system

Abstract

The system according to this embodiment aims to enable users to efficiently find the book that is best suited to them. [Solution] The system according to the embodiment comprises a reception unit, a recommendation unit, and a support unit. The reception unit receives user input. The recommendation unit recommends the most suitable book based on the information received by the reception unit. The support unit supports the reading progress of the book recommended by the recommendation unit.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] <>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for busy users to find the most suitable book for themselves.

[0005] The system according to the embodiment aims to enable a user to efficiently find the most suitable book for themselves.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a recommendation unit, and a support unit. The reception unit receives user input. The recommendation unit recommends the most suitable book based on the information received by the reception unit. The support unit supports the reading progress of the book recommended by the recommendation unit.

Effects of the Invention

[0007] The system according to this embodiment allows users to efficiently find the book that is best suited to them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problem of not being able to find the books that one truly needs in a busy life. This system has an AI personal librarian that instantly provides the user with books that the user wants to read, creating an environment in which the user can maximize their relaxation and growth. This provides enriching time and contributes to personal and business growth. For example, the user can have a simple interaction with the generative AI. For example, if the user asks, "What are some recommended books lately?", the generative AI will recommend the most suitable book based on the user's interests and reading history. The generative AI learns the user's reading pace and suggests books at a comfortable pace. It also links not only online bookstores but also library databases, making it possible to read books for free. Next, the generative AI monitors the user's reading progress and supports a comfortable pace. For example, if the user tells the generative AI their impressions of a book they have read, the generative AI will suggest the next book to read based on those impressions. This allows the user to enjoy reading at their own pace. Furthermore, the generative AI provides a note function that allows the user to organize the books they have read, their summaries, and their impressions. This allows the user to review what books they have read at any time. For example, if a user asks the generative AI to "show me a list of books I've read in the past," the AI ​​will display a list of books the user has read. This system is offered to a wide range of targets, including students, working professionals, educational institutions, and corporations. For instance, the generative AI can suggest optimal textbooks to help students continue their independent learning. Similarly, the AI ​​can recommend the most suitable business books to help employees efficiently find books related to their work. In this way, by utilizing generative AI, users can save time choosing books and be supported in enriching their reading lives. This contributes to personal and business growth, aiming to realize a prosperous and sustainable society. Thus, the system can improve the user's reading experience and contribute to personal and business growth.

[0029] The system according to this embodiment comprises a reception unit, a recommendation unit, and a support unit. The reception unit receives user input. For example, if a user asks, "What are some recommended books lately?", the reception unit can receive the question. The recommendation unit recommends the most suitable books based on the information received by the reception unit. For example, the recommendation unit uses generative AI to recommend the most suitable books based on the user's interests and reading history. The generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models or machine learning algorithms. The support unit supports the reading progress of the books recommended by the recommendation unit. For example, the support unit can learn the user's reading pace and suggest books at a comfortable pace. The support unit can also suggest the next book to read based on the user's reading progress. Furthermore, the support unit can provide a note function that allows the user to organize the books they have read, their summaries, and their impressions. For example, the support unit can display a list of books the user has read, allowing the user to review what books they have read at any time. In this way, the system according to this embodiment can improve the user's reading experience. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's reading history data into a generating AI to learn the user's reading pace, and the generating AI can then analyze the reading pace.

[0030] The reception desk receives user input. For example, if a user asks, "What are some recently recommended books?", the reception desk can receive that question. Specifically, the reception desk receives user input through the user interface and analyzes its content. The user interface includes text input fields and voice input functions, and is designed to allow users to input questions and requests in natural language. In the case of voice input, speech recognition technology is used to convert the user's utterance into text, which is then analyzed. The reception desk analyzes the user's input and extracts the information necessary for appropriate processing. For example, if a user asks, "What are some recently recommended books?", the reception desk extracts the keyword "recommended books" and passes that information to the recommendation department. Furthermore, the reception desk can refer to the user's past input history and profile information, and, taking into account the user's interests and preferences, can provide information to make more appropriate recommendations. In this way, the reception desk can accurately receive user input and provide a foundation for appropriate processing.

[0031] The recommendation department recommends the most suitable books based on the information received by the reception department. For example, the recommendation department uses generative AI to recommend books based on the user's interests and reading history. Generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models and machine learning algorithms. Specifically, the generative AI analyzes data such as the user's past reading history, ratings, reviews, and search history to model the user's preferences. The natural language processing model analyzes the user's input and extracts relevant keywords and themes. For example, if a user inputs "I like mystery novels," the generative AI will extract the keyword "mystery" and, based on data of mystery novels the user has read in the past, recommend new mystery novels that the user might be interested in. Furthermore, machine learning algorithms can utilize the reading history and rating data of other users to recommend books that have been highly rated by users with similar preferences. This allows the recommendation department to provide personalized book recommendations based on the user's interests and preferences. In addition, the recommendation department can provide recommendations that reflect the latest trends and new releases based on data that is updated in real time. This allows users to always receive the best book recommendations based on the latest information.

[0032] The support team assists users in their reading progress for books recommended by the recommendation team. For example, the support team learns the user's reading pace and can suggest books at a manageable pace. It can also monitor the user's reading progress and suggest the next book to read. Specifically, the support team collects the user's reading history data and analyzes their reading pace and completion time. Using generative AI, it learns the user's reading patterns and suggests an optimal reading pace. For example, if a user reads one book per week, the support team will suggest the next book to read at that pace. The support team also displays a list of books the user has read, allowing them to review their reading history at any time. Furthermore, the support team can provide a note-taking function to organize books read, their summaries, and the user's impressions. For example, users can input their thoughts and notes on books they have read, organize them, and save them. This allows users to easily manage their reading history and use it as a reference when choosing their next book. Finally, the support team provides reading advice and reminders to help users continue reading. For example, reminders can be sent if a user is falling behind in their reading progress, encouraging them to continue reading. This allows the support team to improve the user's reading experience and provide support to make it easier for them to continue reading.

[0033] The recommendation system uses generative AI to recommend the most suitable books based on the user's interests and reading history. For example, the recommendation system uses generative AI to identify the user's interests and select the most suitable books. The generative AI can identify the user's interests using, for example, natural language processing models or machine learning algorithms. The recommendation system can also recommend the most suitable books based on the user's reading history. For example, the recommendation system analyzes a list of books the user has read in the past and recommends related books. This makes it possible to recommend the most suitable books based on the user's interests and reading history by using generative AI. The generative AI can, for example, take the user's reading history data as input and output a list of the most suitable books. The generative AI learns the user's interests and reading history and executes an algorithm to recommend the most suitable books. This allows the user to find the book that is best suited to them.

[0034] The support unit can learn the user's reading pace and suggest books at a comfortable pace. For example, the support unit learns the user's reading pace and suggests books that allow the user to continue reading without difficulty. The support unit can, for example, analyze the user's reading history data to identify the user's reading pace. The support unit can also suggest books at a comfortable pace based on the user's lifestyle. For example, the support unit suggests books at a comfortable pace based on the user's past reading pace and lifestyle. In this way, by learning the user's reading pace, it can suggest books at a comfortable pace. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's reading pace data into a generating AI, and the generating AI can execute an algorithm that suggests books at a comfortable pace.

[0035] The support unit can suggest the next book to read based on the user's reading progress. For example, the support unit can suggest the next book based on the user's reading progress. The support unit can also suggest the next book based on the user's impressions of the books they have read. Furthermore, the support unit can analyze the user's reading progress data to identify the next book to read. For example, the support unit can suggest the next book based on the user's reading progress data. This improves the reading experience by suggesting the next book based on the user's reading progress. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's reading progress data into a generating AI, and the generating AI can execute an algorithm that suggests the next book to read.

[0036] The support unit can provide a note-taking function that allows users to organize books they have read, their summaries, and their impressions. For example, the support unit can display a list of books the user has read, allowing them to review what books they have read at any time. The support unit can also organize summaries and impressions of books the user has read and save them as notes. Furthermore, the support unit can suggest books to read next based on the user's impressions of the books they have read. For example, the support unit can analyze the user's impressions of the books they have read and suggest books to read next. This allows for the management of reading history by providing a note-taking function that allows users to organize books they have read, their summaries, and their impressions. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's reading history data into a generating AI, and the generating AI can execute an algorithm that provides the note-taking function.

[0037] The support unit can integrate with library databases to enable users to read books for free. For example, the support unit can integrate with library databases to allow users to read books for free. The support unit can integrate with online catalogs and e-book services to allow users to read books for free. The support unit can also search library databases to help users find books they want to read. For example, the support unit can search library databases based on the user's interests and suggest relevant books. This enables users to read books for free by integrating with library databases. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input library databases into a generating AI, which can then execute an algorithm for reading books for free.

[0038] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also suggest relevant input methods based on the user's past input. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows for the selection of the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then execute an algorithm to select the optimal input method.

[0039] The reception desk can filter input based on the user's current lifestyle and areas of interest. For example, if the user is at work, the reception desk will prioritize input of books related to work. If the user is on vacation, the reception desk can also prioritize input of books that promote relaxation. Furthermore, if the user is working on a specific project, the reception desk can prioritize input of books related to that project. This allows for the provision of more relevant information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle and areas of interest data into a generating AI, which can then execute an algorithm to perform filtering.

[0040] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit can prioritize input of books related to that region. Furthermore, if the user is traveling, the reception unit can prioritize input of books related to their travel destination. Additionally, if the user is participating in a specific event, the reception unit can prioritize input of books related to that event. This allows for the priority receipt of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then execute an algorithm that prioritizes receiving highly relevant information.

[0041] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can prioritize input of books that the user is talking about on social media. It can also prioritize receiving information on new books by authors that the user follows. Furthermore, it can prioritize receiving book recommendations from reading groups that the user belongs to. In this way, by analyzing the user's social media activity, it can prioritize receiving relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can execute an algorithm to receive relevant information.

[0042] The recommendation system can adjust the level of detail in recommendations based on the importance of the books. For example, for important books, the recommendation system will provide recommendations with detailed descriptions. For less important books, the recommendation system can provide recommendations with concise descriptions. Furthermore, the recommendation system can adjust the level of detail in recommendations according to the user's level of interest. This allows for the provision of more relevant information by adjusting the level of detail in recommendations based on the importance of the books. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input book importance data into a generative AI and execute an algorithm that adjusts the level of detail in recommendations using the generative AI.

[0043] The recommendation system can apply different recommendation algorithms depending on the book category. For example, for novels, it can apply a recommendation algorithm that emphasizes storytelling. For business books, it can apply a recommendation algorithm that emphasizes practicality. Furthermore, for academic books, it can apply a recommendation algorithm that emphasizes reliability. By applying different recommendation algorithms depending on the book category, it can recommend more appropriate books. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input book category data into a generative AI and execute an algorithm that applies different recommendation algorithms using the generative AI.

[0044] The recommendation system can determine the priority of recommendations based on the publication date of the books. For example, it will prioritize new releases. It can also recommend classics based on the user's interests. Furthermore, it can prioritize recommending sequels to books the user has previously read. This allows for the recommendation of more appropriate books by prioritizing recommendations based on publication date. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input book publication date data into a generative AI, which can then execute an algorithm to determine the recommendation priority.

[0045] The recommendation system can adjust the order of recommendations based on the relevance of the books. For example, it may recommend books most relevant to the user's interests first. It can also prioritize recommending books that are highly relevant based on the user's past reading history. Furthermore, it can prioritize recommending books that are highly relevant based on the user's current areas of interest. This allows for the recommendation of more relevant books to be prioritized by adjusting the order of recommendations based on their relevance. Some or all of the above processes in the recommendation system may be performed using, for example, generative AI, or without generative AI. For example, the recommendation system may input book relevance data into a generative AI and execute an algorithm that adjusts the order of recommendations using the generative AI.

[0046] The support unit can analyze the user's past reading history to select the optimal support method when providing reading progress support. For example, the support unit can suggest an optimal reading pace based on the user's progress with books they have read in the past. It can also analyze the user's reading trends from their past reading history and suggest the optimal support method. Furthermore, the support unit can suggest the next book to read based on the user's impressions of books they have read in the past. In this way, the optimal support method can be selected by analyzing the user's past reading history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past reading history data into a generating AI, which can then execute an algorithm to select the optimal support method.

[0047] The support unit can customize the means of support when providing reading progress support based on the user's current life circumstances. For example, if the user is busy, the support unit can suggest books that can be read in a short amount of time. Conversely, if the user is relaxed, the support unit can suggest books that can be read for a longer period. Furthermore, if the user is working on a specific project, the support unit can suggest books related to that project. This allows for more appropriate support to be provided by customizing the means of support based on the user's current life circumstances. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user life circumstances data into a generating AI, which can then execute an algorithm to customize the means of support.

[0048] The support unit can select the optimal support method when providing reading progress support, taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can support them in reading books related to that region. Furthermore, if the user is traveling, the support unit can support them in reading books related to their travel destination. In addition, if the user is participating in a specific event, the support unit can support them in reading books related to that event. This allows the support unit to select the optimal support method by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI, which can then execute an algorithm to select the optimal support method.

[0049] The support unit can analyze a user's social media activity and suggest support methods when providing reading progress support. For example, the support unit can support the user in reading books that the user is discussing on social media. It can also prioritize supporting the user in reading new releases from authors the user follows. Furthermore, the support unit can support the user in reading books recommended by reading groups they participate in. This allows the support unit to suggest relevant support methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can then execute an algorithm to suggest support methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The recommendation system can analyze not only a user's reading history but also their social media activity and interests to provide more personalized book recommendations. For example, if a user frequently posts about a particular genre on social media, books related to that genre will be prioritized for recommendation. Recommendations can also be based on new releases from authors the user follows. Furthermore, recommendations can be made based on books recommended by online reading groups the user participates in. This enables more accurate book recommendations that take into account the user's online activities.

[0052] The support team can suggest books that take into account the user's lifestyle and schedule when assisting with their reading progress. For example, they can suggest books that can be read quickly during busy periods and longer books during more relaxed periods. They can also suggest books related to a specific project the user is working on. Furthermore, if the user is traveling, they can suggest books related to their travel destination. This allows for the suggestion of appropriate books tailored to the user's circumstances.

[0053] The support team can suggest books that support a user's reading progress, taking into account not only their reading history but also their hobbies and interests. For example, if a user reads many books related to a particular hobby, the support team can suggest new books related to that hobby. It can also suggest books related to areas the user is interested in. Furthermore, it can suggest books to read next based on the user's impressions of books they have read in the past. This allows for the suggestion of appropriate books that match the user's hobbies and interests.

[0054] The recommendation system can suggest books considering not only the user's reading history but also their geographical location. For example, if a user is in a specific region, it can prioritize recommending books related to that region. If a user is traveling, it can also recommend books related to their destination. Furthermore, if a user is attending a specific event, it can recommend books related to that event. This enables more relevant book recommendations that take the user's geographical location into account.

[0055] The reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also suggest relevant input methods based on the content the user has entered in the past. Furthermore, it can predict and suggest input methods that the user will use during specific time periods. In this way, by analyzing the user's past input history, the system can suggest the most suitable input method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives user input. For example, if a user asks, "What are some recommended books lately?", the reception desk can accept that question. Step 2: The recommendation department recommends the most suitable books based on the information received by the reception department. For example, the recommendation department uses generative AI to recommend the most suitable books based on the user's interests and reading history. Generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models or machine learning algorithms. Step 3: The support team assists with the reading progress of books recommended by the recommendation team. For example, the support team can learn the user's reading pace and suggest books at a manageable pace. It can also monitor the user's reading progress and suggest the next book to read. Furthermore, the support team can provide a note-taking function to organize the books read, their summaries, and the user's impressions. For example, the support team can display a list of books the user has read, allowing them to review what books they have read at any time.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problem of not being able to find the books that one truly needs in a busy life. This system has an AI personal librarian that instantly provides the user with books that the user wants to read, creating an environment in which the user can maximize their relaxation and growth. This provides enriching time and contributes to personal and business growth. For example, the user can have a simple interaction with the generative AI. For example, if the user asks, "What are some recommended books lately?", the generative AI will recommend the most suitable book based on the user's interests and reading history. The generative AI learns the user's reading pace and suggests books at a comfortable pace. It also links not only online bookstores but also library databases, making it possible to read books for free. Next, the generative AI monitors the user's reading progress and supports a comfortable pace. For example, if the user tells the generative AI their impressions of a book they have read, the generative AI will suggest the next book to read based on those impressions. This allows the user to enjoy reading at their own pace. Furthermore, the generative AI provides a note function that allows the user to organize the books they have read, their summaries, and their impressions. This allows the user to review what books they have read at any time. For example, if a user asks the generative AI to "show me a list of books I've read in the past," the AI ​​will display a list of books the user has read. This system is offered to a wide range of targets, including students, working professionals, educational institutions, and corporations. For instance, the generative AI can suggest optimal textbooks to help students continue their independent learning. Similarly, the AI ​​can recommend the most suitable business books to help employees efficiently find books related to their work. In this way, by utilizing generative AI, users can save time choosing books and be supported in enriching their reading lives. This contributes to personal and business growth, aiming to realize a prosperous and sustainable society. Thus, the system can improve the user's reading experience and contribute to personal and business growth.

[0059] The system according to this embodiment comprises a reception unit, a recommendation unit, and a support unit. The reception unit receives user input. For example, if a user asks, "What are some recommended books lately?", the reception unit can receive the question. The recommendation unit recommends the most suitable books based on the information received by the reception unit. For example, the recommendation unit uses generative AI to recommend the most suitable books based on the user's interests and reading history. The generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models or machine learning algorithms. The support unit supports the reading progress of the books recommended by the recommendation unit. For example, the support unit can learn the user's reading pace and suggest books at a comfortable pace. The support unit can also suggest the next book to read based on the user's reading progress. Furthermore, the support unit can provide a note function that allows the user to organize the books they have read, their summaries, and their impressions. For example, the support unit can display a list of books the user has read, allowing the user to review what books they have read at any time. In this way, the system according to this embodiment can improve the user's reading experience. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's reading history data into a generating AI to learn the user's reading pace, and the generating AI can then analyze the reading pace.

[0060] The reception desk receives user input. For example, if a user asks, "What are some recently recommended books?", the reception desk can receive that question. Specifically, the reception desk receives user input through the user interface and analyzes its content. The user interface includes text input fields and voice input functions, and is designed to allow users to input questions and requests in natural language. In the case of voice input, speech recognition technology is used to convert the user's utterance into text, which is then analyzed. The reception desk analyzes the user's input and extracts the information necessary for appropriate processing. For example, if a user asks, "What are some recently recommended books?", the reception desk extracts the keyword "recommended books" and passes that information to the recommendation department. Furthermore, the reception desk can refer to the user's past input history and profile information, and, taking into account the user's interests and preferences, can provide information to make more appropriate recommendations. In this way, the reception desk can accurately receive user input and provide a foundation for appropriate processing.

[0061] The recommendation department recommends the most suitable books based on the information received by the reception department. For example, the recommendation department uses generative AI to recommend books based on the user's interests and reading history. Generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models and machine learning algorithms. Specifically, the generative AI analyzes data such as the user's past reading history, ratings, reviews, and search history to model the user's preferences. The natural language processing model analyzes the user's input and extracts relevant keywords and themes. For example, if a user inputs "I like mystery novels," the generative AI will extract the keyword "mystery" and, based on data of mystery novels the user has read in the past, recommend new mystery novels that the user might be interested in. Furthermore, machine learning algorithms can utilize the reading history and rating data of other users to recommend books that have been highly rated by users with similar preferences. This allows the recommendation department to provide personalized book recommendations based on the user's interests and preferences. In addition, the recommendation department can provide recommendations that reflect the latest trends and new releases based on data that is updated in real time. This allows users to always receive the best book recommendations based on the latest information.

[0062] The support team assists users in their reading progress for books recommended by the recommendation team. For example, the support team learns the user's reading pace and can suggest books at a manageable pace. It can also monitor the user's reading progress and suggest the next book to read. Specifically, the support team collects the user's reading history data and analyzes their reading pace and completion time. Using generative AI, it learns the user's reading patterns and suggests an optimal reading pace. For example, if a user reads one book per week, the support team will suggest the next book to read at that pace. The support team also displays a list of books the user has read, allowing them to review their reading history at any time. Furthermore, the support team can provide a note-taking function to organize books read, their summaries, and the user's impressions. For example, users can input their thoughts and notes on books they have read, organize them, and save them. This allows users to easily manage their reading history and use it as a reference when choosing their next book. Finally, the support team provides reading advice and reminders to help users continue reading. For example, reminders can be sent if a user is falling behind in their reading progress, encouraging them to continue reading. This allows the support team to improve the user's reading experience and provide support to make it easier for them to continue reading.

[0063] The recommendation system uses generative AI to recommend the most suitable books based on the user's interests and reading history. For example, the recommendation system uses generative AI to identify the user's interests and select the most suitable books. The generative AI can identify the user's interests using, for example, natural language processing models or machine learning algorithms. The recommendation system can also recommend the most suitable books based on the user's reading history. For example, the recommendation system analyzes a list of books the user has read in the past and recommends related books. This makes it possible to recommend the most suitable books based on the user's interests and reading history by using generative AI. The generative AI can, for example, take the user's reading history data as input and output a list of the most suitable books. The generative AI learns the user's interests and reading history and executes an algorithm to recommend the most suitable books. This allows the user to find the book that is best suited to them.

[0064] The support unit can learn the user's reading pace and suggest books at a comfortable pace. For example, the support unit learns the user's reading pace and suggests books that allow the user to continue reading without difficulty. The support unit can, for example, analyze the user's reading history data to identify the user's reading pace. The support unit can also suggest books at a comfortable pace based on the user's lifestyle. For example, the support unit suggests books at a comfortable pace based on the user's past reading pace and lifestyle. In this way, by learning the user's reading pace, it can suggest books at a comfortable pace. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's reading pace data into a generating AI, and the generating AI can execute an algorithm that suggests books at a comfortable pace.

[0065] The support unit can suggest the next book to read based on the user's reading progress. For example, the support unit can suggest the next book based on the user's reading progress. The support unit can also suggest the next book based on the user's impressions of the books they have read. Furthermore, the support unit can analyze the user's reading progress data to identify the next book to read. For example, the support unit can suggest the next book based on the user's reading progress data. This improves the reading experience by suggesting the next book based on the user's reading progress. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's reading progress data into a generating AI, and the generating AI can execute an algorithm that suggests the next book to read.

[0066] The support unit can provide a note-taking function that allows users to organize books they have read, their summaries, and their impressions. For example, the support unit can display a list of books the user has read, allowing them to review what books they have read at any time. The support unit can also organize summaries and impressions of books the user has read and save them as notes. Furthermore, the support unit can suggest books to read next based on the user's impressions of the books they have read. For example, the support unit can analyze the user's impressions of the books they have read and suggest books to read next. This allows for the management of reading history by providing a note-taking function that allows users to organize books they have read, their summaries, and their impressions. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's reading history data into a generating AI, and the generating AI can execute an algorithm that provides the note-taking function.

[0067] The support unit can integrate with library databases to enable users to read books for free. For example, the support unit can integrate with library databases to allow users to read books for free. The support unit can integrate with online catalogs and e-book services to allow users to read books for free. The support unit can also search library databases to help users find books they want to read. For example, the support unit can search library databases based on the user's interests and suggest relevant books. This enables users to read books for free by integrating with library databases. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input library databases into a generating AI, which can then execute an algorithm for reading books for free.

[0068] The reception unit can estimate the user's emotions and adjust the timing of input requests based on the estimated emotions. For example, if the reception unit is feeling stressed, it can prompt the user to input during a time when they can relax. It can also prompt the user to input when they are concentrating. Furthermore, if the user is tired, it can prompt them to input after a break. By adjusting the timing of input requests based on the user's emotions, input can be prompted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and execute an algorithm that adjusts the timing of input requests using the generative AI.

[0069] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also suggest relevant input methods based on the user's past input. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows for the selection of the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then execute an algorithm to select the optimal input method.

[0070] The reception desk can filter input based on the user's current lifestyle and areas of interest. For example, if the user is at work, the reception desk will prioritize input of books related to work. If the user is on vacation, the reception desk can also prioritize input of books that promote relaxation. Furthermore, if the user is working on a specific project, the reception desk can prioritize input of books related to that project. This allows for the provision of more relevant information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle and areas of interest data into a generating AI, which can then execute an algorithm to perform filtering.

[0071] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the reception unit is excited, it will prioritize input of books in areas of interest. It can also prioritize input of relaxing books if the user is relaxed. Furthermore, if the user is stressed, it can prioritize input of books that help relieve stress. This allows for the prioritization of more appropriate information by determining input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI, which can then execute an algorithm to determine the priority of input content.

[0072] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit can prioritize input of books related to that region. Furthermore, if the user is traveling, the reception unit can prioritize input of books related to their travel destination. Additionally, if the user is participating in a specific event, the reception unit can prioritize input of books related to that event. This allows for the priority receipt of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then execute an algorithm that prioritizes receiving highly relevant information.

[0073] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can prioritize input of books that the user is talking about on social media. It can also prioritize receiving information on new books by authors that the user follows. Furthermore, it can prioritize receiving book recommendations from reading groups that the user belongs to. In this way, by analyzing the user's social media activity, it can prioritize receiving relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can execute an algorithm to receive relevant information.

[0074] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system will recommend books using calm language. If the user is excited, it can recommend books using energetic language. Furthermore, if the user is stressed, it can recommend books using soothing language. By adjusting the way recommendations are presented based on the user's emotions, the system can recommend books in a more appropriate way. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and execute an algorithm that adjusts the way recommendations are presented using the generative AI.

[0075] The recommendation system can adjust the level of detail in recommendations based on the importance of the books. For example, for important books, the recommendation system will provide recommendations with detailed descriptions. For less important books, the recommendation system can provide recommendations with concise descriptions. Furthermore, the recommendation system can adjust the level of detail in recommendations according to the user's level of interest. This allows for the provision of more relevant information by adjusting the level of detail in recommendations based on the importance of the books. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input book importance data into a generative AI and execute an algorithm that adjusts the level of detail in recommendations using the generative AI.

[0076] The recommendation system can apply different recommendation algorithms depending on the book category. For example, for novels, it can apply a recommendation algorithm that emphasizes storytelling. For business books, it can apply a recommendation algorithm that emphasizes practicality. Furthermore, for academic books, it can apply a recommendation algorithm that emphasizes reliability. By applying different recommendation algorithms depending on the book category, it can recommend more appropriate books. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input book category data into a generative AI and execute an algorithm that applies different recommendation algorithms using the generative AI.

[0077] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation section can provide short, concise recommendations. If the user is relaxed, it can provide longer recommendations with more detailed descriptions. Furthermore, if the user is excited, it can provide recommendations with visually stimulating effects. By adjusting the length of recommendations based on the user's emotions, books can be recommended at a more appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI execute an algorithm to adjust the length of recommendations.

[0078] The recommendation system can determine the priority of recommendations based on the publication date of the books. For example, it will prioritize new releases. It can also recommend classics based on the user's interests. Furthermore, it can prioritize recommending sequels to books the user has previously read. This allows for the recommendation of more appropriate books by prioritizing recommendations based on publication date. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input book publication date data into a generative AI, which can then execute an algorithm to determine the recommendation priority.

[0079] The recommendation system can adjust the order of recommendations based on the relevance of the books. For example, it may recommend books most relevant to the user's interests first. It can also prioritize recommending books that are highly relevant based on the user's past reading history. Furthermore, it can prioritize recommending books that are highly relevant based on the user's current areas of interest. This allows for the recommendation of more relevant books to be prioritized by adjusting the order of recommendations based on their relevance. Some or all of the above processes in the recommendation system may be performed using, for example, generative AI, or without generative AI. For example, the recommendation system may input book relevance data into a generative AI and execute an algorithm that adjusts the order of recommendations using the generative AI.

[0080] The support unit can estimate the user's emotions and adjust how it supports the reading progress based on those emotions. For example, if the user is relaxed, the support unit will support reading at a relaxed pace. If the user is in a hurry, the support unit can also suggest efficient reading methods. Furthermore, if the user is stressed, the support unit can suggest a relaxing reading environment. This allows for more appropriate support by adjusting how it supports the reading progress based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and execute an algorithm that adjusts how it supports the reading progress using the generative AI.

[0081] The support unit can analyze the user's past reading history to select the optimal support method when providing reading progress support. For example, the support unit can suggest an optimal reading pace based on the user's progress with books they have read in the past. It can also analyze the user's reading trends from their past reading history and suggest the optimal support method. Furthermore, the support unit can suggest the next book to read based on the user's impressions of books they have read in the past. In this way, the optimal support method can be selected by analyzing the user's past reading history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past reading history data into a generating AI, which can then execute an algorithm to select the optimal support method.

[0082] The support unit can customize the means of support when providing reading progress support based on the user's current life circumstances. For example, if the user is busy, the support unit can suggest books that can be read in a short amount of time. Conversely, if the user is relaxed, the support unit can suggest books that can be read for a longer period. Furthermore, if the user is working on a specific project, the support unit can suggest books related to that project. This allows for more appropriate support to be provided by customizing the means of support based on the user's current life circumstances. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user life circumstances data into a generating AI, which can then execute an algorithm to customize the means of support.

[0083] The support unit can estimate the user's emotions and determine the priority of reading progress based on the estimated emotions. For example, if the user is excited, the support unit will prioritize reading books in areas of interest. It can also prioritize reading relaxing books if the user is relaxed. Furthermore, if the user is stressed, it can prioritize reading books that help relieve stress. This allows for a more appropriate reading experience by prioritizing reading progress based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input user emotion data into a generative AI, which can then execute an algorithm to determine the priority of reading progress.

[0084] The support unit can select the optimal support method when providing reading progress support, taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can support them in reading books related to that region. Furthermore, if the user is traveling, the support unit can support them in reading books related to their travel destination. In addition, if the user is participating in a specific event, the support unit can support them in reading books related to that event. This allows the support unit to select the optimal support method by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI, which can then execute an algorithm to select the optimal support method.

[0085] The support unit can analyze a user's social media activity and suggest support methods when providing reading progress support. For example, the support unit can support the user in reading books that the user is discussing on social media. It can also prioritize supporting the user in reading new releases from authors the user follows. Furthermore, the support unit can support the user in reading books recommended by reading groups they participate in. This allows the support unit to suggest relevant support methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI, which can then execute an algorithm to suggest support methods.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can analyze the tone and speed of the user's voice when receiving user input, and estimate the user's emotional state. For example, if the user is agitated, the reception desk will respond in a calm tone to alleviate their excitement. If the user is depressed, the reception desk can respond with words of encouragement. Furthermore, if the user is tired, the reception desk can provide a concise and easy-to-understand response. By providing appropriate responses tailored to the user's emotional state, user satisfaction can be improved.

[0088] The recommendation system can analyze not only a user's reading history but also their social media activity and interests to provide more personalized book recommendations. For example, if a user frequently posts about a particular genre on social media, books related to that genre will be prioritized for recommendation. Recommendations can also be based on new releases from authors the user follows. Furthermore, recommendations can be made based on books recommended by online reading groups the user participates in. This enables more accurate book recommendations that take into account the user's online activities.

[0089] The support team can adjust its support methods to accommodate users' emotional states when assisting them with their reading progress. For example, if a user is feeling stressed, it can suggest a relaxing reading environment. If a user is concentrating, it can suggest reading methods to maintain focus. Furthermore, if a user is tired, it can suggest books that can be read in a short amount of time. By providing appropriate support tailored to the user's emotional state, the support team can improve the reading experience.

[0090] The support team can suggest books that take into account the user's lifestyle and schedule when assisting with their reading progress. For example, they can suggest books that can be read quickly during busy periods and longer books during more relaxed periods. They can also suggest books related to a specific project the user is working on. Furthermore, if the user is traveling, they can suggest books related to their travel destination. This allows for the suggestion of appropriate books tailored to the user's circumstances.

[0091] The support team can suggest books that support a user's reading progress, taking into account not only their reading history but also their hobbies and interests. For example, if a user reads many books related to a particular hobby, the support team can suggest new books related to that hobby. It can also suggest books related to areas the user is interested in. Furthermore, it can suggest books to read next based on the user's impressions of books they have read in the past. This allows for the suggestion of appropriate books that match the user's hobbies and interests.

[0092] The reception desk can estimate the user's emotional state when receiving user input and adjust the input method based on that estimation. For example, if the user is tense, it can suggest an input method that promotes relaxation. If the user is relaxed, it can encourage more detailed input. Furthermore, if the user is excited, it can suggest a concise input method. By providing an appropriate input method according to the user's emotional state, the system can improve the user's input experience.

[0093] The recommendation system can suggest books considering not only the user's reading history but also their geographical location. For example, if a user is in a specific region, it can prioritize recommending books related to that region. If a user is traveling, it can also recommend books related to their destination. Furthermore, if a user is attending a specific event, it can recommend books related to that event. This enables more relevant book recommendations that take the user's geographical location into account.

[0094] The support unit can estimate the user's emotional state and adjust the reading pace based on that estimate when assisting the user's reading progress. For example, if the user is relaxed, it will support reading at a leisurely pace. If the user is in a hurry, it can suggest efficient reading methods. Furthermore, if the user is stressed, it can suggest a relaxing reading environment. In this way, the reading experience can be improved by providing an appropriate reading pace according to the user's emotional state.

[0095] The reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). It can also suggest relevant input methods based on the content the user has entered in the past. Furthermore, it can predict and suggest input methods that the user will use during specific time periods. In this way, by analyzing the user's past input history, the system can suggest the most suitable input method.

[0096] The recommendation system can estimate the user's emotional state and adjust the recommendation style based on that estimation. For example, if the user is relaxed, it can recommend books using calming language. If the user is excited, it can recommend books using energetic language. Furthermore, if the user is stressed, it can recommend books using soothing language. By recommending books with appropriate language according to the user's emotional state, it can improve user satisfaction.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives user input. For example, if a user asks, "What are some recommended books lately?", the reception desk can accept that question. Step 2: The recommendation department recommends the most suitable books based on the information received by the reception department. For example, the recommendation department uses generative AI to recommend the most suitable books based on the user's interests and reading history. Generative AI can identify the user's interests and select the most suitable books using, for example, natural language processing models or machine learning algorithms. Step 3: The support team assists with the reading progress of books recommended by the recommendation team. For example, the support team can learn the user's reading pace and suggest books at a manageable pace. It can also monitor the user's reading progress and suggest the next book to read. Furthermore, the support team can provide a note-taking function to organize the books read, their summaries, and the user's impressions. For example, the support team can display a list of books the user has read, allowing them to review what books they have read at any time.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, recommendation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user input. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable book based on the user's interests and reading history using a generating AI. The support unit is implemented by the control unit 46A of the smart device 14 and supports the user's reading progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, recommendation unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user input. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable book based on the user's interests and reading history using a generating AI. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports the user's reading progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, recommendation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user input. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable book based on the user's interests and reading history using a generating AI. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports the user's reading progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, recommendation unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user input. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and recommends the most suitable book based on the user's interests and reading history using a generating AI. The support unit is implemented by, for example, the control unit 46A of the robot 414 and supports the user's reading progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception area that receives user input, Based on the information received by the aforementioned reception department, the recommendation department recommends the most suitable books, The system includes a support unit that supports the reading progress of books recommended by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) The aforementioned recommendation department, The AI ​​generates recommendations for the most suitable books based on the user's interests and reading history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is It learns the user's reading pace and suggests books at a comfortable pace. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is It tracks the user's reading progress and suggests the next book they should read. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is It provides a note-taking function that allows users to organize books they have read, their summaries, and their impressions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We will link with the library's database to make books available for free. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system filters the data based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making a recommendation, adjust the level of detail based on the importance of the book. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the book category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, we prioritize them based on the book's publication date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the books. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is It estimates the user's emotions and adjusts how reading progress is supported based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is When providing reading progress support, the system analyzes the user's past reading history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is When providing reading progress support, customize the support methods based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is It estimates the user's emotions and prioritizes reading progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is When providing reading progress support, the system selects the optimal support method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is When providing reading progress support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, Based on the information received by the aforementioned reception department, the recommendation department recommends the most suitable books, The system includes a support unit that supports the reading progress of books recommended by the aforementioned recommendation unit. A system characterized by the following features.

2. The aforementioned recommendation department, The AI ​​generates recommendations for the most suitable books based on the user's interests and reading history. The system according to feature 1.

3. The aforementioned support unit is It learns the user's reading pace and suggests books at a comfortable pace. The system according to feature 1.

4. The aforementioned support unit is It tracks the user's reading progress and suggests the next book they should read. The system according to feature 1.

5. The aforementioned support unit is It provides a note-taking function that allows users to organize books they have read, their summaries, and their impressions. The system according to feature 1.

6. The aforementioned support unit is We will link with the library's database to make books available for free. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, the system filters the data based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

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