System

Abstract

Depending on embodiments, a system may efficiently recommend a book suitable for an emotion of a user.SOLUTION: A specific processing part 290 of a data processor 12 in the system executes user information analysis processing for analyzing user information, user information analysis processing for recommending an optimum book on the basis of the user information analyzed by the user information analysis processing, database management processing for managing a database of books, and user interface processing for providing the user with the book recommended by the recommendation processing.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently find books that match the user's sensibilities.

[0005] The system according to the embodiment aims to efficiently recommend books that match the user's sensibilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information analysis unit, a recommendation unit, a database management unit, and a user interface unit. The user information analysis unit analyzes user information. The recommendation unit recommends optimal books based on the user information analyzed by the user information analysis unit. The database management unit manages a book database. The user interface unit provides the user with books recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently recommend books that match the user's sensibilities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recommendation system according to the embodiment of the present invention is a system that allows users to find books that suit their sensibilities. In this system, a generation AI analyzes the user's preferences and interests and recommends the most suitable books based on the analysis. This allows the recommendation system to recommend the most suitable books based on the user's preferences and interests.

[0029] The recommendation system according to the embodiment includes a user information analysis unit, a recommendation unit, a database management unit, and a user interface unit. The user information analysis unit analyzes user information. For example, the user inputs information such as a list of books the user has read in the past, ratings, favorite genres and authors, and topics of interest. The user information analysis unit also analyzes the user's reading habits using a generation AI. The generation AI performs analysis based on prompts containing instructions on what the user wants the generation AI to do. The recommendation unit recommends optimal books based on the user information analyzed by the user information analysis unit. For example, if a user likes mystery novels and works by a specific author, the recommendation unit recommends other works by that author and highly rated books in the same genre. It also recommends books related to the user's topics of interest. The database management unit manages a book database. For example, the database management unit builds and manages a massive book database. This database includes information such as the title, author, genre, synopsis, rating, and reviews of each book. The user interface unit provides the user with books recommended by the recommendation unit. For example, the user interface unit may provide a form for the user to input their preferences and interests, and a screen for displaying a list of recommended books. The user interface unit may also provide a function for the user to rate and review the recommended books. This allows the recommendation system according to the embodiment to recommend optimal books based on the user's preferences and interests.

[0030] The user information analysis unit analyzes the content of a user's posts on social media and blogs to understand their interests and concerns in more detail. The user information analysis unit, for example, analyzes the content of a user's posts on social media and extracts their interests and concerns. For example, it analyzes the content of posts on Twitter and Facebook to understand the topics that the user has recently been interested in. The user information analysis unit also analyzes the user's blog posts to understand their interests and concerns in more detail. For example, it extracts keywords and phrases from blog posts to identify the user's interests. The user information analysis unit also analyzes the frequency of posts and responses to the user's social media and blogs to understand changes in their interests and concerns. For example, if there is an increase in posts on a particular topic, it can recommend books related to that topic. In this way, by analyzing the content of a user's posts on social media and blogs, it is possible to understand their interests and concerns in more detail.

[0031] To analyze a user's visual preferences, the user information analysis unit can capture the user's facial expressions and eye movements while reading with a camera and analyze them with a generation AI. For example, the user information analysis unit can capture the user's facial expressions while reading with a camera and analyze them with a generation AI. For example, it can analyze smiling and surprised expressions while reading to understand the user's preferences. The user information analysis unit can also capture the user's eye movements and analyze them with a generation AI. For example, it can identify areas where the gaze lingers for a long time and identify parts of interest. The user information analysis unit can also analyze the user's facial expressions and eye movements in real time while reading to identify the user's preferences. For example, it can recommend similar books based on books that frequently show specific facial expressions and eye movements. This allows the user's visual preferences to be analyzed and more suitable books to be recommended.

[0032] The user information analysis unit analyzes a user's music playlists and movie viewing history to understand their overall entertainment preferences. For example, the user information analysis unit analyzes a user's music playlists to identify their favorite genres and artists. For example, a user who likes rock music can be recommended books related to rock music. The user information analysis unit also analyzes a user's movie viewing history to identify their favorite genres and themes. For example, a user who likes science fiction movies can be recommended science fiction novels. The user information analysis unit also comprehensively analyzes the music playlists and movie viewing history to understand their entertainment preferences. For example, related books can be recommended based on similarities between music and movies. This allows the system to understand the user's overall entertainment preferences and recommend more suitable books.

[0033] The database management unit can summarize the contents of each book using generative AI and add the summaries to the book database. For example, the database management unit can automatically summarize the contents of each book using generative AI and add the summaries to the database. For example, it can summarize the main points of a full-length novel into a short summary. The database management unit can also use generative AI to create summaries for each book and add them to the database. For example, it can summarize the main points of a non-fiction book. The database management unit can also use generative AI to build a system that summarizes the contents of each book and adds the summaries to the database. For example, it can search and recommend books based on the summaries. This allows for efficient searching and recommendations by summarizing the contents of each book and adding them to the database.

[0034] The database management unit can also integrate information about audiobooks and e-books into the book database, enabling multi-format recommendations. For example, the database management unit adds audiobook information to the book database and makes multi-format recommendations to the user. For example, an audiobook and an e-book of the same book can be recommended simultaneously. The database management unit also integrates e-book information into the book database and makes multi-format recommendations to the user. For example, both e-books and paper books can be recommended. The database management unit also integrates information about audiobooks and e-books into the book database, building a system that makes multi-format recommendations to the user. For example, the format can be selected according to the user's preferences. In this way, multi-format recommendations can be made by integrating information about audiobooks and e-books.

[0035] The database management unit also adds information about movies and dramas related to each book to the book database, allowing for cross-media recommendations. The database management unit, for example, adds information about movies and dramas related to each book to the book database and makes cross-media recommendations. For example, it can simultaneously recommend the original book and its film adaptation. The database management unit also adds information about movies and dramas related to each book to the database, building a system that makes cross-media recommendations to users. For example, it can recommend books and movies on the same theme. The database management unit also integrates information about movies and dramas into the book database and makes cross-media recommendations to users. For example, it can recommend books that have been made into movies or dramas. This makes it possible to make cross-media recommendations by adding information about related movies and dramas.

[0036] The recommendation unit can analyze the user's reading speed and reading time, and recommend books that match the user's optimal reading pace. The recommendation unit, for example, analyzes the user's reading speed and recommends books that match the user's optimal reading pace. For example, it can recommend full-length novels to users who are good at speed reading, and short stories to users who read slowly. The recommendation unit also analyzes the user's reading time and builds a system that recommends books that match the user's optimal reading pace. For example, it can recommend books that can be read in a short time to users who have limited reading time. The recommendation unit also recommends books that match the user's optimal reading pace based on the reading speed and reading time. For example, it can recommend long books to users who read quickly, and short stories and essays to users who read quickly. In this way, by analyzing the user's reading speed and reading time, it is possible to recommend books that match the user's optimal reading pace.

[0037] The recommendation unit can analyze the reading history of the user's friends and family and recommend books that share common interests. For example, the recommendation unit can analyze the reading history of the user's friends and family and recommend books that share common interests. For example, it can recommend books that the whole family can enjoy. The recommendation unit can also build a system that recommends books that share common interests based on the reading history of friends and family. For example, it can recommend books that friends have given high ratings. The recommendation unit can also analyze the reading history of the user's friends and family and recommend books that share common interests. For example, it can recommend books in the same genre or theme. In this way, by analyzing the reading history of the user's friends and family, it can recommend books that share common interests.

[0038] The recommendation unit can analyze the user's geographical location information and recommend books available at local bookstores and libraries. For example, the recommendation unit analyzes the user's geographical location information and recommends books available at local bookstores and libraries. For example, it recommends books that can be purchased at nearby bookstores. The recommendation unit also builds a system that recommends books available at local bookstores and libraries based on the geographical location information. For example, it recommends books based on library collection information. The recommendation unit also analyzes the user's geographical location information and recommends books available at local bookstores and libraries. For example, it recommends books related to local events and fairs. In this way, by analyzing the user's geographical location information, it is possible to recommend books available at local bookstores and libraries.

[0039] The user interface unit visually displays the user's reading history and ratings, and can provide an intuitively understandable dashboard. The user interface unit, for example, provides a dashboard visually displays the user's reading history and ratings. For example, it displays a list of books read and ratings in graphs and charts. The user interface unit also builds a system that provides an intuitively understandable dashboard based on the reading history and ratings. For example, it visually displays reading trends. The user interface unit also visually displays the user's reading history and ratings, and provides an intuitively understandable dashboard. For example, it highlights highly rated books and genres. In this way, by visually displaying the user's reading history and ratings, it is possible to provide an intuitively understandable dashboard.

[0040] The user interface unit can analyze the user's voice input and enable book search and recommendation by voice. The user interface unit, for example, analyzes the user's voice input and builds a system that enables book search and recommendation by voice. For example, it uses voice recognition technology to search for book titles and author names. The user interface unit also recommends the most suitable books to the user based on the voice input. For example, it can recommend books by specifying a favorite genre or theme by voice. The user interface unit also analyzes the user's voice input and enables book search and recommendation by voice. For example, it can display a list of books in response to a voice command. This makes it possible to analyze the user's voice input and enable book search and recommendation by voice.

[0041] The user interface unit can analyze the user's gesture input and enable touchless operation. The user interface unit, for example, analyzes the user's gesture input and builds a system that enables touchless operation. For example, a page can be turned with a hand movement. The user interface unit also allows the user to search for books and receive recommendations without touching them based on the gesture input. For example, a list of books can be scrolled with a finger movement. The user interface unit also analyzes the user's gesture input and enables touchless operation. For example, detailed information about a book can be displayed with a hand movement. In this way, the user's gesture input can be analyzed and touchless operation can be enabled.

[0042] The user feedback analysis unit can summarize the content of the feedback using a generation AI and improve the algorithm based on the summary. The user feedback analysis unit, for example, summarizes the content of the user's feedback using a generation AI and improves the algorithm based on the summary. For example, it extracts the main points of the feedback and reflects them in the algorithm. The user feedback analysis unit can also summarize the content of the feedback using a generation AI and build a system that improves the algorithm based on the summary. For example, it can adjust the parameters of the algorithm based on the summary. The user feedback analysis unit can also summarize the content of the user's feedback using a generation AI and improve the algorithm based on the summary. For example, it can improve the accuracy of the algorithm based on the summary. In this way, the content of the feedback can be summarized and the algorithm can be improved.

[0043] The user feedback analysis unit can share the user's feedback with other users and collect community-based improvement suggestions. The user feedback analysis unit, for example, builds a system for sharing the user's feedback with other users and collecting community-based improvement suggestions. For example, a feedback forum is set up to collect opinions. The user feedback analysis unit also shares feedback and collects improvement suggestions from other users. For example, a platform is provided where users can exchange opinions with each other. The user feedback analysis unit also shares the user's feedback with other users and collects community-based improvement suggestions. For example, discussions are held based on the feedback to identify areas for improvement. This makes it possible to share the user's feedback with other users and collect community-based improvement suggestions.

[0044] The user's feedback analysis unit can visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. The user's feedback analysis unit, for example, builds a system that visualizes the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it displays the main points of the feedback in a graph or chart. The user's feedback analysis unit can also visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it can display the content of the feedback in a mind map format. The user's feedback analysis unit can also visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it can display the content of the feedback in an infographic. In this way, the content of the feedback can be visualized, allowing the user to intuitively understand the areas for improvement.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The user information analysis unit can analyze the user's reading environment and provide an optimal reading experience. For example, it can use sensors to detect the user's reading environment (lighting, volume, seating comfort, etc.) and suggest the optimal environment. The user information analysis unit can also suggest a time of day that is suitable for reading based on the user's reading environment. For example, it can recommend a quiet time of day or a time of day when you can relax. The user information analysis unit can also analyze the user's reading environment and suggest a place that is suitable for reading. For example, it can recommend a quiet place such as a cafe or library. This can optimize the user's reading environment and provide a better reading experience.

[0047] The user information analysis unit can analyze the user's health condition and suggest health-conscious reading habits. For example, it can use sensors to detect the user's posture and eye fatigue and suggest appropriate breaks. The user information analysis unit can also suggest stretches and exercises to do between reading sessions based on the user's health condition. For example, it can recommend simple exercises to reduce fatigue caused by long periods of reading. The user information analysis unit can also analyze the user's health condition and suggest health-conscious reading times. For example, it can set an appropriate reading time to prevent eye fatigue. This makes it possible to suggest reading habits that take the user's health condition into consideration and provide a healthy reading experience.

[0048] The user information analysis unit can analyze the user's learning style and suggest the optimal learning method. For example, it can analyze the user's learning style (visual, auditory, tactile, etc.) and suggest the optimal learning method. The user information analysis unit can also suggest learning materials suitable for learning based on the user's learning style. For example, it can recommend books with many diagrams and illustrations to a user who is good at visual learning. The user information analysis unit can also analyze the user's learning style and suggest the optimal time of day for studying. For example, it can recommend the time of day when concentration is highest. This makes it possible to suggest the optimal learning method that matches the user's learning style and provide an effective learning experience.

[0049] The user information analysis unit can analyze the user's reading history and visualize the user's reading progress. For example, it can analyze the number of pages of books read by the user and the reading time, and display the progress in graphs and charts. The user information analysis unit can also set reading goals based on the user's reading progress. For example, it can set monthly or yearly reading goals and visualize the progress of achievement. The user information analysis unit can also analyze the user's reading progress and provide feedback to improve reading motivation. For example, it can display rewards or encouraging messages for achieved goals. This makes it possible to visualize the user's reading progress and improve reading motivation.

[0050] The user information analysis unit can analyze a user's reading history and visualize their reading trends. For example, it can analyze the genres and themes of books read by the user and display the reading trends in graphs and charts. The user information analysis unit can also suggest books in new genres or themes based on the user's reading trends. For example, if there is a bias toward a particular genre, it can recommend books in a different genre. The user information analysis unit can also analyze reading trends and provide feedback to improve the diversity of reading. For example, it can explain the benefits of reading books in different genres and themes. This makes it possible to visualize the user's reading trends and improve the diversity of reading.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The user information analysis unit analyzes user information. For example, the user inputs information such as a list of books they have read in the past, their ratings, favorite genres and authors, and topics of interest. The generation AI also analyzes the user's reading habits. The generation AI performs its analysis based on prompts, which include instructions on what the user wants the generation AI to do. Step 2: The recommendation unit recommends the most suitable book based on the user information analyzed by the user information analysis unit. For example, if a user likes mystery novels and prefers the works of a particular author, the recommendation unit will recommend other works by that author and highly rated books in the same genre. It will also recommend books related to the user's interests. Step 3: The database management department manages the book database. For example, it builds and manages a huge book database. This database contains information such as the title, author, genre, synopsis, rating, and reviews of each book. Step 4: The user interface unit provides the user with the books recommended by the recommendation unit. For example, it provides a form for the user to input their preferences and interests, and a screen that displays a list of recommended books. It also provides a function that allows the user to rate and review the recommended books.

[0053] (Example 2) The recommendation system according to the embodiment of the present invention is a system that allows users to find books that suit their sensibilities. In this system, a generation AI analyzes the user's preferences and interests and recommends the most suitable books based on the analysis. This allows the recommendation system to recommend the most suitable books based on the user's preferences and interests.

[0054] The recommendation system according to the embodiment includes a user information analysis unit, a recommendation unit, a database management unit, and a user interface unit. The user information analysis unit analyzes user information. For example, the user inputs information such as a list of books the user has read in the past, ratings, favorite genres and authors, and topics of interest. The user information analysis unit also analyzes the user's reading habits using a generation AI. The generation AI performs analysis based on prompts containing instructions on what the user wants the generation AI to do. The recommendation unit recommends optimal books based on the user information analyzed by the user information analysis unit. For example, if a user likes mystery novels and works by a specific author, the recommendation unit recommends other works by that author and highly rated books in the same genre. It also recommends books related to the user's topics of interest. The database management unit manages a book database. For example, the database management unit builds and manages a massive book database. This database includes information such as the title, author, genre, synopsis, rating, and reviews of each book. The user interface unit provides the user with books recommended by the recommendation unit. For example, the user interface unit may provide a form for the user to input their preferences and interests, and a screen for displaying a list of recommended books. The user interface unit may also provide a function for the user to rate and review the recommended books. This allows the recommendation system according to the embodiment to recommend optimal books based on the user's preferences and interests.

[0055] The user information analysis unit analyzes the user's emotional state in real time and can recommend books that match their mood at that time. The user information analysis unit, for example, analyzes the user's facial expressions and voice to grasp their emotional state in real time. For example, it analyzes the user's emotions using a camera or microphone and recommends books that will help them relax when they want to relax. The user information analysis unit also analyzes the user's input content and operation history to estimate their emotional state at that time. For example, it recommends books that will help them relieve stress when they are feeling stressed. The user information analysis unit also analyzes the user's biometric information (heart rate, electrodermal activity, etc.) to grasp their emotional state in real time. For example, it recommends books that will help them relax when their heart rate is high. This makes it possible to recommend books that match the user's emotional state.

[0056] The user information analysis unit analyzes the content of a user's posts on social media and blogs to understand their interests and concerns in more detail. The user information analysis unit, for example, analyzes the content of a user's posts on social media and extracts their interests and concerns. For example, it analyzes the content of posts on Twitter and Facebook to understand the topics that the user has recently been interested in. The user information analysis unit also analyzes the user's blog posts to understand their interests and concerns in more detail. For example, it extracts keywords and phrases from blog posts to identify the user's interests. The user information analysis unit also analyzes the frequency of posts and responses to the user's social media and blogs to understand changes in their interests and concerns. For example, if there is an increase in posts on a particular topic, it can recommend books related to that topic. In this way, by analyzing the content of a user's posts on social media and blogs, it is possible to understand their interests and concerns in more detail.

[0057] The user information analysis unit uses the emotion estimation function to analyze the user's emotions regarding books they have read in the past and recommend books that evoke positive emotions. The user information analysis unit, for example, analyzes reviews and ratings of books the user has read in the past and calculates an emotion score. For example, it recommends similar books based on books with high positive emotion scores. The user information analysis unit also analyzes the user's emotional responses to books they have read in the past and identifies books that evoke positive emotions. For example, it recommends books based on the genre or theme of books with high emotion scores. The user information analysis unit also uses the emotion estimation function to analyze the user's emotions regarding books they have read in the past in real time and recommends books that evoke positive emotions. For example, it adjusts the recommendation content according to changes in the user's emotions. This makes it possible to analyze the user's emotions regarding books they have read in the past and recommend books that evoke positive emotions.

[0058] To analyze a user's visual preferences, the user information analysis unit can capture the user's facial expressions and eye movements while reading with a camera and analyze them with a generation AI. For example, the user information analysis unit can capture the user's facial expressions while reading with a camera and analyze them with a generation AI. For example, it can analyze smiling and surprised expressions while reading to understand the user's preferences. The user information analysis unit can also capture the user's eye movements and analyze them with a generation AI. For example, it can identify areas where the gaze lingers for a long time and identify parts of interest. The user information analysis unit can also analyze the user's facial expressions and eye movements in real time while reading to identify the user's preferences. For example, it can recommend similar books based on books that frequently show specific facial expressions and eye movements. This allows the user's visual preferences to be analyzed and more suitable books to be recommended.

[0059] The user information analysis unit analyzes a user's music playlists and movie viewing history to understand their overall entertainment preferences. For example, the user information analysis unit analyzes a user's music playlists to identify their favorite genres and artists. For example, a user who likes rock music can be recommended books related to rock music. The user information analysis unit also analyzes a user's movie viewing history to identify their favorite genres and themes. For example, a user who likes science fiction movies can be recommended science fiction novels. The user information analysis unit also comprehensively analyzes the music playlists and movie viewing history to understand their entertainment preferences. For example, related books can be recommended based on similarities between music and movies. This allows the system to understand the user's overall entertainment preferences and recommend more suitable books.

[0060] The user information analysis unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and provide prompts that elicit positive emotions. The user information analysis unit, for example, analyzes the facial expressions and voice of the user when entering input and estimates the emotion in real time. For example, it uses a camera or microphone to analyze the user's emotion and provides prompts that elicit positive emotions. The user information analysis unit also uses the emotion estimation function to analyze the emotion of the user when entering input in real time and provide an interface for eliciting positive emotions. For example, it presents encouraging messages and success stories. The user information analysis unit also builds a system that analyzes the emotion of the user when entering input in real time and provides prompts that elicit positive emotions. For example, it displays appropriate encouraging or complimenting words according to the input content. In this way, the emotion of the user when entering input can be analyzed in real time and prompts that elicit positive emotions can be provided.

[0061] The database management unit can summarize the contents of each book using generative AI and add the summaries to the book database. For example, the database management unit can automatically summarize the contents of each book using generative AI and add the summaries to the database. For example, it can summarize the main points of a full-length novel into a short summary. The database management unit can also use generative AI to create summaries for each book and add them to the database. For example, it can summarize the main points of a non-fiction book. The database management unit can also use generative AI to build a system that summarizes the contents of each book and adds the summaries to the database. For example, it can search and recommend books based on the summaries. This allows for efficient searching and recommendations by summarizing the contents of each book and adding them to the database.

[0062] The database management unit can analyze readers' emotional responses to the content of a book and add emotion tags to the book database. The database management unit, for example, analyzes readers' reviews and ratings and generates emotion tags. For example, tags such as "moving" or "fun" are added to books with a lot of positive emotions. The database management unit also builds a system that analyzes readers' emotional responses and adds emotion tags to the database. For example, emotion tags are generated based on the content of reviews. The database management unit also uses an emotion estimation function to analyze readers' emotional responses and add emotion tags to the database. For example, tags are generated based on the reader's emotion score. In this way, by analyzing readers' emotional responses and adding emotion tags, it becomes possible to recommend books based on emotions.

[0063] The database management unit can use the emotion estimation function to analyze emotions for reviews and ratings of each book and add the emotion score to the book database. For example, the database management unit analyzes reviews and ratings of each book using the emotion estimation function and calculates an emotion score. For example, it identifies books with high positive emotion scores. The database management unit also uses the emotion estimation function to build a system that analyzes emotions for reviews and ratings of each book and adds the emotion score to the database. For example, it searches for and recommends books based on the emotion score. The database management unit also analyzes reviews and ratings of each book using the emotion estimation function and adds the emotion score to the database. For example, it creates a book ranking based on the emotion score. In this way, by analyzing emotions for reviews and ratings and adding the emotion score, it becomes possible to recommend books based on emotions.

[0064] The database management unit can also integrate information about audiobooks and e-books into the book database, enabling multi-format recommendations. For example, the database management unit adds audiobook information to the book database and makes multi-format recommendations to the user. For example, an audiobook and an e-book of the same book can be recommended simultaneously. The database management unit also integrates e-book information into the book database and makes multi-format recommendations to the user. For example, both e-books and paper books can be recommended. The database management unit also integrates information about audiobooks and e-books into the book database, building a system that makes multi-format recommendations to the user. For example, the format can be selected according to the user's preferences. In this way, multi-format recommendations can be made by integrating information about audiobooks and e-books.

[0065] The database management unit also adds information about movies and dramas related to each book to the book database, allowing for cross-media recommendations. The database management unit, for example, adds information about movies and dramas related to each book to the book database and makes cross-media recommendations. For example, it can simultaneously recommend the original book and its film adaptation. The database management unit also adds information about movies and dramas related to each book to the database, building a system that makes cross-media recommendations to users. For example, it can recommend books and movies on the same theme. The database management unit also integrates information about movies and dramas into the book database and makes cross-media recommendations to users. For example, it can recommend books that have been made into movies or dramas. This makes it possible to make cross-media recommendations by adding information about related movies and dramas.

[0066] The database management unit can use the emotion estimation function to analyze the emotional themes of books in the database and provide a search function by emotional theme. The database management unit, for example, uses the emotion estimation function to analyze the emotional themes of books in the database and provide a search function by emotional theme. For example, it makes it possible to search by theme such as "moving books" or "fun books." The database management unit also builds a system that analyzes the emotional themes of books in the database and provides a search function by emotional theme. For example, it classifies books based on emotion scores and provides a search function. The database management unit also uses the emotion estimation function to analyze the emotional themes of books in the database and provide a search function by emotional theme. For example, it makes it possible to search for books by theme according to the user's emotional state. In this way, by analyzing emotional themes and providing a search function by emotional theme, it becomes possible to search for books based on the user's emotions.

[0067] The recommendation unit can analyze the user's reading history and emotional responses, and prioritize recommend books that provide high emotional satisfaction. The recommendation unit, for example, analyzes the user's reading history and emotional responses, and identifies books that provide high emotional satisfaction. For example, it recommends books similar to books that have been highly rated in the past. The recommendation unit also builds a system that prioritizes recommending books that provide high emotional satisfaction based on the reading history and emotional responses. For example, it prioritizes displaying books with high emotional scores. The recommendation unit also analyzes the user's reading history and emotional responses, and recommends books that provide high emotional satisfaction. For example, it recommends books based on books with a high number of positive emotional responses. In this way, by analyzing the user's reading history and emotional responses, it is possible to prioritize recommend books that provide high emotional satisfaction.

[0068] The recommendation unit can analyze the user's reading speed and reading time, and recommend books that match the user's optimal reading pace. The recommendation unit, for example, analyzes the user's reading speed and recommends books that match the user's optimal reading pace. For example, it can recommend full-length novels to users who are good at speed reading, and short stories to users who read slowly. The recommendation unit also analyzes the user's reading time and builds a system that recommends books that match the user's optimal reading pace. For example, it can recommend books that can be read in a short time to users who have limited reading time. The recommendation unit also recommends books that match the user's optimal reading pace based on the reading speed and reading time. For example, it can recommend long books to users who read quickly, and short stories and essays to users who read quickly. In this way, by analyzing the user's reading speed and reading time, it is possible to recommend books that match the user's optimal reading pace.

[0069] The recommendation unit uses the emotion estimation function to predict the emotion a user will have toward a recommended book and can recommend books that elicit positive emotions. The recommendation unit, for example, uses the emotion estimation function to predict the emotion a user will have toward a recommended book. For example, it identifies books that elicit positive emotions based on past emotional response data. The recommendation unit also predicts the user's emotional response and builds a system that recommends books that elicit positive emotions. For example, it preferentially recommends books with high emotion scores. The recommendation unit also uses the emotion estimation function to predict the emotion a user will have toward a recommended book and recommends books that elicit positive emotions. For example, it adjusts the recommendation content according to changes in the user's emotions. This makes it possible to predict the emotion a user will have toward a recommended book and recommend books that elicit positive emotions.

[0070] The recommendation unit can analyze the reading history of the user's friends and family and recommend books that share common interests. For example, the recommendation unit can analyze the reading history of the user's friends and family and recommend books that share common interests. For example, it can recommend books that the whole family can enjoy. The recommendation unit can also build a system that recommends books that share common interests based on the reading history of friends and family. For example, it can recommend books that friends have given high ratings. The recommendation unit can also analyze the reading history of the user's friends and family and recommend books that share common interests. For example, it can recommend books in the same genre or theme. In this way, by analyzing the reading history of the user's friends and family, it can recommend books that share common interests.

[0071] The recommendation unit can analyze the user's geographical location information and recommend books available at local bookstores and libraries. For example, the recommendation unit analyzes the user's geographical location information and recommends books available at local bookstores and libraries. For example, it recommends books that can be purchased at nearby bookstores. The recommendation unit also builds a system that recommends books available at local bookstores and libraries based on the geographical location information. For example, it recommends books based on library collection information. The recommendation unit also analyzes the user's geographical location information and recommends books available at local bookstores and libraries. For example, it recommends books related to local events and fairs. In this way, by analyzing the user's geographical location information, it is possible to recommend books available at local bookstores and libraries.

[0072] The recommendation unit uses the emotion estimation function to monitor the user's emotional response to recommended books in real time and continuously recommend the most suitable books. The recommendation unit, for example, uses the emotion estimation function to monitor the user's emotional response to recommended books in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The recommendation unit also builds a system that continuously recommends the most suitable books based on the user's emotional response data. For example, it preferentially recommends books with a high number of positive emotional responses. The recommendation unit also uses the emotion estimation function to monitor the user's emotional response to recommended books in real time and continuously recommends the most suitable books. For example, it adjusts the recommendation content according to changes in the user's emotions. This allows the user to monitor the user's emotional response to recommended books in real time and continuously recommend the most suitable books.

[0073] The user interface unit can analyze the emotional state of the user and provide an interface design that corresponds to the emotion. For example, the user interface unit analyzes the emotional state of the user and provides an interface design that corresponds to the emotion. For example, when the user wants to relax, a design with calm colors is provided. The user interface unit also uses an emotion estimation function to analyze the user's emotional state in real time and dynamically change the interface design. For example, the design is adjusted according to the emotion score. The user interface unit also builds a system that analyzes the user's emotional state and provides an interface design that corresponds to the emotion. For example, a design that elicits positive emotions is provided. This improves the user's experience by providing an interface design that corresponds to the user's emotional state.

[0074] The user interface unit visually displays the user's reading history and ratings, and can provide an intuitively understandable dashboard. The user interface unit, for example, provides a dashboard visually displays the user's reading history and ratings. For example, it displays a list of books read and ratings in graphs and charts. The user interface unit also builds a system that provides an intuitively understandable dashboard based on the reading history and ratings. For example, it visually displays reading trends. The user interface unit also visually displays the user's reading history and ratings, and provides an intuitively understandable dashboard. For example, it highlights highly rated books and genres. In this way, by visually displaying the user's reading history and ratings, it is possible to provide an intuitively understandable dashboard.

[0075] The user interface unit can use the emotion estimation function to analyze the emotion a user feels when operating an interface and improve operability. The user interface unit, for example, uses the emotion estimation function to analyze the emotion a user feels when operating an interface and improve operability. For example, it simplifies the operation when the user is feeling stressed. The user interface unit also analyzes the user's emotional state in real time and provides an interface that improves operability. For example, it displays an operation guide that elicits positive emotions. The user interface unit also uses the emotion estimation function to analyze the emotion a user feels when operating an interface and builds a system that improves operability. For example, it adjusts the operation procedure according to the emotion score. This allows the emotion a user feels when operating an interface to be analyzed and improves operability.

[0076] The user interface unit can analyze the user's voice input and enable book search and recommendation by voice. The user interface unit, for example, analyzes the user's voice input and builds a system that enables book search and recommendation by voice. For example, it uses voice recognition technology to search for book titles and author names. The user interface unit also recommends the most suitable books to the user based on the voice input. For example, it can recommend books by specifying a favorite genre or theme by voice. The user interface unit also analyzes the user's voice input and enables book search and recommendation by voice. For example, it can display a list of books in response to a voice command. This makes it possible to analyze the user's voice input and enable book search and recommendation by voice.

[0077] The user interface unit can analyze the user's gesture input and enable touchless operation. The user interface unit, for example, analyzes the user's gesture input and builds a system that enables touchless operation. For example, a page can be turned with a hand movement. The user interface unit also allows the user to search for books and receive recommendations without touching them based on the gesture input. For example, a list of books can be scrolled with a finger movement. The user interface unit also analyzes the user's gesture input and enables touchless operation. For example, detailed information about a book can be displayed with a hand movement. In this way, the user's gesture input can be analyzed and touchless operation can be enabled.

[0078] The user interface unit can use the emotion estimation function to analyze the emotion of the user when operating the interface in real time, and provide an operation guide that elicits positive emotions. The user interface unit, for example, uses the emotion estimation function to analyze the emotion of the user when operating the interface in real time, and provide an operation guide that elicits positive emotions. For example, an encouraging message is displayed during operation. The user interface unit also builds a system that analyzes the user's emotional state in real time, and provides an operation guide that elicits positive emotions. For example, the operation procedure is adjusted according to the emotion score. The user interface unit also uses the emotion estimation function to analyze the emotion of the user when operating the interface in real time, and provide an operation guide that elicits positive emotions. For example, success stories are presented during operation. In this way, the emotion of the user when operating the interface can be analyzed in real time, and an operation guide that elicits positive emotions can be provided.

[0079] The user feedback analysis unit uses the emotion estimation function to analyze the user's emotion regarding the feedback and can prioritize reflect emotionally positive feedback. The user feedback analysis unit, for example, analyzes the user's emotion regarding the feedback and prioritizes reflecting emotionally positive feedback. For example, the algorithm is improved based on feedback with a high positive emotion score. The user feedback analysis unit also uses the emotion estimation function to analyze the user's emotion regarding the feedback and builds a system that prioritizes reflecting positive feedback. For example, the priority of feedback is determined according to the emotion score. The user feedback analysis unit also analyzes the user's emotion regarding the feedback and prioritizes reflecting emotionally positive feedback. For example, the algorithm is adjusted based on feedback that elicits positive emotions. This makes it possible to analyze the user's emotion regarding the feedback and prioritize reflecting emotionally positive feedback.

[0080] The user feedback analysis unit can summarize the content of the feedback using a generation AI and improve the algorithm based on the summary. The user feedback analysis unit, for example, summarizes the content of the user's feedback using a generation AI and improves the algorithm based on the summary. For example, it extracts the main points of the feedback and reflects them in the algorithm. The user feedback analysis unit can also summarize the content of the feedback using a generation AI and build a system that improves the algorithm based on the summary. For example, it can adjust the parameters of the algorithm based on the summary. The user feedback analysis unit can also summarize the content of the user's feedback using a generation AI and improve the algorithm based on the summary. For example, it can improve the accuracy of the algorithm based on the summary. In this way, the content of the feedback can be summarized and the algorithm can be improved.

[0081] The user feedback analysis unit uses the emotion estimation function to analyze the user's emotion toward the feedback and identify areas for improvement that are likely to be emotionally relatable. The user feedback analysis unit, for example, uses the emotion estimation function to analyze the user's emotion toward the feedback and identify areas for improvement that are likely to be emotionally relatable. For example, the unit extracts areas for improvement based on feedback that elicits a lot of positive emotions. The user feedback analysis unit also analyzes the user's emotion toward the feedback and builds a system that identifies areas for improvement that are likely to be emotionally relatable. For example, the unit determines the priority of the areas for improvement based on the emotion score. The user feedback analysis unit also uses the emotion estimation function to analyze the user's emotion toward the feedback and identify areas for improvement that are likely to be emotionally relatable. For example, the unit identifies areas for improvement based on feedback that elicits positive emotions. In this way, the user's emotion toward the feedback can be analyzed and areas for improvement that are likely to be emotionally relatable can be identified.

[0082] The user feedback analysis unit can share the user's feedback with other users and collect community-based improvement suggestions. The user feedback analysis unit, for example, builds a system for sharing the user's feedback with other users and collecting community-based improvement suggestions. For example, a feedback forum is set up to collect opinions. The user feedback analysis unit also shares feedback and collects improvement suggestions from other users. For example, a platform is provided where users can exchange opinions with each other. The user feedback analysis unit also shares the user's feedback with other users and collects community-based improvement suggestions. For example, discussions are held based on the feedback to identify areas for improvement. This makes it possible to share the user's feedback with other users and collect community-based improvement suggestions.

[0083] The user's feedback analysis unit can visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. The user's feedback analysis unit, for example, builds a system that visualizes the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it displays the main points of the feedback in a graph or chart. The user's feedback analysis unit can also visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it can display the content of the feedback in a mind map format. The user's feedback analysis unit can also visualize the content of the feedback, allowing the user to intuitively understand the areas for improvement. For example, it can display the content of the feedback in an infographic. In this way, the content of the feedback can be visualized, allowing the user to intuitively understand the areas for improvement.

[0084] The user feedback analysis unit uses the emotion estimation function to analyze the user's emotional response to feedback in real time and continuously reflect optimal improvements. The user feedback analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to feedback in real time and builds a system that continuously reflects optimal improvements. For example, the unit identifies improvements based on an emotion score. The user feedback analysis unit also analyzes the user's emotional response to feedback in real time and continuously reflects optimal improvements. For example, the unit identifies improvements based on feedback with a high percentage of positive emotions. The user feedback analysis unit also uses the emotion estimation function to analyze the user's emotional response to feedback in real time and continuously reflects optimal improvements. For example, the unit determines the priority of improvements based on the emotion score. This makes it possible to analyze the user's emotional response to feedback in real time and continuously reflect optimal improvements.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The user information analysis unit can analyze the user's reading environment and provide an optimal reading experience. For example, it can use sensors to detect the user's reading environment (lighting, volume, seating comfort, etc.) and suggest the optimal environment. The user information analysis unit can also suggest a time of day that is suitable for reading based on the user's reading environment. For example, it can recommend a quiet time of day or a time of day when you can relax. The user information analysis unit can also analyze the user's reading environment and suggest a place that is suitable for reading. For example, it can recommend a quiet place such as a cafe or library. This can optimize the user's reading environment and provide a better reading experience.

[0087] The user information analysis unit can analyze the user's health condition and suggest health-conscious reading habits. For example, it can use sensors to detect the user's posture and eye fatigue and suggest appropriate breaks. The user information analysis unit can also suggest stretches and exercises to do between reading sessions based on the user's health condition. For example, it can recommend simple exercises to reduce fatigue caused by long periods of reading. The user information analysis unit can also analyze the user's health condition and suggest health-conscious reading times. For example, it can set an appropriate reading time to prevent eye fatigue. This makes it possible to suggest reading habits that take the user's health condition into consideration and provide a healthy reading experience.

[0088] The user information analysis unit can analyze the user's learning style and suggest the optimal learning method. For example, it can analyze the user's learning style (visual, auditory, tactile, etc.) and suggest the optimal learning method. The user information analysis unit can also suggest learning materials suitable for learning based on the user's learning style. For example, it can recommend books with many diagrams and illustrations to a user who is good at visual learning. The user information analysis unit can also analyze the user's learning style and suggest the optimal time of day for studying. For example, it can recommend the time of day when concentration is highest. This makes it possible to suggest the optimal learning method that matches the user's learning style and provide an effective learning experience.

[0089] The user information analysis unit can analyze the user's reading history and visualize the user's reading progress. For example, it can analyze the number of pages of books read by the user and the reading time, and display the progress in graphs and charts. The user information analysis unit can also set reading goals based on the user's reading progress. For example, it can set monthly or yearly reading goals and visualize the progress of achievement. The user information analysis unit can also analyze the user's reading progress and provide feedback to improve reading motivation. For example, it can display rewards or encouraging messages for achieved goals. This makes it possible to visualize the user's reading progress and improve reading motivation.

[0090] The user information analysis unit can analyze a user's reading history and visualize their reading trends. For example, it can analyze the genres and themes of books read by the user and display the reading trends in graphs and charts. The user information analysis unit can also suggest books in new genres or themes based on the user's reading trends. For example, if there is a bias toward a particular genre, it can recommend books in a different genre. The user information analysis unit can also analyze reading trends and provide feedback to improve the diversity of reading. For example, it can explain the benefits of reading books in different genres and themes. This makes it possible to visualize the user's reading trends and improve the diversity of reading.

[0091] The user information analysis unit can use the emotion estimation function to analyze the emotions felt by the user while reading and provide a reading experience based on the emotions. For example, it can analyze the joy or surprise felt by the user while reading and provide feedback based on the emotions. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading in real time and suggest reading progress based on the emotions. For example, it can recommend reading in scenes that are emotionally charged. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading and suggest reading breaks based on the emotions. For example, it can recommend taking a break when the emotions have calmed down. This allows for a reading experience based on the user's emotions and a more fulfilling reading experience.

[0092] The user information analysis unit can use the emotion estimation function to analyze the emotions the user feels after reading and provide post-reading feedback based on the emotions. For example, it can analyze the satisfaction and excitement the user feels after reading and provide feedback based on the emotions. The user information analysis unit can also use the emotion estimation function to analyze the emotions the user feels after reading in real time and suggest post-reading activities based on the emotions. For example, it can recommend writing down impressions of scenes that are most moving. The user information analysis unit can also use the emotion estimation function to analyze the emotions the user feels after reading and suggest post-reading reflections based on the emotions. For example, it can recommend reflecting when emotions have calmed down. This makes it possible to provide post-reading feedback based on the user's emotions and achieve a more fulfilling reading experience.

[0093] The user information analysis unit can use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading progress based on the emotions. For example, it can analyze the excitement or tension felt by the user while reading and suggest a reading progress based on the emotions. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading in real time and suggest a reading pace based on the emotions. For example, it can adjust the reading pace in scenes where emotions become intense. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading break based on the emotions. For example, it can recommend taking a break when emotions have calmed down. This makes it possible to suggest a reading progress based on the user's emotions, resulting in a more fulfilling reading experience.

[0094] The user information analysis unit can use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading progress based on the emotions. For example, it can analyze the excitement or tension felt by the user while reading and suggest a reading progress based on the emotions. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading in real time and suggest a reading pace based on the emotions. For example, it can adjust the reading pace in scenes where emotions become intense. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading break based on the emotions. For example, it can recommend taking a break when emotions have calmed down. This makes it possible to suggest a reading progress based on the user's emotions, resulting in a more fulfilling reading experience.

[0095] The user information analysis unit can use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading progress based on the emotions. For example, it can analyze the excitement or tension felt by the user while reading and suggest a reading progress based on the emotions. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading in real time and suggest a reading pace based on the emotions. For example, it can adjust the reading pace in scenes where emotions become intense. The user information analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while reading and suggest a reading break based on the emotions. For example, it can recommend taking a break when emotions have calmed down. This makes it possible to suggest a reading progress based on the user's emotions, resulting in a more fulfilling reading experience.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The user information analysis unit analyzes user information. For example, the user inputs information such as a list of books they have read in the past, their ratings, favorite genres and authors, and topics of interest. The generation AI also analyzes the user's reading habits. The generation AI performs its analysis based on prompts, which include instructions on what the user wants the generation AI to do. Step 2: The recommendation unit recommends the most suitable book based on the user information analyzed by the user information analysis unit. For example, if a user likes mystery novels and prefers the works of a particular author, the recommendation unit will recommend other works by that author and highly rated books in the same genre. It will also recommend books related to the user's interests. Step 3: The database management department manages the book database. For example, it builds and manages a huge book database. This database contains information such as the title, author, genre, synopsis, rating, and reviews of each book. Step 4: The user interface unit provides the user with the books recommended by the recommendation unit. For example, it provides a form for the user to input their preferences and interests, and a screen that displays a list of recommended books. It also provides a function that allows the user to rate and review the recommended books.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0132] 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.

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

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

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

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

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

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

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

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

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

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

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

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

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information analysis unit that analyzes user information; a recommendation unit that recommends an optimal book based on the user information analyzed by the user information analysis unit; A database management department that manages the book database; a user interface unit that provides the user with the books recommended by the recommendation unit. A system characterized by:

2. The user information analysis unit Analyzes the user's emotional state in real time and recommends books that match their mood at that time 2. The system of claim 1.

3. The user information analysis unit Analyzing users' social media and blog posts to understand their interests in more detail 2. The system of claim 1.

4. The user information analysis unit Analyzes users' feelings about books they have read in the past and recommends books that evoke positive emotions 2. The system of claim 1.

5. The user information analysis unit To analyze users' visual preferences, facial expressions and eye movements while reading are captured with a camera and analyzed by generative AI.

2. The system of claim 1.

Images