the analysis process. For example, the analysis unit prioritizes analyzing information related to the topic the child is currently studying. It can also analyze information of appropriate difficulty level according to the child's learning progress. Furthermore, the analysis unit can analyze relevant characters and themes based on the child's interests. This allows for the customization of the analysis method according to the learning situation. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's current learning situation into the AI, which can then analyze the information and customize the analysis method.

[0080] The analysis unit can estimate a child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is nervous, the analysis unit provides a simple and highly visible display method. If the child is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the child is in a hurry, the analysis unit can provide a concise display method. This makes it possible to adjust the display method of the analysis results according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input image data of the child taken by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0081] The analysis unit can perform analysis while considering the child's geographical location. For example, the analysis unit can perform analysis based on themes and characters related to the child's current location. The analysis unit can also analyze the setting of a relevant story based on the child's geographical location. Furthermore, the analysis unit can analyze appropriate background information while considering the child's location. This enables analysis that takes geographical location into account. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's geographical location into the AI, which can then analyze the information and perform analysis based on relevant themes and characters.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the child during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the child has read in the past. The analysis unit can also analyze relevant literature related to the child and provide data to generate the optimal story. Furthermore, the analysis unit can predict specific themes and characters from the child's relevant literature and incorporate them into the analysis. This makes it possible to improve the accuracy of the analysis based on relevant literature. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input relevant literature related to the child into the AI, which can then analyze the information and provide data to generate the optimal story.

[0083] The generation unit can estimate a child's emotions and adjust the story generation method based on the estimated emotions. For example, if the child is relaxed, the generation unit can generate a story that progresses at a leisurely pace. If the child is in a hurry, the generation unit can also generate a story that emphasizes the shortest route. Furthermore, if the child is excited, the generation unit can generate a story with visually stimulating effects. This allows for adjustment of the story generation method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit can input image data of a child taken by a camera into the generation AI and have the generation AI perform the estimation of the child's emotions.

[0084] The generation unit can generate the most suitable story by referring to the child's past reading history. For example, it can generate a related story based on themes and characters from stories the child has read in the past. The generation unit can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, the generation unit can predict themes and characters the child will use at specific times of the day based on their past reading history and reflect this in the story. This makes it possible to generate the most suitable story based on past reading history. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's past reading history into the AI, which can then analyze the information and generate the most suitable story.

[0085] The generation unit can customize the content of stories based on the child's current learning situation when generating them. For example, the generation unit can prioritize generating stories related to themes the child is currently studying. It can also generate stories of appropriate difficulty levels according to the child's learning progress. Furthermore, the generation unit can incorporate relevant characters and themes into the stories based on the child's interests. This allows for the customization of story content according to the learning situation. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's current learning situation into the AI, which can then analyze the information and customize the story content.

[0086] The generation unit can estimate a child's emotions and prioritize stories based on those emotions. For example, if a child is excited, the generation unit will prioritize generating visually stimulating stories. If a child is relaxed, the generation unit can also prioritize generating stories with a slower pace. Furthermore, if a child is tired, the generation unit can prioritize generating simple and easy-to-understand stories. This makes it possible to prioritize stories according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit can input image data of a child taken by a camera into the generation AI and have the generation AI estimate the child's emotions.

[0087] The generation unit can generate an optimal story by considering the child's geographical location during story generation. For example, the generation unit can generate a story based on themes and characters related to the child's current location. It can also generate relevant story settings based on the child's geographical location. Furthermore, the generation unit can reflect appropriate background information into the story, taking the child's location into consideration. This makes it possible to generate an optimal story that takes geographical location into account. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's geographical location into the AI, which can then analyze the information and generate an optimal story.

[0088] The generation unit can analyze a child's social media activity and adjust the story content during story generation. For example, the generation unit can generate a story based on themes and characters that the child has shown interest in on social media. It can also analyze a child's social media activity and generate relevant story settings. Furthermore, the generation unit can consider the child's social media activity and incorporate appropriate background information into the story. This makes it possible to adjust the story content based on social media activity. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input a child's social media activity into the AI, which can then analyze the information and adjust the story content.

[0089] The display unit can estimate a child's emotions and adjust the display method of the digital picture book based on the estimated emotions. For example, if the child is excited, the display unit can provide a display method with visually stimulating effects. It can also provide a display method with calming colors if the child is relaxed. Furthermore, if the child is tired, the display unit can provide a simple and highly visible display method. This allows for adjustment of the digital picture book display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input image data of the child captured by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0090] The display unit can select the optimal display method when displaying a digital picture book by referring to the child's past reading history. For example, the display unit can provide relevant display methods based on the themes and characters of stories the child has read in the past. The display unit can also analyze the child's past reading history and select the optimal display method. Furthermore, the display unit can predict themes and characters to be used at specific times of the day based on the child's past reading history and reflect this in the display method. This makes it possible to select the optimal display method based on past reading history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the child's past reading history into AI, and the AI ​​can analyze the information and select the optimal display method.

[0091] The display unit can customize the displayed content based on the child's current learning situation when displaying a digital picture book. For example, the display unit can prioritize displaying content related to the theme the child is currently learning about. It can also provide displaying content of an appropriate difficulty level according to the child's learning progress. Furthermore, the display unit can reflect relevant characters and themes in the displayed content based on the child's interests. This makes it possible to customize the displayed content according to the learning situation. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the child's current learning situation into the AI, which can then analyze the information and customize the displayed content.

[0092] The display unit can estimate a child's emotions and adjust the operation procedure of the digital picture book based on the estimated emotions. For example, if the child is excited, the display unit can provide simple and intuitive operation procedures. If the child is relaxed, the display unit can also provide detailed operation procedures. Furthermore, if the child is tired, the display unit can prioritize voice control for quick operation. This makes it possible to adjust the operation procedure according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input image data of the child taken by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0093] The display unit can select the optimal display method when displaying a digital picture book, taking into account the child's device information. For example, if the child is using a tablet, the display unit can provide a display method optimized for a large screen. It can also provide a display method adapted to the screen size if the child is using a smartphone. Furthermore, if the child is using a smartwatch, the display unit can provide a simple and highly visible display method. This enables the selection of the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the child's device information into the AI, which can then analyze the information and select the optimal display method.

[0094] The display unit can analyze a child's social media activity and adjust the displayed content when displaying a digital picture book. For example, the display unit can adjust the displayed content based on themes and characters that the child has shown interest in on social media. The display unit can also analyze a child's social media activity and provide relevant displayed content. Furthermore, the display unit can take into account the child's social media activity and reflect appropriate background information in the displayed content. This makes it possible to adjust the displayed content based on social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input a child's social media activity into AI, which can then analyze the information and adjust the displayed content.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The reception desk can estimate the child's emotions and adjust the information input method based on the estimated emotions. For example, if the child is excited, it can provide a simple and intuitive interface and minimize the input steps. If the child is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the child is tired, it can prioritize voice input to allow for quick information entry. This allows for more appropriate information to be entered by providing an information input method that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input image data of the child taken by a camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0097] The analysis unit can estimate a child's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the child is relaxed, it can perform a detailed analysis and generate a highly accurate story. If the child is in a hurry, it can perform a simplified analysis and quickly generate a story. Furthermore, if the child is excited, it can perform an analysis to generate a story that includes visually stimulating elements. This allows for adjustment of the analysis algorithm according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input image data of the child captured by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0098] The generation unit can estimate a child's emotions and adjust the story generation method based on the estimated emotions. For example, if the child is relaxed, it can generate a story that progresses at a leisurely pace. If the child is in a hurry, it can generate a story that emphasizes the shortest route. Furthermore, if the child is excited, it can generate a story with visually stimulating effects. This allows for adjustment of the story generation method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit can input image data of a child taken by a camera into the generation AI and have the generation AI perform the estimation of the child's emotions.

[0099] The display unit can estimate a child's emotions and adjust the display method of the digital picture book based on the estimated emotions. For example, if the child is excited, it can provide a display method with visually stimulating effects. If the child is relaxed, it can provide a display method with calming colors. Furthermore, if the child is tired, it can provide a simple and highly visible display method. This makes it possible to adjust the display method of the digital picture book according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the display unit may be performed using AI or not using AI. For example, the display unit can input image data of the child taken by a camera into the generative AI and have the generative AI perform the estimation of the child's emotions.

[0100] The reception desk can analyze a child's past reading history and provide guidance for optimal information input. For example, it can automatically suggest relevant information based on themes and characters from stories the child has read in the past. It can also prioritize suggesting input methods the child has used in the past (voice, text, etc.). Furthermore, it can predict and suggest themes and characters that the child might use at specific times of day based on their past reading history. This allows for the generation of more appropriate stories by guiding the child to input optimal information based on their past reading history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's past reading history into an AI, which can then analyze the information and provide guidance for optimal information input.

[0101] The analysis unit can improve the accuracy of its analysis by referring to the child's past reading history. For example, it can analyze relevant information based on the themes and characters of stories the child has read in the past. It can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, it can predict themes and characters the child will use at specific times of day based on their past reading history and incorporate this into the analysis. This makes it possible to improve the accuracy of analysis based on past reading history. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's past reading history into the AI, which can then analyze the information and provide data to generate the most suitable story.

[0102] The generation unit can generate the most suitable story by referring to the child's past reading history. For example, it can generate a related story based on themes and characters from stories the child has read in the past. It can also analyze the child's past reading history and provide data to generate the most suitable story. Furthermore, it can predict themes and characters the child will use at specific times of the day based on their past reading history and reflect this in the story. This makes it possible to generate the most suitable story based on past reading history. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit can input the child's past reading history into the AI, which can then analyze the information and generate the most suitable story.

[0103] The display unit can select the optimal display method when displaying a digital picture book by referring to the child's past reading history. For example, it can provide a relevant display method based on the themes and characters of stories the child has read in the past. It can also analyze the child's past reading history and select the optimal display method. Furthermore, it can predict themes and characters used at specific times of the day based on the child's past reading history and reflect this in the display method. This makes it possible to select the optimal display method based on past reading history. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the child's past reading history into AI, which can then analyze the information and select the optimal display method.

[0104] The reception desk can customize input fields based on the child's current learning situation and interests when information is entered. For example, it can prioritize inputting information related to the topic the child is currently studying. It can also automatically suggest relevant characters and themes based on the child's interests. Furthermore, it can input information on appropriate difficulty levels according to the child's learning progress. This makes it possible to input information that is tailored to the child's learning situation and interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's current learning situation and interests into the AI, which can then analyze the information and customize the input fields.

[0105] The analysis unit can customize its analysis methods based on the child's current learning situation. For example, it can prioritize analyzing information related to the topic the child is currently studying. It can also analyze information of appropriate difficulty level according to the child's learning progress. Furthermore, it can analyze relevant characters and themes based on the child's interests. This allows for the customization of analysis methods according to the learning situation. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the child's current learning situation into the AI, which can then analyze the information and customize the analysis method.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The reception desk enters the child's information. This information includes, for example, age, interests, personality, favorite characters or themes. The reception desk accepts the information entered by the user. It can also support multiple input methods, such as voice input and touch input. For example, it can use speech recognition technology to convert what the user says into text data. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses machine learning algorithms and natural language processing techniques to analyze information such as the child's age, interests, personality, favorite characters and themes. For example, it extracts keywords from text data and performs analysis based on those keywords. Step 3: The generation unit generates a story based on the information analyzed by the analysis unit. The generation unit uses text generation AI (e.g., LLM) and multimodal generation AI to generate a story best suited for children. For example, it uses text generation AI to generate the storyline and multimodal generation AI to generate illustrations and animations. Step 4: The display unit displays the story generated by the generation unit as a digital picture book. The display unit displays the story on a device such as a tablet or smartphone, and includes interactive elements so that children can enjoy reading the story. For example, it supports touch and voice control, allowing children to interact with the characters in the story.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs information about their child. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it generates a story based on the analyzed information. The display unit is implemented by the output device 40 of the smart device 14, where it displays the generated story as a digital picture book. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs information about the child. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the input information is analyzed. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a story is generated based on the analyzed information. The display unit is implemented by the speaker 240 of the smart glasses 214, where the generated story is displayed as a digital picture book. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs information about the child. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the input information is analyzed. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a story is generated based on the analyzed information. The display unit is implemented by the display 343 of the headset terminal 314, where the generated story is displayed as a digital picture book. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs information about the child. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it generates a story based on the analyzed information. The display unit is implemented by, for example, the speaker 240 of the robot 414, where it displays the generated story as a digital picture book. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) The reception area where you enter the child's information, An analysis unit analyzes the information input by the reception unit, A generation unit that generates a story based on the information analyzed by the analysis unit, The system includes a display unit that displays the story generated by the generation unit as a digital picture book. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept information such as age, interests, personality, and favorite characters or themes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Includes an algorithm for generating stories best suited for children. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The story includes scenarios and scenes designed to foster self-development, empathy, and cooperation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the child's emotions and adjusts the information input method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes a child's past reading history and provides guidance on optimal information entry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering information, the input fields are customized based on the child's current learning situation and interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the child's emotions and prioritizes the information to be entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering information, the system analyzes the child's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referring to the child's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis method is customized based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the child's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we refer to relevant literature on children to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the child's emotions and adjusts the story generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a story, the system references the child's past reading history to generate the most suitable story. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a story, customize the story content based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates the child's emotions and determines the priority of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a story, the system considers the child's geographical location to generate the most suitable story. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a story, we analyze children's social media activity and adjust the story's content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the child's emotions and adjusts how the digital picture book is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying digital picture books, the system selects the optimal display method by referring to the child's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying digital picture books, the displayed content is customized based on the child's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is It estimates the child's emotions and adjusts the operation procedure of the digital picture book based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying digital picture books, the optimal display method is selected considering the child's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying digital picture books, the content is adjusted by analyzing children's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where you enter the child's information, An analysis unit analyzes the information input by the reception unit, A generation unit that generates a story based on the information analyzed by the analysis unit, The system includes a display unit that displays the story generated by the generation unit as a digital picture book. A system characterized by the following features.

2. The aforementioned reception unit is We accept information such as age, interests, personality, and favorite characters or themes. The system according to feature 1.

3. The aforementioned analysis unit, Includes an algorithm for generating stories best suited for children. The system according to feature 1.

4. The generating unit is The story includes scenarios and scenes designed to foster self-development, empathy, and cooperation. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the child's emotions and adjusts the information input method based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes a child's past reading history and provides guidance on optimal information entry. The system according to feature 1.

7. The aforementioned reception unit is When entering information, the input fields are customized based on the child's current learning situation and interests. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the child's emotions and prioritizes the information to be entered based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the child's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When entering information, the system analyzes the child's social media activity and inputs relevant information. The system according to feature 1.

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