system

Abstract

The system according to this embodiment aims to recommend picture books that will interest children and to ensure that parents can have reading time with their children even when they are busy. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a recommendation unit, a generation unit, a narration unit, and a notification unit. The reception unit receives information about the child. The analysis unit analyzes the information entered by the reception unit. The recommendation unit recommends picture books based on the information analyzed by the analysis unit. The generation unit generates the content of the picture books recommended by the recommendation unit. The narration unit reads aloud the content of the picture books generated by the generation unit. The notification unit sends a notification to the child at a designated time to inform them of reading time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to find picture books that attract children's interest, and it is difficult to ensure reading time with children when guardians are busy.

[0005] The system according to the embodiment aims to recommend picture books that attract children's interest and ensure reading time with children even when guardians are busy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a generation unit, a narration unit, and a notification unit. The reception unit receives information about the child. The analysis unit analyzes the information entered by the reception unit. The recommendation unit recommends picture books based on the information analyzed by the analysis unit. The generation unit generates the content of the picture books recommended by the recommendation unit. The narration unit reads aloud the content of the picture books generated by the generation unit. The notification unit sends a notification to the child at a designated time to inform them of reading time. [Effects of the Invention]

[0007] The system according to this embodiment recommends picture books that will interest children, allowing parents to secure reading time with their children even when they are busy. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The picture book recommendation system according to an embodiment of the present invention is a system that uses AI to recommend picture books based on a child's age, personality, existing preferences, etc., and provides an infinite variety. The picture book recommendation system takes information such as the child's age, personality, and existing preferences as input, and the AI ​​analyzes this information to pick out picture books that the child is most likely to be interested in. Furthermore, the AI ​​generates the content of the picture books, providing an infinite variety. This allows for the creation of new stories and characters based on the child's favorite themes. In addition, by utilizing the AI ​​narration function, children can read picture books even when their parents are busy. Furthermore, the system can call the child at a designated time and send a notification to encourage reading time. For example, the picture book recommendation system takes information such as the child's age, personality, and existing preferences as input. In this case, the parent only needs to input the child's basic information. For example, the child's age, favorite animals or characters, themes of interest, etc. are entered. This information is input to the AI. Next, the AI ​​analyzes the input information and picks out picture books that the child is most likely to be interested in. The AI ​​selects the optimal picture book based on past data and the preferences of other children. For example, if a 3-year-old child likes animals, the system will recommend picture books featuring animals. Furthermore, the AI ​​generates the content of the picture books, offering endless variations. The AI ​​creates new stories and characters based on the child's favorite themes. For example, for a child who loves animals, it will generate a story about animals on adventure. This allows children to enjoy a new picture book every time. In addition, the AI ​​narration function allows children to read picture books even when their parents are busy. The AI ​​can read the contents of the picture book aloud. For example, even when parents are busy at work, the AI ​​can read the picture book aloud, allowing children to enjoy it on their own. Moreover, it sends a notification to call the child over at a designated time to encourage reading time. The AI ​​sends a notification to the child at the time set by the parent, reminding them it's reading time. This ensures that children have regular reading time. This system eliminates the effort of finding interesting and educational picture books for children and prevents them from getting bored of reading the same book repeatedly.Furthermore, even when parents are busy, children can use the AI ​​narration function to read picture books, thus ensuring they have time to read. This allows the picture book recommendation system to keep children interested.

[0029] The picture book recommendation system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a generation unit, a narration unit, and a notification unit. The reception unit receives information about the child. This information includes, but is not limited to, age, gender, interests, and past reading history. For example, the reception unit only requires the parent to input basic information about the child. The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining or machine learning algorithms, but is not limited to, such methods. The analysis unit selects the most suitable picture book based on, for example, past data and the preferences of other children. The recommendation unit recommends picture books based on the information analyzed by the analysis unit. The recommendation is performed using, for example, similarity calculation or ranking algorithms, but is not limited to, such methods. The recommendation unit recommends picture books based on, for example, information such as the child's age, personality, and existing preferences. The generation unit generates the content of the picture book recommended by the recommendation unit. The generation is performed using, for example, a story generation algorithm or a character generation algorithm, but is not limited to, such methods. The generation unit creates new stories and characters based on the child's favorite themes, for example. The narration unit reads aloud the contents of the picture book generated by the generation unit. The narration is performed using, for example, speech synthesis technology or text-to-speech technology, but is not limited to these examples. The narration unit allows, for example, children to enjoy the picture book even when their parents are busy. The notification unit sends a notification to the child at a designated time to let them know it's time to read. The notification is performed using, for example, a timer function, but is not limited to these examples. The notification unit sends a notification to the child at a time set by the parent to let them know it's time to read. In this way, the picture book recommendation system according to the embodiment can recommend the most suitable picture book based on the child's information and keep the child interested by providing an endless variety.

[0030] The reception desk inputs information about the child. This information may include, but is not limited to, age, gender, interests, and past reading history. The reception desk only requires parents to input basic information about their child. Specifically, the reception desk provides a user-friendly interface to make it easy for parents to input information. For example, drop-down menus and checkboxes can be used to select age, gender, and areas of interest. In addition to manual input by parents, the system also has a function to automatically record past reading history. This allows parents to accurately input information about their child without any hassle. Furthermore, the reception desk has security measures in place to safely store the entered information and protect privacy. For example, data is encrypted, and firewalls and authentication systems are in place to prevent unauthorized access from external sources. This allows parents to input information about their child with peace of mind.

[0031] The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit selects the most suitable picture book based on past data and the preferences of other children. Specifically, it uses a large dataset to extract patterns related to children's interests and preferences. For example, it analyzes what kinds of picture books are preferred based on age, gender, and themes of interest. Machine learning algorithms use this data to build a model that predicts the most suitable picture book for each child. This model can continuously incorporate new data to improve its accuracy. Furthermore, the analysis unit uses natural language processing techniques to analyze the content and themes of picture books and identify books that match a child's interests. For example, it uses text mining techniques to extract the story and character characteristics of picture books and select books that match a child's interests. The analysis unit can also use clustering algorithms to group children with similar interests and recommend the most suitable picture book for each group. This allows the analysis unit to analyze children's information from multiple perspectives and select the most suitable picture book.

[0032] The recommendation unit recommends picture books based on information analyzed by the analysis unit. Recommendations are made using, for example, similarity calculations or ranking algorithms. The recommendation unit recommends picture books based on information such as the child's age, personality, and existing preferences. Specifically, the recommendation unit lists the most suitable picture books for each child based on the data provided by the analysis unit. Similarity calculation algorithms identify the most similar picture books based on previously read picture books and the preferences of other children. Ranking algorithms rank the best picture books, taking into account their popularity and ratings. Furthermore, the recommendation unit provides parents and children with a list of recommended picture books through a user interface. This list includes information such as the picture book title, author, brief synopsis, and rating, making it easy for parents and children to select. The recommendation unit can also collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, it collects data such as whether recommended picture books were actually read and their ratings after reading, using this information to improve the algorithms. This allows the recommendation unit to provide picture books best suited to children's interests and preferences, enhancing their reading experience.

[0033] The generation unit generates the content of picture books recommended by the recommendation unit. Generation is performed using, for example, story generation algorithms and character generation algorithms, but is not limited to these examples. The generation unit creates new stories and characters based on themes that children like. Specifically, the generation unit uses natural language generation technology to automatically generate stories that match children's interests. For example, if a child is interested in animals, it will generate an adventure story with animals as the main characters. Character generation algorithms can also design characters that match children's preferences and incorporate them into the story. Furthermore, the generation unit can also automatically generate illustrations and layouts for picture books. For example, it can use image generation algorithms to create illustrations that match the story and automatically arrange the page layout. This allows the generation unit to quickly generate original picture books that match children's interests. The generation unit can also provide parents and children with a preview of the generated picture book content and allow for modifications and customizations as needed. This allows the generation unit to provide picture books that are best suited to children's interests and preferences, enriching their reading experience.

[0034] The narration unit reads aloud the content of the picture book generated by the generation unit. Narration is performed using, for example, speech synthesis technology or text-to-speech technology, but is not limited to these examples. The narration unit allows children to enjoy picture books even when their parents are busy. Specifically, the narration unit inputs the generated picture book text into a speech synthesis engine and reads it aloud in a natural voice. The speech synthesis engine can select a voice quality and tone that is appealing to children and can express emotions that match the atmosphere of the story. Furthermore, the narration unit also has a function to adjust the speed and volume of the voice, allowing it to be customized to the child's preferences. The narration unit also has a function to automatically turn the pages of the picture book, allowing children to enjoy the book without using their hands. In this way, the narration unit provides an environment where children can enjoy picture books even when their parents are busy. In addition, the narration unit supports multiple languages ​​and can read aloud in different languages. This allows children to enjoy reading in different languages ​​and can also be used for language learning.

[0035] The notification unit sends notifications to children at designated times to remind them it's reading time. These notifications may, for example, be sent using a timer function, but are not limited to this. For instance, the notification unit sends notifications to children at times set by their parents. Specifically, it sends notifications to devices such as smartphones and tablets to remind children it's reading time. Notifications can be sent in various forms, including pop-up messages, audio alerts, and vibrations. The notification unit can also send regular notifications based on a schedule set by the parents. For example, sending notifications at a fixed time each day can help children develop reading habits. Furthermore, the notification unit tracks reading progress and reports it to parents. This allows parents to understand their child's reading situation and provide appropriate support. The notification unit plays a role in creating an environment where children can enjoy reading and promoting reading habits.

[0036] The narration unit can read aloud the contents of a picture book using speech synthesis technology. For example, the narration unit can read aloud the contents of a picture book using speech synthesis technology. For example, the narration unit can convert text to speech using TTS (Text-to-Speech) technology. The narration unit can also generate more natural-sounding speech using speech sample-based synthesis technology. For example, the narration unit can generate speech based on speech samples and read aloud the contents of a picture book. This allows children to enjoy picture books even when their parents are busy, by using speech synthesis technology. Some or all of the above-described processes in the narration unit may be performed using AI, for example, or without AI. For example, the narration unit can input text data into a generating AI and have the generating AI generate speech data.

[0037] The notification unit can use a timer function to send a notification to the child at a specified time to remind them of reading time. For example, the notification unit can send a notification to the child at a specified time using a timer function. For example, the notification unit can send a notification to the child at a time set by the parent to remind them of reading time. The notification unit can also customize how the time is set and the format of the notification. For example, the notification unit can send notifications using voice notifications or text messages. This allows the child to have regular reading time by using the timer function. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the time set by the parent into a generating AI and have the generating AI execute the sending of the notification.

[0038] The analysis unit can select the most suitable picture book based on past data and the preferences of other children. For example, the analysis unit can select the most suitable picture book based on past data and the preferences of other children. For example, the analysis unit can select a picture book based on past reading history and past recommendation results. The analysis unit can also select a picture book based on the preferences of other children. For example, the analysis unit can select a picture book based on survey results and behavioral logs. This allows for the selection of a more appropriate picture book by using past data and the preferences of other children. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI select the most suitable picture book.

[0039] The generation unit can create new stories and characters based on themes that children like. For example, the generation unit can create new stories and characters based on themes that children like. For example, for a child who likes animals, the generation unit can generate a story about animals going on adventures. The generation unit can also generate a story set in a fantasy world for a child who likes fantasy. For example, the generation unit can generate a story featuring wizards and dragons. In this way, by creating new stories and characters based on themes that children like, it is possible to keep children interested. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the child's favorite theme into the generation AI and have the generation AI perform the generation of new stories and characters.

[0040] The recommendation unit can recommend picture books based on information such as the child's age, personality, and existing preferences. For example, if a 3-year-old child likes animals, the recommendation unit will recommend picture books featuring animals. The recommendation unit can also recommend quiet picture books to introverted children. For example, the recommendation unit will recommend picture books with nature or animal themes. By recommending picture books based on information such as the child's age, personality, and existing preferences, the system can provide picture books that are most likely to interest the child. Some or all of the processing described above in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input the child's information into a generating AI and have the generating AI recommend the most suitable picture books.

[0041] The reception desk can analyze the parent's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the parent has used in the past. The reception desk can also automatically complete input fields based on information the parent has entered in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the parent's past input history. This allows the reception desk to suggest the optimal input method by analyzing the parent's past input 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 parent's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0042] The reception desk can customize input fields based on the child's current interests and concerns when information is entered. For example, if the child is interested in animals, the reception desk will prioritize displaying input fields related to animals. The reception desk can also add input fields related to a specific character if the child is interested in that character. The reception desk can also customize input fields related to a specific theme if the child is interested in that theme. This allows for the input of more appropriate information by customizing input fields based on the child's current interests and concerns. 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 interests and concerns into a generating AI and have the generating AI perform the customization of input fields.

[0043] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location when entering information. For example, if the child lives in a specific area, the reception desk can prioritize inputting information related to that area. For example, if the child lives in a specific area, the reception desk can prioritize inputting information related to that area. For example, if the child is interested in a specific place, the reception desk can prioritize inputting information related to that place. For example, if the child is traveling, the reception desk can prioritize inputting information related to the travel destination. For example, if the child is traveling, the reception desk can prioritize inputting information related to the travel destination. In this way, highly relevant information can be prioritized by considering the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's geographical location information into a generating AI and have the generating AI perform the input of highly relevant information.

[0044] The reception desk can analyze the parent's social media activity and input relevant information when information is entered. For example, the reception desk can suggest relevant input fields based on information the parent has shared on social media. The reception desk can also customize input fields based on information about accounts the parent follows on social media. The reception desk can also add input fields based on topics the parent has shown interest in on social media. This allows relevant information to be entered by analyzing the parent's 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 the parent's social media activity into a generating AI and have the generating AI input the relevant information.

[0045] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past data. The analysis unit can also analyze past data and adjust the parameters of the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm by referring to past data. In this way, the analysis algorithm can be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0046] The analysis unit can apply different analysis methods depending on the child's age and personality during the analysis. For example, the analysis unit can select an appropriate analysis method according to the child's age. The analysis unit can also customize the analysis method according to the child's personality. The analysis unit can also apply the optimal analysis method considering the child's age and personality. This allows for more appropriate analysis by applying analysis methods according to the child's age and personality. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of the analysis method.

[0047] The analysis unit can perform analysis while considering the child's geographical location information. For example, if the child lives in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the child lives in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the child is interested in a specific place, the analysis unit can prioritize analyzing information related to that place. For example, if the child is traveling, the analysis unit can prioritize analyzing information related to the travel destination. For example, if the child is traveling, the analysis unit can prioritize analyzing information related to the travel destination. In this way, by considering the child's geographical location information, highly relevant information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. For example, the analysis unit can input the child's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the preferences and tendencies of other children during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the preferences of other children. The analysis unit can also analyze the tendencies of other children and adjust the analysis algorithm. For example, the analysis unit can analyze the tendencies of other children and adjust the analysis algorithm. The analysis unit can also optimize the accuracy of its analysis by referring to the data of other children. For example, the analysis unit can optimize the accuracy of its analysis by referring to the data of other children. This allows the accuracy of the analysis to be improved by referring to the preferences and tendencies of other children. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the preferences and tendencies of other children into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0049] The recommendation section can adjust the level of detail of its recommendations based on the importance of the picture book. For example, if the picture book is important, the recommendation section will provide detailed recommendation information. For example, if the picture book is important, the recommendation section will provide detailed recommendation information. The recommendation section can also provide simple recommendation information for general picture books. For example, if the picture book is general, the recommendation section will provide simple recommendation information. The recommendation section can also adjust the level of detail of its recommendations based on the child's preferences. For example, the recommendation section can adjust the level of detail of its recommendations based on the importance of the picture book. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input picture book importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of its recommendations.

[0050] The recommendation unit can apply different recommendation algorithms depending on the category of the picture book. For example, in the case of an educational picture book, the recommendation unit can apply a recommendation algorithm that prioritizes educational effectiveness. For example, in the case of an entertaining picture book, the recommendation unit can apply a recommendation algorithm that prioritizes enjoyment. For example, in the case of an entertaining picture book, the recommendation unit can apply a recommendation algorithm that prioritizes enjoyment. For example, in the case of a picture book based on a specific theme, the recommendation unit can apply a recommendation algorithm that is best suited to that theme. By applying different recommendation algorithms depending on the category of the picture book, more appropriate recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input picture book category data into a generating AI and have the generating AI execute the application of the recommendation algorithm.

[0051] The recommendation unit can determine the priority of recommendations based on the publication date of the picture books. For example, the recommendation unit will prioritize recommending newer picture books. The recommendation unit can also determine the priority of older picture books by comparing them with other picture books. The recommendation unit can also adjust the priority of recommendations based on children's preferences, regardless of publication date. This allows for more appropriate recommendations by determining the priority of recommendations based on the publication date of the picture books. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input data on the publication date of the picture books into a generating AI and have the generating AI determine the priority of recommendations.

[0052] The recommendation unit can adjust the order of recommendations based on the relevance of the picture books. For example, the recommendation unit can prioritize recommending picture books that are most relevant to the child's preferences. The recommendation unit can also prioritize recommending picture books that are most appropriate for the child's age. The recommendation unit can also prioritize recommending picture books that are most relevant to the child's interests. By adjusting the order of recommendations based on the relevance of the picture books, more appropriate recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input data on the relevance of the picture books into a generating AI and have the generating AI adjust the order of recommendations.

[0053] The generation unit can select a story theme by referring to the child's past preferences during generation. For example, the generation unit can select a story theme based on themes the child has liked in the past. The generation unit can also analyze the child's past preferences and select the most suitable theme. The generation unit can also customize the story theme by referring to the child's past preferences. For example, the generation unit can customize the story theme by referring to the child's past preferences. This allows for the selection of a more appropriate story theme by referring to the child's past preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the child's past preferences into a generation AI and have the generation AI perform the selection of a story theme.

[0054] The generation unit can apply different story generation methods depending on the child's age and personality during generation. For example, the generation unit can select an appropriate story generation method depending on the child's age. The generation unit can also customize the story generation method depending on the child's personality. The generation unit can also apply the optimal story generation method considering the child's age and personality. By applying story generation methods according to the child's age and personality, more appropriate stories are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the child's age and personality into a generation AI and have the generation AI perform the application of story generation methods.

[0055] The generation unit can set the setting of the story while considering the child's geographical location information. For example, the generation unit can generate a story set in the area where the child lives. The generation unit can also generate a story set in a place the child is interested in. The generation unit can also generate a story set in the travel destination if the child is traveling. This allows for the setting of a more appropriate story by considering the child's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the child's geographical location information into the generation AI and have the generation AI perform the setting of the story.

[0056] The generation unit can optimize the story content by referring to the preferences and tendencies of other children during generation. For example, the generation unit can optimize the story content based on the preferences of other children. The generation unit can also analyze the tendencies of other children and adjust the story content. The generation unit can also optimize the story content by referring to data on other children. This allows for the generation of more appropriate story content by referring to the preferences and tendencies of other children. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the preferences and tendencies of other children into a generation AI and have the generation AI perform the optimization of the story content.

[0057] The narration unit can select the optimal narration method by referring to the child's past responses during narration. For example, the narration unit can select the optimal narration method based on the child's past preferred narration methods. The narration unit can also analyze the child's past responses and adjust the narration method. For example, the narration unit can analyze the child's past responses and adjust the narration method. The narration unit can also optimize the narration method by referring to the child's past responses. For example, the narration unit can optimize the narration method by referring to the child's past responses. This allows for the selection of a more appropriate narration method by referring to the child's past responses. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input data on the child's past responses into a generating AI and have the generating AI select the narration method.

[0058] The narration unit can apply different narration techniques depending on the content of the picture book during narration. For example, in the case of an educational picture book, the narration unit can apply narration techniques that prioritize educational effectiveness. For example, in the case of an educational picture book, the narration unit can apply narration techniques that prioritize educational effectiveness. The narration unit can also apply narration techniques that prioritize enjoyment in the case of an entertaining picture book. For example, in the case of an entertaining picture book, the narration unit can apply narration techniques that prioritize enjoyment. The narration unit can also apply narration techniques that are best suited to a specific theme in the case of a picture book based on a particular theme. For example, in the case of a picture book based on a specific theme, the narration unit can apply narration techniques that are best suited to that theme. By applying different narration techniques depending on the content of the picture book, more appropriate narration can be achieved. Some or all of the above processing in the narration unit may be performed using AI, or it may be performed without using AI. For example, the narration unit can input data on the content of the picture book into a generating AI and have the generating AI perform the application of narration techniques.

[0059] The narration unit can select the most appropriate narration method when narrating, taking into account the child's geographical location. For example, if the child lives in a specific area, the narration unit can select a narration method related to that area. For example, if the child lives in a specific area, the narration unit can select a narration method related to that area. For example, if the child is interested in a specific place, the narration unit can select a narration method related to that place. For example, if the child is traveling, the narration unit can select a narration method related to the travel destination. For example, if the child is traveling, the narration unit can select a narration method related to the travel destination. This allows for the selection of a more appropriate narration method by considering the child's geographical location. Some or all of the above processing in the narration unit may be performed using AI, or not. For example, the narration unit can input the child's geographical location information into a generating AI and have the generating AI select the narration method.

[0060] The narration unit can optimize the content of the narration by referring to the reactions of other children during narration. For example, the narration unit can optimize the content of the narration based on the reactions of other children. The narration unit can also analyze the reactions of other children and adjust the content of the narration. For example, the narration unit can analyze the reactions of other children and adjust the content of the narration. The narration unit can also optimize the content of the narration by referring to data of other children. For example, the narration unit can optimize the content of the narration by referring to data of other children. This allows for the provision of more appropriate narration content by referring to the reactions of other children. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input data on the reactions of other children into a generating AI and have the generating AI perform the optimization of the narration content.

[0061] The notification unit can select the optimal notification method by referring to the child's past responses when sending a notification. For example, the notification unit can select the optimal notification method based on the notification method the child has preferred in the past. The notification unit can also analyze the child's past responses and adjust the notification method. For example, the notification unit can analyze the child's past responses and adjust the notification method. The notification unit can also optimize the notification method by referring to the child's past responses. For example, the notification unit can optimize the notification method by referring to the child's past responses. This allows for the selection of a more appropriate notification method by referring to the child's past responses. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the child's past responses into a generating AI and have the generating AI select the notification method.

[0062] The notification unit can apply different notification methods depending on the child's age and personality when sending a notification. For example, the notification unit can select an appropriate notification method depending on the child's age. The notification unit can also customize the notification method depending on the child's personality. The notification unit can also apply the optimal notification method considering the child's age and personality. This allows for more appropriate notifications by applying notification methods according to the child's age and personality. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of notification methods.

[0063] The notification unit can select the most appropriate notification method when sending a notification, taking into account the child's geographical location. For example, if the child lives in a specific area, the notification unit can select a notification method related to that area. For example, if the child lives in a specific area, the notification unit can select a notification method related to that area. For example, if the child is interested in a specific place, the notification unit can select a notification method related to that place. For example, if the child is traveling, the notification unit can select a notification method related to the travel destination. For example, if the child is traveling, the notification unit can select a notification method related to the travel destination. This allows for the selection of a more appropriate notification method by considering the child's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input the child's geographical location into a generating AI and have the generating AI select the notification method.

[0064] The notification unit can optimize the content of a notification by referring to the reactions of other children. For example, the notification unit can optimize the content of a notification based on the reactions of other children. The notification unit can also analyze the reactions of other children and adjust the content of the notification. The notification unit can also optimize the content of a notification by referring to data on other children. This allows for the provision of more appropriate notification content by referring to the reactions of other children. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the reactions of other children into a generating AI and have the generating AI perform the optimization of the notification content.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can analyze the parent's past input history and suggest the optimal input method. For example, it can prioritize suggesting input methods the parent has used in the past (voice, text, etc.). It can also automatically complete input fields based on information the parent has previously entered. Furthermore, it can predict and suggest input methods to be used at specific times based on the parent's past input history. In this way, the optimal input method can be suggested by analyzing the parent's past input 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 parent's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0067] The reception desk can customize input fields based on the child's current interests when they enter information. For example, if the child is interested in animals, input fields related to animals will be displayed preferentially. If the child is interested in a particular character, input fields related to that character can be added. Furthermore, if the child is interested in a particular theme, input fields related to that theme can be customized. This allows for more appropriate information to be entered by customizing input fields based on the child's current 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 interests into a generating AI and have the generating AI perform the customization of input fields.

[0068] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location when entering information. For example, if the child lives in a specific area, information related to that area can be prioritized. Similarly, if the child is interested in a particular place, information related to that place can be prioritized. Furthermore, if the child is traveling, information related to the travel destination can be prioritized. This allows for the prioritization of highly relevant information by considering the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the child's geographical location into a generating AI and have the generating AI input highly relevant information.

[0069] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, it can select the optimal analysis algorithm based on past data. It can also analyze past data and adjust the parameters of the analysis algorithm. Furthermore, it can improve the accuracy of the analysis algorithm by referring to past data. In this way, the analysis algorithm can be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0070] The analysis unit can apply different analysis methods depending on the child's age and personality during the analysis. For example, it can select an appropriate analysis method according to the child's age. It can also customize the analysis method according to the child's personality. Furthermore, it can apply the optimal analysis method considering both the child's age and personality. This allows for more appropriate analysis by applying analysis methods according to the child's age and personality. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of the analysis method.

[0071] The analysis unit can perform analysis while considering the child's geographical location. For example, if the child lives in a specific area, it can prioritize the analysis of information related to that area. Similarly, if the child is interested in a particular place, it can prioritize the analysis of information related to that place. Furthermore, if the child is traveling, it can prioritize the analysis of information related to the travel destination. This allows for the prioritization of highly relevant information by considering the child's geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the child's geographical location into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk enters the child's information. This information includes, for example, age, gender, interests, and past reading history. Parents only need to enter their child's basic information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining or machine learning algorithms. Based on past data and the preferences of other children, the most suitable picture book is selected. Step 3: The recommendation unit recommends picture books based on the information analyzed by the analysis unit. Recommendations are made using, for example, similarity calculations or ranking algorithms. Picture books are recommended based on information such as the child's age, personality, and existing preferences. Step 4: The generation unit generates the picture book content recommended by the recommendation unit. Generation is performed using, for example, a story generation algorithm or a character generation algorithm. New stories and characters are created based on themes that children like. Step 5: The narration unit reads aloud the content of the picture book generated by the generation unit. The narration is performed using, for example, speech synthesis technology or text-to-speech technology. Children can enjoy the picture book even when their parents are busy. Step 6: The notification unit sends a notification to the child at the designated time to remind them it's reading time. The notification is sent, for example, using a timer function. When the time set by the parent arrives, a notification is sent to the child reminding them it's reading time.

[0074] (Example of form 2) The picture book recommendation system according to an embodiment of the present invention is a system that uses AI to recommend picture books based on a child's age, personality, existing preferences, etc., and provides an infinite variety. The picture book recommendation system takes information such as the child's age, personality, and existing preferences as input, and the AI ​​analyzes this information to pick out picture books that the child is most likely to be interested in. Furthermore, the AI ​​generates the content of the picture books, providing an infinite variety. This allows for the creation of new stories and characters based on the child's favorite themes. In addition, by utilizing the AI ​​narration function, children can read picture books even when their parents are busy. Furthermore, the system can call the child at a designated time and send a notification to encourage reading time. For example, the picture book recommendation system takes information such as the child's age, personality, and existing preferences as input. In this case, the parent only needs to input the child's basic information. For example, the child's age, favorite animals or characters, themes of interest, etc. are entered. This information is input to the AI. Next, the AI ​​analyzes the input information and picks out picture books that the child is most likely to be interested in. The AI ​​selects the optimal picture book based on past data and the preferences of other children. For example, if a 3-year-old child likes animals, the system will recommend picture books featuring animals. Furthermore, the AI ​​generates the content of the picture books, offering endless variations. The AI ​​creates new stories and characters based on the child's favorite themes. For example, for a child who loves animals, it will generate a story about animals on adventure. This allows children to enjoy a new picture book every time. In addition, the AI ​​narration function allows children to read picture books even when their parents are busy. The AI ​​can read the contents of the picture book aloud. For example, even when parents are busy at work, the AI ​​can read the picture book aloud, allowing children to enjoy it on their own. Moreover, it sends a notification to call the child over at a designated time to encourage reading time. The AI ​​sends a notification to the child at the time set by the parent, reminding them it's reading time. This ensures that children have regular reading time. This system eliminates the effort of finding interesting and educational picture books for children and prevents them from getting bored of reading the same book repeatedly.Furthermore, even when parents are busy, children can use the AI ​​narration function to read picture books, thus ensuring they have time to read. This allows the picture book recommendation system to keep children interested.

[0075] The picture book recommendation system according to this embodiment comprises a reception unit, an analysis unit, a recommendation unit, a generation unit, a narration unit, and a notification unit. The reception unit receives information about the child. This information includes, but is not limited to, age, gender, interests, and past reading history. For example, the reception unit only requires the parent to input basic information about the child. The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining or machine learning algorithms, but is not limited to, such methods. The analysis unit selects the most suitable picture book based on, for example, past data and the preferences of other children. The recommendation unit recommends picture books based on the information analyzed by the analysis unit. The recommendation is performed using, for example, similarity calculation or ranking algorithms, but is not limited to, such methods. The recommendation unit recommends picture books based on, for example, information such as the child's age, personality, and existing preferences. The generation unit generates the content of the picture book recommended by the recommendation unit. The generation is performed using, for example, a story generation algorithm or a character generation algorithm, but is not limited to, such methods. The generation unit creates new stories and characters based on the child's favorite themes, for example. The narration unit reads aloud the contents of the picture book generated by the generation unit. The narration is performed using, for example, speech synthesis technology or text-to-speech technology, but is not limited to these examples. The narration unit allows, for example, children to enjoy the picture book even when their parents are busy. The notification unit sends a notification to the child at a designated time to let them know it's time to read. The notification is performed using, for example, a timer function, but is not limited to these examples. The notification unit sends a notification to the child at a time set by the parent to let them know it's time to read. In this way, the picture book recommendation system according to the embodiment can recommend the most suitable picture book based on the child's information and keep the child interested by providing an endless variety.

[0076] The reception desk inputs information about the child. This information may include, but is not limited to, age, gender, interests, and past reading history. The reception desk only requires parents to input basic information about their child. Specifically, the reception desk provides a user-friendly interface to make it easy for parents to input information. For example, drop-down menus and checkboxes can be used to select age, gender, and areas of interest. In addition to manual input by parents, the system also has a function to automatically record past reading history. This allows parents to accurately input information about their child without any hassle. Furthermore, the reception desk has security measures in place to safely store the entered information and protect privacy. For example, data is encrypted, and firewalls and authentication systems are in place to prevent unauthorized access from external sources. This allows parents to input information about their child with peace of mind.

[0077] The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit selects the most suitable picture book based on past data and the preferences of other children. Specifically, it uses a large dataset to extract patterns related to children's interests and preferences. For example, it analyzes what kinds of picture books are preferred based on age, gender, and themes of interest. Machine learning algorithms use this data to build a model that predicts the most suitable picture book for each child. This model can continuously incorporate new data to improve its accuracy. Furthermore, the analysis unit uses natural language processing techniques to analyze the content and themes of picture books and identify books that match a child's interests. For example, it uses text mining techniques to extract the story and character characteristics of picture books and select books that match a child's interests. The analysis unit can also use clustering algorithms to group children with similar interests and recommend the most suitable picture book for each group. This allows the analysis unit to analyze children's information from multiple perspectives and select the most suitable picture book.

[0078] The recommendation unit recommends picture books based on information analyzed by the analysis unit. Recommendations are made using, for example, similarity calculations or ranking algorithms. The recommendation unit recommends picture books based on information such as the child's age, personality, and existing preferences. Specifically, the recommendation unit lists the most suitable picture books for each child based on the data provided by the analysis unit. Similarity calculation algorithms identify the most similar picture books based on previously read picture books and the preferences of other children. Ranking algorithms rank the best picture books, taking into account their popularity and ratings. Furthermore, the recommendation unit provides parents and children with a list of recommended picture books through a user interface. This list includes information such as the picture book title, author, brief synopsis, and rating, making it easy for parents and children to select. The recommendation unit can also collect user feedback to continuously improve the accuracy of its recommendation algorithms. For example, it collects data such as whether recommended picture books were actually read and their ratings after reading, using this information to improve the algorithms. This allows the recommendation unit to provide picture books best suited to children's interests and preferences, enhancing their reading experience.

[0079] The generation unit generates the content of picture books recommended by the recommendation unit. Generation is performed using, for example, story generation algorithms and character generation algorithms, but is not limited to these examples. The generation unit creates new stories and characters based on themes that children like. Specifically, the generation unit uses natural language generation technology to automatically generate stories that match children's interests. For example, if a child is interested in animals, it will generate an adventure story with animals as the main characters. Character generation algorithms can also design characters that match children's preferences and incorporate them into the story. Furthermore, the generation unit can also automatically generate illustrations and layouts for picture books. For example, it can use image generation algorithms to create illustrations that match the story and automatically arrange the page layout. This allows the generation unit to quickly generate original picture books that match children's interests. The generation unit can also provide parents and children with a preview of the generated picture book content and allow for modifications and customizations as needed. This allows the generation unit to provide picture books that are best suited to children's interests and preferences, enriching their reading experience.

[0080] The narration unit reads aloud the content of the picture book generated by the generation unit. Narration is performed using, for example, speech synthesis technology or text-to-speech technology, but is not limited to these examples. The narration unit allows children to enjoy picture books even when their parents are busy. Specifically, the narration unit inputs the generated picture book text into a speech synthesis engine and reads it aloud in a natural voice. The speech synthesis engine can select a voice quality and tone that is appealing to children and can express emotions that match the atmosphere of the story. Furthermore, the narration unit also has a function to adjust the speed and volume of the voice, allowing it to be customized to the child's preferences. The narration unit also has a function to automatically turn the pages of the picture book, allowing children to enjoy the book without using their hands. In this way, the narration unit provides an environment where children can enjoy picture books even when their parents are busy. In addition, the narration unit supports multiple languages ​​and can read aloud in different languages. This allows children to enjoy reading in different languages ​​and can also be used for language learning.

[0081] The notification unit sends notifications to children at designated times to remind them it's reading time. These notifications may, for example, be sent using a timer function, but are not limited to this. For instance, the notification unit sends notifications to children at times set by their parents. Specifically, it sends notifications to devices such as smartphones and tablets to remind children it's reading time. Notifications can be sent in various forms, including pop-up messages, audio alerts, and vibrations. The notification unit can also send regular notifications based on a schedule set by the parents. For example, sending notifications at a fixed time each day can help children develop reading habits. Furthermore, the notification unit tracks reading progress and reports it to parents. This allows parents to understand their child's reading situation and provide appropriate support. The notification unit plays a role in creating an environment where children can enjoy reading and promoting reading habits.

[0082] The narration unit can read aloud the contents of a picture book using speech synthesis technology. For example, the narration unit can read aloud the contents of a picture book using speech synthesis technology. For example, the narration unit can convert text to speech using TTS (Text-to-Speech) technology. The narration unit can also generate more natural-sounding speech using speech sample-based synthesis technology. For example, the narration unit can generate speech based on speech samples and read aloud the contents of a picture book. This allows children to enjoy picture books even when their parents are busy, by using speech synthesis technology. Some or all of the above-described processes in the narration unit may be performed using AI, for example, or without AI. For example, the narration unit can input text data into a generating AI and have the generating AI generate speech data.

[0083] The notification unit can use a timer function to send a notification to the child at a specified time to remind them of reading time. For example, the notification unit can send a notification to the child at a specified time using a timer function. For example, the notification unit can send a notification to the child at a time set by the parent to remind them of reading time. The notification unit can also customize how the time is set and the format of the notification. For example, the notification unit can send notifications using voice notifications or text messages. This allows the child to have regular reading time by using the timer function. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the time set by the parent into a generating AI and have the generating AI execute the sending of the notification.

[0084] The analysis unit can select the most suitable picture book based on past data and the preferences of other children. For example, the analysis unit can select the most suitable picture book based on past data and the preferences of other children. For example, the analysis unit can select a picture book based on past reading history and past recommendation results. The analysis unit can also select a picture book based on the preferences of other children. For example, the analysis unit can select a picture book based on survey results and behavioral logs. This allows for the selection of a more appropriate picture book by using past data and the preferences of other children. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI select the most suitable picture book.

[0085] The generation unit can create new stories and characters based on themes that children like. For example, the generation unit can create new stories and characters based on themes that children like. For example, for a child who likes animals, the generation unit can generate a story about animals going on adventures. The generation unit can also generate a story set in a fantasy world for a child who likes fantasy. For example, the generation unit can generate a story featuring wizards and dragons. In this way, by creating new stories and characters based on themes that children like, it is possible to keep children interested. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the child's favorite theme into the generation AI and have the generation AI perform the generation of new stories and characters.

[0086] The recommendation unit can recommend picture books based on information such as the child's age, personality, and existing preferences. For example, if a 3-year-old child likes animals, the recommendation unit will recommend picture books featuring animals. The recommendation unit can also recommend quiet picture books to introverted children. For example, the recommendation unit will recommend picture books with nature or animal themes. By recommending picture books based on information such as the child's age, personality, and existing preferences, the system can provide picture books that are most likely to interest the child. Some or all of the processing described above in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input the child's information into a generating AI and have the generating AI recommend the most suitable picture books.

[0087] The reception unit can estimate the child's emotions and adjust the timing of information input based on the estimated emotions. For example, if the child is excited, the reception unit can delay the input timing to give the child time to calm down. The reception unit can also speed up the input timing to quickly collect information if the child is tired. The reception unit can also input detailed information at a time when the child is focused. By adjusting the timing of information input based on the child's emotions, information can be entered at a more appropriate time. 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 unit may be performed using AI or not. For example, the reception desk can input children's emotional data into a generating AI and have the AI ​​adjust the timing of information input.

[0088] The reception desk can analyze the parent's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the parent has used in the past. The reception desk can also automatically complete input fields based on information the parent has entered in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the parent's past input history. This allows the reception desk to suggest the optimal input method by analyzing the parent's past input 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 parent's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0089] The reception desk can customize input fields based on the child's current interests and concerns when information is entered. For example, if the child is interested in animals, the reception desk will prioritize displaying input fields related to animals. The reception desk can also add input fields related to a specific character if the child is interested in that character. The reception desk can also customize input fields related to a specific theme if the child is interested in that theme. This allows for the input of more appropriate information by customizing input fields based on the child's current interests and concerns. 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 interests and concerns into a generating AI and have the generating AI perform the customization of input fields.

[0090] The reception unit can estimate the child's emotions and determine the priority of the information to be input based on the estimated emotions. For example, if the child is excited, the reception unit will prioritize inputting important information. For example, if the child is excited, the reception unit will prioritize inputting important information. The reception unit can also prioritize inputting simple information if the child is tired. For example, if the child is tired, the reception unit will prioritize inputting simple information. The reception unit can also prioritize inputting detailed information if the child is focused. For example, if the child is focused, the reception unit will prioritize inputting detailed information. This allows for the input of more important information by determining the priority of the information to be input based on 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 the child's emotion data into a generative AI and have the generative AI determine the priority of the information.

[0091] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location when entering information. For example, if the child lives in a specific area, the reception desk can prioritize inputting information related to that area. For example, if the child lives in a specific area, the reception desk can prioritize inputting information related to that area. For example, if the child is interested in a specific place, the reception desk can prioritize inputting information related to that place. For example, if the child is traveling, the reception desk can prioritize inputting information related to the travel destination. For example, if the child is traveling, the reception desk can prioritize inputting information related to the travel destination. In this way, highly relevant information can be prioritized by considering the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's geographical location information into a generating AI and have the generating AI perform the input of highly relevant information.

[0092] The reception desk can analyze the parent's social media activity and input relevant information when information is entered. For example, the reception desk can suggest relevant input fields based on information the parent has shared on social media. The reception desk can also customize input fields based on information about accounts the parent follows on social media. The reception desk can also add input fields based on topics the parent has shown interest in on social media. This allows relevant information to be entered by analyzing the parent's 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 the parent's social media activity into a generating AI and have the generating AI input the relevant information.

[0093] The analysis unit can estimate the child's emotions and adjust the analysis method based on the estimated emotions. For example, if the child is excited, the analysis unit can apply a simple analysis method. For example, if the child is excited, the analysis unit can apply a simple analysis method. The analysis unit can also apply a rapid analysis method if the child is tired. For example, if the child is tired, the analysis unit can apply a detailed analysis method if the child is focused. For example, if the analysis unit is focused, the analysis unit can apply a detailed analysis method. By adjusting the analysis method based on the child's emotions, a more appropriate analysis can be performed. 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI adjust the analysis method.

[0094] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past data. The analysis unit can also analyze past data and adjust the parameters of the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm by referring to past data. In this way, the analysis algorithm can be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0095] The analysis unit can apply different analysis methods depending on the child's age and personality during the analysis. For example, the analysis unit can select an appropriate analysis method according to the child's age. The analysis unit can also customize the analysis method according to the child's personality. The analysis unit can also apply the optimal analysis method considering the child's age and personality. This allows for more appropriate analysis by applying analysis methods according to the child's age and personality. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of the analysis method.

[0096] The analysis unit can estimate a child's emotions and determine the priority of analysis based on the estimated emotions. For example, if the child is excited, the analysis unit will prioritize analyzing important information. For example, if the child is excited, the analysis unit will prioritize analyzing important information. The analysis unit can also prioritize analyzing simple information if the child is tired. For example, if the child is tired, the analysis unit will prioritize analyzing simple information. The analysis unit can also prioritize analyzing detailed information if the child is focused. For example, if the child is focused, the analysis unit will prioritize analyzing detailed information. By determining the priority of analysis based on the child's emotions, more important information can be prioritized. 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 may be performed using AI or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0097] The analysis unit can perform analysis while considering the child's geographical location information. For example, if the child lives in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the child lives in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the child is interested in a specific place, the analysis unit can prioritize analyzing information related to that place. For example, if the child is traveling, the analysis unit can prioritize analyzing information related to the travel destination. For example, if the child is traveling, the analysis unit can prioritize analyzing information related to the travel destination. In this way, by considering the child's geographical location information, highly relevant information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI. For example, the analysis unit can input the child's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0098] The analysis unit can improve the accuracy of its analysis by referring to the preferences and tendencies of other children during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the preferences of other children. The analysis unit can also analyze the tendencies of other children and adjust the analysis algorithm. For example, the analysis unit can analyze the tendencies of other children and adjust the analysis algorithm. The analysis unit can also optimize the accuracy of its analysis by referring to the data of other children. For example, the analysis unit can optimize the accuracy of its analysis by referring to the data of other children. This allows the accuracy of the analysis to be improved by referring to the preferences and tendencies of other children. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the preferences and tendencies of other children into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0099] The recommendation unit can estimate a child's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the child is excited, the recommendation unit can provide simple and easily understandable recommendations. For example, if the child is excited, the recommendation unit can provide simple recommendations. For example, if the child is tired, the recommendation unit can provide simple recommendations. For example, if the child is focused, the recommendation unit can provide detailed recommendations. For example, if the child is focused, the recommendation unit can provide detailed recommendations. This allows for more appropriate recommendations by adjusting the way recommendations are presented based on 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input child emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0100] The recommendation section can adjust the level of detail of its recommendations based on the importance of the picture book. For example, if the picture book is important, the recommendation section will provide detailed recommendation information. For example, if the picture book is important, the recommendation section will provide detailed recommendation information. The recommendation section can also provide simple recommendation information for general picture books. For example, if the picture book is general, the recommendation section will provide simple recommendation information. The recommendation section can also adjust the level of detail of its recommendations based on the child's preferences. For example, the recommendation section can adjust the level of detail of its recommendations based on the importance of the picture book. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input picture book importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of its recommendations.

[0101] The recommendation unit can apply different recommendation algorithms depending on the category of the picture book. For example, in the case of an educational picture book, the recommendation unit can apply a recommendation algorithm that prioritizes educational effectiveness. For example, in the case of an entertaining picture book, the recommendation unit can apply a recommendation algorithm that prioritizes enjoyment. For example, in the case of an entertaining picture book, the recommendation unit can apply a recommendation algorithm that prioritizes enjoyment. For example, in the case of a picture book based on a specific theme, the recommendation unit can apply a recommendation algorithm that is best suited to that theme. By applying different recommendation algorithms depending on the category of the picture book, more appropriate recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input picture book category data into a generating AI and have the generating AI perform the application of the recommendation algorithm.

[0102] The recommendation unit can estimate a child's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the child is excited, the recommendation unit can provide short, concise recommendations. For example, if the child is excited, the recommendation unit can provide short, concise recommendations. For example, if the child is tired, the recommendation unit can provide concise recommendations. For example, if the child is focused, the recommendation unit can provide detailed recommendations. For example, if the child is focused, the recommendation unit can provide detailed recommendations. By adjusting the length of recommendations based on the child's emotions, more appropriate recommendations can be made. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input child emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0103] The recommendation unit can determine the priority of recommendations based on the publication date of the picture books. For example, the recommendation unit will prioritize recommending newer picture books. The recommendation unit can also determine the priority of older picture books by comparing them with other picture books. The recommendation unit can also adjust the priority of recommendations based on children's preferences, regardless of publication date. This allows for more appropriate recommendations by determining the priority of recommendations based on the publication date of the picture books. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input data on the publication date of the picture books into a generating AI and have the generating AI determine the priority of recommendations.

[0104] The recommendation unit can adjust the order of recommendations based on the relevance of the picture books. For example, the recommendation unit can prioritize recommending picture books that are most relevant to the child's preferences. The recommendation unit can also prioritize recommending picture books that are most appropriate for the child's age. The recommendation unit can also prioritize recommending picture books that are most relevant to the child's interests. By adjusting the order of recommendations based on the relevance of the picture books, more appropriate recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input data on the relevance of the picture books into a generating AI and have the generating AI adjust the order of recommendations.

[0105] The generation unit can estimate a child's emotions and adjust the content of the generated story based on the estimated emotions. For example, if the child is excited, the generation unit can generate an action-packed story. For example, if the child is excited, the generation unit can generate an action-packed story. For example, if the child is tired, the generation unit can generate a relaxing story. For example, if the child is focused, the generation unit can generate a complex story. For example, if the child is focused, the generation unit can generate a complex story. By adjusting the story content based on the child's emotions, a more appropriate story can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input child emotion data into the generation AI and have the generation AI adjust the story content.

[0106] The generation unit can select a story theme by referring to the child's past preferences during generation. For example, the generation unit can select a story theme based on themes the child has liked in the past. The generation unit can also analyze the child's past preferences and select the most suitable theme. The generation unit can also customize the story theme by referring to the child's past preferences. For example, the generation unit can customize the story theme by referring to the child's past preferences. This allows for the selection of a more appropriate story theme by referring to the child's past preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the child's past preferences into a generation AI and have the generation AI perform the selection of a story theme.

[0107] The generation unit can apply different story generation methods depending on the child's age and personality during generation. For example, the generation unit can select an appropriate story generation method depending on the child's age. The generation unit can also customize the story generation method depending on the child's personality. The generation unit can also apply the optimal story generation method considering the child's age and personality. By applying story generation methods according to the child's age and personality, more appropriate stories are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the child's age and personality into a generation AI and have the generation AI perform the application of story generation methods.

[0108] The generation unit can estimate a child's emotions and adjust the personality of the character it generates based on the estimated emotions. For example, if the child is excited, the generation unit can generate an energetic character. For example, if the child is excited, the generation unit can generate an energetic character. The generation unit can also generate a calm character if the child is tired. For example, if the child is focused, the generation unit can generate a character with a complex personality. For example, if the child is focused, the generation unit can generate a character with a complex personality. By adjusting the character's personality based on the child's emotions, a more appropriate character can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input child emotion data into the generation AI and have the generation AI perform the adjustment of the character's personality.

[0109] The generation unit can set the setting of the story while considering the child's geographical location information. For example, the generation unit can generate a story set in the area where the child lives. The generation unit can also generate a story set in a place the child is interested in. The generation unit can also generate a story set in the travel destination if the child is traveling. This allows for the setting of a more appropriate story by considering the child's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the child's geographical location information into the generation AI and have the generation AI perform the setting of the story.

[0110] The generation unit can optimize the story content by referring to the preferences and tendencies of other children during generation. For example, the generation unit can optimize the story content based on the preferences of other children. The generation unit can also analyze the tendencies of other children and adjust the story content. The generation unit can also optimize the story content by referring to data on other children. This allows for the generation of more appropriate story content by referring to the preferences and tendencies of other children. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the preferences and tendencies of other children into a generation AI and have the generation AI perform the optimization of the story content.

[0111] The narration unit can estimate the child's emotions and adjust the tone of the narration based on the estimated emotions. For example, if the child is excited, the narration unit will narrate in a calm tone. For example, if the child is excited, the narration unit will narrate in a calm tone. For example, if the child is tired, the narration unit will narrate in an energetic tone. For example, if the child is tired, the narration unit will narrate in an energetic tone. For example, if the child is focused, the narration unit will narrate in a tone that includes detailed explanations. For example, if the child is focused, the narration unit will narrate in a tone that includes detailed explanations. In this way, more appropriate narration can be provided by adjusting the tone of the narration based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the narration unit may be performed using AI or not using AI. For example, the narration unit can input child emotional data into a generating AI, which can then adjust the tone of the narration.

[0112] The narration unit can select the optimal narration method by referring to the child's past responses during narration. For example, the narration unit can select the optimal narration method based on the child's past preferred narration methods. The narration unit can also analyze the child's past responses and adjust the narration method. For example, the narration unit can analyze the child's past responses and adjust the narration method. The narration unit can also optimize the narration method by referring to the child's past responses. For example, the narration unit can optimize the narration method by referring to the child's past responses. This allows for the selection of a more appropriate narration method by referring to the child's past responses. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input data on the child's past responses into a generating AI and have the generating AI select the narration method.

[0113] The narration unit can apply different narration techniques depending on the content of the picture book during narration. For example, in the case of an educational picture book, the narration unit can apply narration techniques that prioritize educational effectiveness. For example, in the case of an educational picture book, the narration unit can apply narration techniques that prioritize educational effectiveness. The narration unit can also apply narration techniques that prioritize enjoyment in the case of an entertaining picture book. For example, in the case of an entertaining picture book, the narration unit can apply narration techniques that prioritize enjoyment. The narration unit can also apply narration techniques that are best suited to a specific theme in the case of a picture book based on a particular theme. For example, in the case of a picture book based on a specific theme, the narration unit can apply narration techniques that are best suited to that theme. By applying different narration techniques depending on the content of the picture book, more appropriate narration can be achieved. Some or all of the above processing in the narration unit may be performed using AI, or it may be performed without using AI. For example, the narration unit can input data on the content of the picture book into a generating AI and have the generating AI perform the application of narration techniques.

[0114] The narration unit can estimate the child's emotions and adjust the narration speed based on the estimated emotions. For example, if the child is excited, the narration unit can narrate at a slower pace. For example, if the child is excited, the narration unit can narrate at a slower pace. The narration unit can also narrate at a faster pace if the child is tired. For example, if the child is tired, the narration unit can narrate at a faster pace. The narration unit can also narrate at an appropriate pace if the child is focused. For example, if the child is focused, the narration unit can narrate at an appropriate pace. By adjusting the narration speed based on the child's emotions, more appropriate narration can be provided. 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 narration unit may be performed using AI or not. For example, the narration unit can input child emotional data into a generating AI and have the AI ​​adjust the narration speed.

[0115] The narration unit can select the most appropriate narration method when narrating, taking into account the child's geographical location. For example, if the child lives in a specific area, the narration unit can select a narration method related to that area. For example, if the child lives in a specific area, the narration unit can select a narration method related to that area. For example, if the child is interested in a specific place, the narration unit can select a narration method related to that place. For example, if the child is traveling, the narration unit can select a narration method related to the travel destination. For example, if the child is traveling, the narration unit can select a narration method related to the travel destination. This allows for the selection of a more appropriate narration method by considering the child's geographical location. Some or all of the above processing in the narration unit may be performed using AI, or not. For example, the narration unit can input the child's geographical location information into a generating AI and have the generating AI select the narration method.

[0116] The narration unit can optimize the content of the narration by referring to the reactions of other children during narration. For example, the narration unit can optimize the content of the narration based on the reactions of other children. The narration unit can also analyze the reactions of other children and adjust the content of the narration. For example, the narration unit can analyze the reactions of other children and adjust the content of the narration. The narration unit can also optimize the content of the narration by referring to data of other children. For example, the narration unit can optimize the content of the narration by referring to data of other children. This allows for the provision of more appropriate narration content by referring to the reactions of other children. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input data on the reactions of other children into a generating AI and have the generating AI perform the optimization of the narration content.

[0117] The notification unit can estimate the child's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the child is excited, the notification unit can delay the notification to give the child time to calm down. The notification unit can also advance the notification to send a notification quickly if the child is tired. The notification unit can also send a notification at a time when the child is concentrating. By adjusting the timing of notifications based on the child's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 notification unit may be performed using AI or not. For example, the notification unit can input the child's emotional data into a generating AI and have the AI ​​adjust the timing of notifications.

[0118] The notification unit can select the optimal notification method by referring to the child's past responses when sending a notification. For example, the notification unit can select the optimal notification method based on the notification method the child has preferred in the past. The notification unit can also analyze the child's past responses and adjust the notification method. For example, the notification unit can analyze the child's past responses and adjust the notification method. The notification unit can also optimize the notification method by referring to the child's past responses. For example, the notification unit can optimize the notification method by referring to the child's past responses. This allows for the selection of a more appropriate notification method by referring to the child's past responses. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the child's past responses into a generating AI and have the generating AI select the notification method.

[0119] The notification unit can apply different notification methods depending on the child's age and personality when sending a notification. For example, the notification unit can select an appropriate notification method depending on the child's age. The notification unit can also customize the notification method depending on the child's personality. The notification unit can also apply the optimal notification method considering the child's age and personality. This allows for more appropriate notifications by applying notification methods according to the child's age and personality. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of notification methods.

[0120] The notification unit can estimate the child's emotions and adjust the content of the notification based on the estimated emotions. For example, if the child is excited, the notification unit can provide a concise notification. For example, if the child is excited, the notification unit can provide a concise notification. For example, if the child is tired, the notification unit can provide an encouraging notification. For example, if the child is focused, the notification unit can provide a detailed notification. For example, if the child is focused, the notification unit can provide a detailed notification. By adjusting the content of the notification based on the child's emotions, more appropriate notifications can be provided. 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input the child's emotion data into the generative AI and have the generative AI adjust the content of the notification.

[0121] The notification unit can select the most appropriate notification method when sending a notification, taking into account the child's geographical location. For example, if the child lives in a specific area, the notification unit can select a notification method related to that area. For example, if the child lives in a specific area, the notification unit can select a notification method related to that area. For example, if the child is interested in a specific place, the notification unit can select a notification method related to that place. For example, if the child is traveling, the notification unit can select a notification method related to the travel destination. For example, if the child is traveling, the notification unit can select a notification method related to the travel destination. This allows for the selection of a more appropriate notification method by considering the child's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input the child's geographical location into a generating AI and have the generating AI select the notification method.

[0122] The notification unit can optimize the content of a notification by referring to the reactions of other children. For example, the notification unit can optimize the content of a notification based on the reactions of other children. The notification unit can also analyze the reactions of other children and adjust the content of the notification. The notification unit can also optimize the content of a notification by referring to data on other children. This allows for the provision of more appropriate notification content by referring to the reactions of other children. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the reactions of other children into a generating AI and have the generating AI perform the optimization of the notification content.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The reception unit can estimate the child's emotions and adjust the timing of information input based on the estimated emotions. For example, if the child is excited, the input timing can be delayed to give time for them to calm down. If the child is tired, the input timing can be advanced to quickly gather information. Furthermore, if the child is focused, detailed information can be entered at that time. In this way, by adjusting the timing of information input based on the child's emotions, information can be entered at a more appropriate time. 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 the child's emotion data into the generative AI and have the generative AI adjust the timing of information input.

[0125] The reception desk can analyze the parent's past input history and suggest the optimal input method. For example, it can prioritize suggesting input methods the parent has used in the past (voice, text, etc.). It can also automatically complete input fields based on information the parent has previously entered. Furthermore, it can predict and suggest input methods to be used at specific times based on the parent's past input history. In this way, the optimal input method can be suggested by analyzing the parent's past input 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 parent's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0126] The reception desk can customize input fields based on the child's current interests when they enter information. For example, if the child is interested in animals, input fields related to animals will be displayed preferentially. If the child is interested in a particular character, input fields related to that character can be added. Furthermore, if the child is interested in a particular theme, input fields related to that theme can be customized. This allows for more appropriate information to be entered by customizing input fields based on the child's current 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 interests into a generating AI and have the generating AI perform the customization of input fields.

[0127] 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, important information will be prioritized. If the child is tired, simple information may be prioritized. Furthermore, if the child is focused, detailed information may be prioritized. This allows for the prioritization of more important information based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI or not. For example, the reception unit can input the child's emotion data into a generative AI and have the generative AI determine the priority of the information.

[0128] The reception desk can prioritize inputting highly relevant information by considering the child's geographical location when entering information. For example, if the child lives in a specific area, information related to that area can be prioritized. Similarly, if the child is interested in a particular place, information related to that place can be prioritized. Furthermore, if the child is traveling, information related to the travel destination can be prioritized. This allows for the prioritization of highly relevant information by considering the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the child's geographical location into a generating AI and have the generating AI input highly relevant information.

[0129] The analysis unit can estimate the child's emotions and adjust the analysis method based on the estimated emotions. For example, if the child is excited, a simple analysis method can be applied. If the child is tired, a rapid analysis method can be applied. Furthermore, if the child is focused, a detailed analysis method can be applied. By adjusting the analysis method based on the child's emotions, a more appropriate analysis can be performed. 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 may be performed using AI or not. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI adjust the analysis method.

[0130] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, it can select the optimal analysis algorithm based on past data. It can also analyze past data and adjust the parameters of the analysis algorithm. Furthermore, it can improve the accuracy of the analysis algorithm by referring to past data. In this way, the analysis algorithm can be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0131] The analysis unit can apply different analysis methods depending on the child's age and personality during the analysis. For example, it can select an appropriate analysis method according to the child's age. It can also customize the analysis method according to the child's personality. Furthermore, it can apply the optimal analysis method considering both the child's age and personality. This allows for more appropriate analysis by applying analysis methods according to the child's age and personality. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's age and personality into a generating AI and have the generating AI execute the application of the analysis method.

[0132] The analysis unit can estimate the child's emotions and determine the priority of analysis based on the estimated emotions. For example, if the child is excited, important information can be prioritized for analysis. If the child is tired, simple information can be prioritized for analysis. Furthermore, if the child is focused, detailed information can be prioritized for analysis. In this way, by determining the priority of analysis based on the child's emotions, more important information can be prioritized for analysis. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0133] The analysis unit can perform analysis while considering the child's geographical location. For example, if the child lives in a specific area, it can prioritize the analysis of information related to that area. Similarly, if the child is interested in a particular place, it can prioritize the analysis of information related to that place. Furthermore, if the child is traveling, it can prioritize the analysis of information related to the travel destination. This allows for the prioritization of highly relevant information by considering the child's geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the child's geographical location into a generating AI and have the generating AI perform the analysis of highly relevant information.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The reception desk enters the child's information. This information includes, for example, age, gender, interests, and past reading history. Parents only need to enter their child's basic information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using, for example, data mining or machine learning algorithms. Based on past data and the preferences of other children, the most suitable picture book is selected. Step 3: The recommendation unit recommends picture books based on the information analyzed by the analysis unit. Recommendations are made using, for example, similarity calculations or ranking algorithms. Picture books are recommended based on information such as the child's age, personality, and existing preferences. Step 4: The generation unit generates the picture book content recommended by the recommendation unit. Generation is performed using, for example, a story generation algorithm or a character generation algorithm. New stories and characters are created based on themes that children like. Step 5: The narration unit reads aloud the content of the picture book generated by the generation unit. The narration is performed using, for example, speech synthesis technology or text-to-speech technology. Children can enjoy the picture book even when their parents are busy. Step 6: The notification unit sends a notification to the child at the designated time to remind them it's reading time. The notification is sent, for example, using a timer function. When the time set by the parent arrives, a notification is sent to the child reminding them it's reading time.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, generation unit, narration unit, and notification 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 control unit 46A of the smart device 14, where the parent inputs the child's basic information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends picture books based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the content of the recommended picture book. The narration unit is implemented by the control unit 46A of the smart device 14, which reads aloud the generated content of the picture book. The notification unit is implemented by the control unit 46A of the smart device 14, which sends a notification to the child at a designated time to inform them of reading time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, generation unit, narration unit, and notification 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 control unit 46A of the smart glasses 214, where the parent inputs the child's basic information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends picture books based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the content of the recommended picture book. The narration unit is implemented by the control unit 46A of the smart glasses 214, which reads aloud the generated content of the picture book. The notification unit is implemented by the control unit 46A of the smart glasses 214, which sends a notification to the child at a designated time to inform them of reading time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, generation unit, narration unit, and notification 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 control unit 46A of the headset terminal 314, where the parent inputs the child's basic information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends picture books based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the content of the recommended picture book. The narration unit is implemented by the control unit 46A of the headset terminal 314, which reads aloud the generated content of the picture book. The notification unit is implemented by the control unit 46A of the headset terminal 314, which sends a notification to the child at a designated time to inform them of reading time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the reception unit, analysis unit, recommendation unit, generation unit, narration unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the parent inputs the child's basic information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends picture books based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates the content of the recommended picture book. The narration unit is implemented by the control unit 46A of the robot 414, which reads aloud the generated content of the picture book. The notification unit is implemented by the control unit 46A of the robot 414, which sends a notification to the child at a designated time to inform them of reading time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) The reception area where you enter the child's information, An analysis unit analyzes the information input by the reception unit, A recommendation unit recommends picture books based on the information analyzed by the aforementioned analysis unit, A generation unit that generates the content of the picture book recommended by the recommendation unit, A narration unit reads aloud the contents of the picture book generated by the generation unit, It includes a notification unit that sends a notification to the child at a designated time to inform them of reading time. A system characterized by the following features. (Note 2) The aforementioned narration section is, The contents of the picture book will be read aloud using speech synthesis technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, The timer function sends a notification to the child at a specified time to remind them it's reading time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Select the best picture book based on past data and other children's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Create new stories and characters based on themes that children like. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation section is, We recommend picture books based on information such as the child's age, personality, and existing preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the child's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze parents' past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, the input fields are customized based on the child's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) The aforementioned reception unit is When entering information, the system analyzes the parents' social media activity and enters relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the child's emotions and adjust the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the child's age and personality. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the child's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the preferences and tendencies of other children. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation section is, It estimates the child's emotions and adjusts recommended expressions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation section is, When making recommendations, adjust the level of detail based on the importance of the picture book. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation section is, When making recommendations, different recommendation algorithms are applied depending on the category of the picture book. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation section is, It estimates the child's emotions and adjusts the recommended length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation section is, When making recommendations, we determine the priority of recommendations based on the publication date of the picture book. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation section is, When making recommendations, the order of recommendations is adjusted based on the relevance of the picture books. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the child's emotions and adjusts the content of the generated story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the story's theme is selected by referencing the child's past preferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, different story generation methods are applied depending on the child's age and personality. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the child's emotions and adjusts the generated character's personality based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating the story, the setting is determined by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, the story content is optimized by referencing the preferences and tendencies of other children. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned narration section is, The system estimates the child's emotions and adjusts the tone of the narration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned narration section is, When narrating, the most suitable narration method is selected by referring to the child's past reactions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned narration section is, When narrating, different narration techniques are applied depending on the content of the picture book. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned narration section is, The system estimates the child's emotions and adjusts the narration speed based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned narration section is, When narrating, the most suitable narration method is selected, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned narration section is, During narration, the content of the narration is optimized by referring to the reactions of other children. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, It estimates the child's emotions and adjusts the timing of notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned notification unit, When sending a notification, the system will refer to the child's past responses to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned notification unit, When sending notifications, different notification methods will be applied depending on the child's age and personality. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned notification unit, The system estimates the child's emotions and adjusts the content of the notification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned notification unit, When sending a notification, the system will select the most appropriate notification method, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned notification unit, When sending notifications, the content of the notifications is optimized by referring to the reactions of other children. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 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 recommendation unit recommends picture books based on the information analyzed by the aforementioned analysis unit, A generation unit that generates the content of the picture book recommended by the recommendation unit, A narration unit reads aloud the contents of the picture book generated by the generation unit, It includes a notification unit that sends a notification to the child at a designated time to inform them of reading time. A system characterized by the following features.

2. The aforementioned narration section is, The contents of the picture book will be read aloud using speech synthesis technology. The system according to feature 1.

3. The aforementioned notification unit, The timer function sends a notification to the child at a specified time to remind them it's reading time. The system according to feature 1.

4. The aforementioned analysis unit, Select the best picture book based on past data and other children's preferences. The system according to feature 1.

5. The generating unit is Create new stories and characters based on themes that children like. The system according to feature 1.

6. The aforementioned recommendation section is, We recommend picture books based on information such as the child's age, personality, and existing preferences. The system according to feature 1.

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

8. The aforementioned reception unit is We analyze parents' past input history and suggest the optimal input method. The system according to feature 1.

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

10. 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.

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