System

Abstract

To provide a system according to an embodiment that proposes a book to be read next on the basis of the history of a user's reading and the user's preference.SOLUTION: A system comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an arrangement unit. The collection unit collects information on a user's reading. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a book on the basis of a result of the analysis performed by the analysis unit. The provision unit provides information according to a question from the user. The arrangement unit attaches tags to portions highlighted by the user and memos and categorizes and arranges the tagged portions and memos.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately suggest the next book to read based on a user's reading history and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest the next book to read based on the user's reading history and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a provision unit, and an organization unit. The collection unit collects information related to a user's reading. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests books based on the analysis results by the analysis unit. The provision unit provides information in response to a user's question. The organization unit tags the portions and notes highlighted by the user, and organizes them into categories. Effect of the Invention

[0007] The system according to the embodiment can suggest the next book to read based on the user's reading history and preferences. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The reading assistant system according to the embodiment of the present invention is a system that analyzes a user's reading history and preferences, and suggests a book to be read next. The system collects and analyzes information about the user's reading, and identifies the user's preferences. Based on the analysis results, the system suggests a book to be read next. Also, information and summaries about the contents of the book are provided in response to the user's questions. Furthermore, the system organizes and saves the parts and notes highlighted by the user. For example, the reading assistant system collects information such as the user's reading history, book evaluation history, and reading genre tendencies. This information is collected by a collection unit. Next, the collected information is analyzed. The analysis unit analyzes the collected data and identifies the user's preferences. For example, the user's preferences are identified based on the genres and evaluations of books that the user has read in the past. Based on the analysis results, the system suggests a book to be read next. Based on the analysis results, the suggestion unit suggests a book that is optimal for the user. For example, books in a genre that the user has highly rated in the past are suggested. Also, information and summaries about the contents of the book are provided in response to the user's questions. The provision unit provides information and summaries about the contents of the book based on the user's questions. For example, if a user asks a question about a specific book, a summary of the book is provided. Furthermore, the part highlighted by the user and the notes are organized and saved. The organizing unit tags the part highlighted by the user and the notes, categorizes them, and organizes them. This allows the user to efficiently manage his / her notes. This mechanism allows the user to find the most suitable book based on his / her reading history and preferences. In addition, the reading experience of the user can be improved by providing information and summaries about the contents of the book in response to the user's questions. Furthermore, the part highlighted by the user and the notes are organized and saved, and the user can efficiently manage his / her notes. This allows the reading assistant system to suggest the most suitable book based on the user's reading history and preferences, and improve the reading experience.

[0029] The reading assistant system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a provision unit, and an organization unit. The collection unit collects information on the user's reading. The information on the user's reading includes, for example, a reading history, a book evaluation history, a reading genre tendency, and the like, but is not limited to such examples. The collection unit collects information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the books read by the user on the e-book reader. The collection unit can also collect information on the evaluations and reviews given by the user to the books. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and evaluations of books read by the user in the past. The analysis unit can identify the user's preferences using data mining technology or machine learning algorithms. The suggestion unit suggests the next book to read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating to. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. The provision unit provides information and summaries regarding the contents of the book in response to the user's question. The provision unit uses AI to provide information and summaries regarding the contents of the book based on the user's question. For example, when the user asks about a specific book, the provision unit provides a summary of the book. The organizing unit organizes and saves the parts and notes highlighted by the user. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. This allows the user to efficiently manage his or her notes. This allows the reading assistant system according to the embodiment to suggest the most suitable book based on the user's reading history and preferences, improving the reading experience.

[0030] The collection unit collects information on the user's reading. The information on the user's reading includes, for example, a reading history, a book evaluation history, a reading genre tendency, and the like, but is not limited to such examples. The collection unit collects, for example, information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the books read by the user on the e-book reader. The collection unit can also collect information on the evaluations and reviews given by the user to the books. Specifically, data is acquired in real time from the e-book reader and the smartphone app, and detailed information such as which book the user read for how long, which parts were highlighted, and what notes were left. Furthermore, the collection unit can also collect the history of books purchased by the user from an online bookstore and information on books borrowed from a library. This allows a comprehensive understanding of the user's entire reading activity. The collection unit stores this information on a cloud server and makes it accessible to the analysis unit and the suggestion unit. In addition, the collection unit can encrypt and anonymize data to protect the user's privacy. This enables the collection unit to collect information about the user's reading activities efficiently and safely, thereby improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology and machine learning algorithms to identify the user's preferences. Specifically, the analysis unit uses clustering and classification algorithms to analyze the collected data and extract the user's reading patterns and tendencies. For example, if a user frequently reads books of a specific genre, it is determined that the user has a high interest in that genre. In addition, the analysis unit can analyze the characteristics of books that the user has highly rated and identify common themes and styles. Furthermore, the analysis unit can use natural language processing technology to analyze the user's reviews and comments and perform sentiment analysis. This makes it possible to understand what elements the user is responding positively to. Based on this information, the analysis unit identifies the user's preferences in detail and provides them to the suggestion unit. As a result, the analysis unit can provide important information to improve the user's reading experience and increase the accuracy and effectiveness of the entire system.

[0032] The suggestion unit suggests the next book to be read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating to. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. Specifically, the suggestion unit selects the most suitable book for the user using collaborative filtering or a content-based recommendation algorithm. Collaborative filtering suggests books that users with similar preferences have given a high rating to based on the ratings and reading history of other users. On the other hand, content-based recommendation suggests similar books based on the contents and characteristics of books that the user has read in the past. Furthermore, the suggestion unit can update the user's reading history and rating history in real time and make suggestions based on the latest information. For example, when a user inputs a rating for a newly read book, the suggestion unit immediately reflects that information and updates the suggestions for the next book to be read. The suggestion unit can also collect user feedback and continuously improve the accuracy of the suggestion algorithm. This allows the suggestion unit to always suggest the most suitable book to the user and improve the reading experience.

[0033] The providing unit provides information and summaries about the contents of the book in response to the user's question. The providing unit uses AI to provide information and summaries about the contents of the book based on the user's question. For example, when the user asks about a specific book, the providing unit provides a summary of the book. Specifically, the providing unit uses natural language processing technology to analyze the contents of the book and generate a summary. When the user asks about a specific chapter or paragraph, the providing unit can extract and provide a summary of that part or important points. In addition, when the user asks about a specific theme or topic, the providing unit can suggest related books and chapters and summarize and provide their contents. Furthermore, the providing unit can respond interactively to the user's questions. For example, when the user asks an additional question, the providing unit provides appropriate information for that question as well, supporting the user to deepen their understanding. The providing unit provides this information in real time, allowing the user to quickly obtain the information they need. This allows the providing unit to accurately and quickly respond to the user's questions and improve the reading experience.

[0034] The organizing unit organizes and saves the parts and notes highlighted by the user. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. Specifically, the organizing unit uses natural language processing technology to analyze the content of the text highlighted by the user and automatically generates related tags. For example, if the user highlights a part related to a specific theme or topic, the tag related to that theme is attached and saved. In addition, when the user takes a note, the organizing unit can analyze the content of the note and classify it into a related category. Furthermore, the organizing unit creates an index to make it easier for the user to search the parts and notes highlighted by the user. This allows the user to easily search for parts and notes highlighted in the past and quickly obtain the information he or she needs. The organizing unit stores this information on a cloud server, allowing the user to access it from any device. In addition, the organizing unit can encrypt data and control access to protect the user's privacy. This allows the organizing unit to efficiently and safely organize the user's highlights and notes, improving the reading experience.

[0035] The reading assistant system includes a function for protecting the privacy of the user. The function for privacy protection includes, but is not limited to, data encryption, anonymization technology, and access control. The function for privacy protection may be realized using AI. For example, the function for privacy protection encrypts the user's data so that a third party cannot access the data. The function for privacy protection can also anonymize the user's data so that the individual cannot be identified. Furthermore, the function for privacy protection can also perform access control so that only specific users can access the data. This can protect the user's privacy.

[0036] The collection unit can collect the user's reading history, book evaluation history, or reading genre tendency. The collection unit collects, for example, information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the book read by the user on the e-book reader. The collection unit can also collect information on the evaluation and review given by the user to the book. This makes it possible to collect detailed information on the user's reading. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input information on the book read by the user to AI, and the AI ​​can automatically collect the data.

[0037] The analysis unit can analyze the collected data and identify the user's preferences. The analysis unit can use AI to analyze the collected data and identify the user's preferences. For example, the analysis unit can identify the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology or machine learning algorithms to identify the user's preferences. This makes it possible to suggest more appropriate books by identifying the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can automatically analyze the data.

[0038] The suggestion unit can suggest the next book to read based on the analysis result. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. This makes it possible to suggest the most suitable book to the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the analysis result to AI, and the AI ​​can automatically suggest the next book to read.

[0039] The providing unit can provide information or a summary about the contents of the book based on the user's question. The providing unit uses AI to provide information or a summary about the contents of the book based on the user's question. For example, when a user asks a question about a specific book, the providing unit provides a summary of the book. This makes it possible to provide information about the contents of the book in response to the user's question. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input a user's question into AI, and the AI ​​can automatically provide information or a summary about the contents of the book.

[0040] The organizing unit can tag the parts and notes highlighted by the user, categorize them, and organize them. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. This allows the user's notes to be managed efficiently. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit can input the parts highlighted by the user into AI, and the AI ​​can automatically tag, categorize, and organize them.

[0041] The suggestion unit can determine the number of pages of a book to be suggested based on the user's reading speed. The suggestion unit uses AI to determine the number of pages of a book to be suggested based on the user's reading speed. For example, if the user reads quickly, the suggestion unit suggests a book that can be read in a short time. Also, if the user reads slowly, the suggestion unit can suggest a book that can be read over a long time. This makes it possible to suggest books according to the user's reading speed. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's reading speed data into AI, and the AI ​​can automatically suggest a book with an appropriate number of pages.

[0042] The collection unit can collect information on books shared by users on social media. The collection unit uses AI to collect information on books shared by users on social media. For example, the collection unit collects titles and comments on books shared by users on social media. The collection unit can also collect information on authors and reading groups that users follow on social media. This makes it possible to collect information shared on social media. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input social media data into AI, and the AI ​​can automatically collect related information.

[0043] The suggestion unit can preferentially suggest audiobooks based on the user's reading tendency. The suggestion unit preferentially suggests audiobooks based on the user's reading tendency using AI. For example, if the user has used audiobooks with a preference in the past, the suggestion unit preferentially suggests audiobooks. In addition, if the user likes audiobooks of a specific genre, the suggestion unit can also suggest audiobooks of that genre. This makes it possible to suggest audiobooks according to the user's reading tendency. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's reading tendency data into AI, and the AI ​​can automatically suggest audiobooks.

[0044] The collection unit can analyze the user's past reading history and select the optimal acquisition method. The collection unit uses AI to analyze the user's past reading history and select the optimal acquisition method. For example, the collection unit preferentially acquires books in a genre that the user frequently reads. The collection unit can also preferentially acquire information on books that the user has given high ratings. Furthermore, the collection unit can also preferentially acquire information on books that the user has read for a long time. This makes it possible to select the optimal acquisition method based on the user's past reading history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's past reading history data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0045] The collection unit can perform filtering based on the user's current field of interest when acquiring the reading history. The collection unit uses AI to perform filtering based on the user's current field of interest when acquiring the reading history. For example, the collection unit preferentially acquires books in a genre in which the user has recently been interested. The collection unit can also acquire books related to keywords recently searched by the user. Furthermore, the collection unit can also acquire information related to books recently purchased by the user. This makes it possible to filter the reading history based on the user's current field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's current field of interest data into AI, and the AI ​​can automatically perform filtering.

[0046] The collection unit can preferentially acquire a history with high relevance in consideration of the geographical location information of the user when acquiring a reading history. The collection unit uses AI to preferentially acquire a history with high relevance in consideration of the geographical location information of the user when acquiring a reading history. For example, when the user is in a specific area, the collection unit preferentially acquires a history of books related to the area. In addition, when the user is traveling, the collection unit can also preferentially acquire a history of books related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially acquire a history of books related to the home. This makes it possible to acquire a history with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically acquire a history with high relevance.

[0047] The collection unit can analyze the user's social media activities when acquiring the reading history and acquire related history. The collection unit uses AI to analyze the user's social media activities when acquiring the reading history and acquire related history. For example, the collection unit acquires information on books shared by the user on social media. The collection unit can also acquire the history of books by authors the user follows on social media. Furthermore, the collection unit can also acquire information on reading groups in which the user participates on social media. This makes it possible to acquire related history based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, and the AI ​​can automatically acquire related history.

[0048] The analysis unit can optimize the analysis algorithm by referring to the user's past reading history during the analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past reading history using AI during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the genre of books the user has read in the past. The analysis unit can also optimize the analysis algorithm based on the characteristics of books that the user has highly rated. Furthermore, the analysis unit can adjust the analysis algorithm based on the pattern of books that the user has read for a long time. This makes it possible to optimize the analysis algorithm based on the user's past reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's past reading history data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0049] The analysis unit can perform the analysis while taking into consideration the user's reading speed and reading time. The analysis unit performs the analysis while taking into consideration the user's reading speed and reading time using AI. For example, when the user reads quickly, the analysis unit prioritizes the analysis of books that can be read in a short time. In addition, when the user reads slowly, the analysis unit can also prioritize the analysis of books that can be read over a long time. Furthermore, when the user reads during a specific time period, the analysis unit can also analyze books that are suitable for that time period. This makes it possible to perform the analysis based on the user's reading speed and reading time. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's reading speed and reading time data into AI, and the AI ​​can automatically perform the analysis.

[0050] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. The analysis unit performs the analysis while taking into consideration the geographical location information of the user using AI. For example, when the user is in a specific area, the analysis unit analyzes books related to the area. In addition, when the user is traveling, the analysis unit can also analyze books related to the travel destination. Furthermore, when the user is at home, the analysis unit can also analyze books related to the home. This allows the analysis to be performed based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically perform the analysis.

[0051] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the user's social media activity during the analysis. For example, the analysis unit performs the analysis based on information of books shared by the user on social media. The analysis unit can also perform the analysis based on information of books by authors the user follows on social media. Furthermore, the analysis unit can also perform the analysis based on information of reading groups the user participates in on social media. This makes it possible to improve the accuracy of the analysis based on the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's social media activity data into AI, and the AI ​​can automatically improve the accuracy of the analysis.

[0052] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the book at the time of suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the book at the time of suggestion using AI. For example, the suggestion unit makes detailed suggestions for highly rated books. The suggestion unit can also make simplified suggestions for newly published books. Furthermore, the suggestion unit can also make special suggestions for popular books. This makes it possible to adjust the level of detail of the suggestion according to the importance of the book. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book importance data to AI, and the AI ​​can automatically adjust the level of detail of the suggestion.

[0053] The suggestion unit can apply different suggestion algorithms depending on the book category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the book category at the time of suggestion. For example, the suggestion unit makes a suggestion that emphasizes the story for novels. The suggestion unit can also make a suggestion that emphasizes practicality for business books. Furthermore, the suggestion unit can also make a suggestion that emphasizes motivation for self-help books. This makes it possible to make suggestions according to the book category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book category data to AI, and the AI ​​can automatically apply different suggestion algorithms.

[0054] The suggestion unit can determine the priority of the suggestions based on the publication date of the book at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestions based on the publication date of the book at the time of suggestion. For example, the suggestion unit preferentially suggests new books. The suggestion unit can also suggest classic books at a specific timing. Furthermore, the suggestion unit can also preferentially suggest sequels to books that the user has read in the past. This makes it possible to determine the priority of the suggestions based on the publication date of the books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input publication date data of books into AI, and the AI ​​can automatically determine the priority of the suggestions.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the books when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the books when making suggestions using AI. For example, the suggestion unit preferentially suggests books that are highly relevant to books that the user has read in the past. The suggestion unit can also preferentially suggest books that are highly relevant to books that the user has given a high rating to. Furthermore, the suggestion unit can also preferentially suggest books that are highly relevant to books that the user has read for a long time. This makes it possible to adjust the order of suggestions based on the relevance of the books. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book relevance data to AI, and the AI ​​can automatically adjust the order of suggestions.

[0056] The providing unit can adjust the level of detail of the information based on the importance of the book when providing the information. The providing unit adjusts the level of detail of the information based on the importance of the book when providing the information using AI. For example, the providing unit provides detailed information for highly rated books. The providing unit can also provide simplified information for newly published books. Furthermore, the providing unit can also provide special information for popular books. This makes it possible to adjust the level of detail of the information according to the importance of the book. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the book to AI, and the AI ​​can automatically adjust the level of detail of the information.

[0057] The providing unit can apply different information provision algorithms depending on the book category at the time of provision. The providing unit uses AI to apply different information provision algorithms depending on the book category at the time of provision. For example, the providing unit provides a summary of the story for a novel. The providing unit can also provide main points for a business book. Furthermore, the providing unit can provide information that increases motivation for a self-help book. This makes it possible to provide information according to the book category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input book category data into AI, and the AI ​​can automatically apply different information provision algorithms.

[0058] The providing unit can determine the priority of information based on the publication date of the book at the time of providing. The providing unit uses AI to determine the priority of information based on the publication date of the book at the time of providing. For example, the providing unit provides information on newly published books preferentially. The providing unit can also provide information on classic books at a specific timing. Furthermore, the providing unit can also provide information on sequels to books that the user has read in the past preferentially. This makes it possible to determine the priority of information based on the publication date of the book. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input publication date data of books into AI, and the AI ​​can automatically determine the priority of information.

[0059] The providing unit can adjust the order of information based on the relevance of books at the time of providing. The providing unit adjusts the order of information based on the relevance of books using AI at the time of providing. For example, the providing unit preferentially provides information on books that are highly relevant to books that the user has read in the past. The providing unit can also preferentially provide information on books that are highly relevant to books that the user has given high ratings to. Furthermore, the providing unit can also preferentially provide information on books that are highly relevant to books that the user has read for a long time. This makes it possible to adjust the order of information based on the relevance of books. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input book relevance data to AI, and the AI ​​can automatically adjust the order of information.

[0060] The organizing unit can select the optimal organizing method by referring to the user's past highlights and notes when organizing. The organizing unit uses AI to select the optimal organizing method by referring to the user's past highlights and notes when organizing. For example, the organizing unit adjusts the organizing method based on the parts that the user has highlighted in the past. The organizing unit can also optimize the organizing method based on the contents of the user's past notes. Furthermore, the organizing unit can adjust the organizing method based on information that the user has tagged in the past. This makes it possible to select the optimal organizing method based on the user's past highlights and notes. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's past highlights and memo data into AI, and the AI ​​can automatically select the optimal organizing method.

[0061] The organizing unit can organize the data by considering the user's reading speed and reading time. The organizing unit uses AI to organize the data by considering the user's reading speed and reading time. For example, when the user reads quickly, the organizing unit selects a method that can organize the data in a short time. In addition, when the user reads slowly, the organizing unit can select a detailed organizing method. Furthermore, when the user reads during a specific time period, the organizing unit can select an organizing method suitable for that time period. This allows the data to be organized based on the user's reading speed and reading time. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's reading speed and reading time data into AI, and the AI ​​can automatically organize the data.

[0062] The organizing unit can select the optimal organizing method in consideration of the geographical location information of the user when organizing. The organizing unit uses AI to select the optimal organizing method in consideration of the geographical location information of the user when organizing. For example, when the user is in a specific area, the organizing unit prioritizes organizing highlights and notes related to the area. In addition, when the user is traveling, the organizing unit can also prioritize organizing highlights and notes related to the travel destination. Furthermore, when the user is at home, the organizing unit can also prioritize organizing highlights and notes related to the home. This makes it possible to select the optimal organizing method based on the geographical location information of the user. Part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically select the optimal organizing method.

[0063] The organizing unit can improve the accuracy of the organization by referring to the user's social media activity when organizing. The organizing unit uses AI to improve the accuracy of the organization by referring to the user's social media activity when organizing. For example, the organizing unit organizes based on highlights and notes shared by the user on social media. The organizing unit can also organize based on information on authors the user follows on social media. Furthermore, the organizing unit can organize based on information on reading groups the user participates in on social media. This makes it possible to improve the accuracy of the organization based on the user's social media activity. A part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's social media activity data into AI, and the AI ​​can automatically improve the accuracy of the organization.

[0064] The protection unit can select the optimal protection method by referring to the user's past privacy settings at the time of protection. The protection unit uses AI to select the optimal protection method by referring to the user's past privacy settings at the time of protection. For example, the protection unit adjusts the protection method based on the privacy protection level set by the user in the past. The protection unit can also optimize the protection method based on the privacy settings selected by the user in the past. Furthermore, the protection unit can adjust the protection method based on the privacy protection function used by the user in the past. This makes it possible to select the optimal protection method based on the user's past privacy settings. A part or all of the above-mentioned processing in the protection unit may be performed using AI or may be performed without using AI. For example, the protection unit inputs the user's past privacy setting data into AI, and the AI ​​can automatically select the optimal protection method.

[0065] The protection unit can select the optimal protection method in consideration of the geographical location information of the user at the time of protection. The protection unit uses AI to select the optimal protection method in consideration of the geographical location information of the user at the time of protection. For example, when the user is in a specific area, the protection unit prioritizes privacy protection related to the area. In addition, when the user is traveling, the protection unit can also prioritize privacy protection related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize privacy protection related to the home. This makes it possible to select the optimal protection method based on the geographical location information of the user. Part or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically select the optimal protection method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reading assistant system can also monitor the user's reading environment and suggest optimal reading times. For example, the system can detect the noise level and lighting conditions around the user and recommend reading in a quiet environment. It can also analyze that the user is more likely to concentrate during certain times of the day and suggest reading at those times. Furthermore, the system can take the user's schedule into account and suggest a time that is suitable for reading. This allows the user to enjoy reading in the optimal environment and time.

[0068] The reading assistant system can set reading goals for the user based on the user's reading history. For example, the system can set a goal for the user to read how many books per month and track the progress. The system can also set a goal for the user to read a certain genre of books within a certain period of time. In addition, the system can send reminders for the goals set by the user to help them achieve their goals. This makes it easier for the user to maintain their motivation to read.

[0069] The reading assistant system can analyze the user's reading history and visualize the reading tendency. For example, the system can display the genre and rating of the books the user has read in the past in graphs and charts. It can also display a timeline of how many books the user has read in a specific period. Furthermore, it can analyze the characteristics of the books that the user has given high ratings and visually display the results. This allows the user to understand his or her reading tendency at a glance.

[0070] The reading assistant system can provide advice to improve a user's reading habits based on the user's reading history. For example, the system can analyze the time the user spends reading and suggest efficient reading methods. If the user is biased towards a particular genre, the system can also suggest books in other genres. Furthermore, if the user tends to stop reading, the system can send reminders to encourage the user to continue reading. This allows the user to develop better reading habits.

[0071] The reading assistant system can track the user's reading progress based on the user's reading history and support goal achievement. For example, the system can display the progress of the user toward the reading goal set by the user. If the user is approaching the goal, the system can also send encouraging messages. Furthermore, if the user is moving away from the goal, the system can provide advice for improvement. This makes it easier for the user to achieve the reading goal.

[0072] The process flow of the first embodiment will be briefly described below.

[0073] Step 1: The collection unit collects information about the user's reading. Specifically, it collects information such as the user's reading history, book rating history, reading genre tendencies, titles and authors of books read, reading time, and reading frequency. The collection unit can also use AI to automatically collect the user's reading history. For example, it collects information about books read on an e-book reader, and information about ratings and reviews given to books by users. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology and machine learning algorithms to identify the user's preferences. Step 3: The suggestion unit suggests the next book to read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the best book for the user. For example, it suggests books in a genre that the user has previously rated highly. It can also determine the number of pages of the book to suggest based on the user's reading speed. Step 4: The provider uses AI to provide information and summaries about the book's contents based on the user's question. For example, if a user asks about a specific book, the provider provides a summary of that book. Step 5: The organizer organizes and saves the parts and notes highlighted by the user. The organizer uses AI to tag, categorize, and organize the parts and notes highlighted by the user. For example, the parts highlighted by the user are automatically tagged, categorized, and saved. This allows the user to manage their notes efficiently.

[0074] (Example 2) The reading assistant system according to the embodiment of the present invention is a system that analyzes a user's reading history and preferences, and suggests a book to be read next. The system collects and analyzes information about the user's reading, and identifies the user's preferences. Based on the analysis results, the system suggests a book to be read next. Also, information and summaries about the contents of the book are provided in response to the user's questions. Furthermore, the system organizes and saves the parts and notes highlighted by the user. For example, the reading assistant system collects information such as the user's reading history, book evaluation history, and reading genre tendencies. This information is collected by a collection unit. Next, the collected information is analyzed. The analysis unit analyzes the collected data and identifies the user's preferences. For example, the user's preferences are identified based on the genres and evaluations of books that the user has read in the past. Based on the analysis results, the system suggests a book to be read next. Based on the analysis results, the suggestion unit suggests a book that is optimal for the user. For example, books in a genre that the user has highly rated in the past are suggested. Also, information and summaries about the contents of the book are provided in response to the user's questions. The provision unit provides information and summaries about the contents of the book based on the user's questions. For example, if a user asks a question about a specific book, a summary of the book is provided. Furthermore, the part highlighted by the user and the notes are organized and saved. The organizing unit tags the part highlighted by the user and the notes, categorizes them, and organizes them. This allows the user to efficiently manage his / her notes. This mechanism allows the user to find the most suitable book based on his / her reading history and preferences. In addition, the reading experience of the user can be improved by providing information and summaries about the contents of the book in response to the user's questions. Furthermore, the part highlighted by the user and the notes are organized and saved, and the user can efficiently manage his / her notes. This allows the reading assistant system to suggest the most suitable book based on the user's reading history and preferences, and improve the reading experience.

[0075] The reading assistant system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a provision unit, and an organization unit. The collection unit collects information on the user's reading. The information on the user's reading includes, for example, a reading history, a book evaluation history, a reading genre tendency, and the like, but is not limited to such examples. The collection unit collects information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the books read by the user on the e-book reader. The collection unit can also collect information on the evaluations and reviews given by the user to the books. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and evaluations of books read by the user in the past. The analysis unit can identify the user's preferences using data mining technology or machine learning algorithms. The suggestion unit suggests the next book to read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating to. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. The provision unit provides information and summaries regarding the contents of the book in response to the user's question. The provision unit uses AI to provide information and summaries regarding the contents of the book based on the user's question. For example, when the user asks about a specific book, the provision unit provides a summary of the book. The organizing unit organizes and saves the parts and notes highlighted by the user. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. This allows the user to efficiently manage his or her notes. This allows the reading assistant system according to the embodiment to suggest the most suitable book based on the user's reading history and preferences, improving the reading experience.

[0076] The collection unit collects information on the user's reading. The information on the user's reading includes, for example, a reading history, a book evaluation history, a reading genre tendency, and the like, but is not limited to such examples. The collection unit collects, for example, information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the books read by the user on the e-book reader. The collection unit can also collect information on the evaluations and reviews given by the user to the books. Specifically, data is acquired in real time from the e-book reader and the smartphone app, and detailed information such as which book the user read for how long, which parts were highlighted, and what notes were left. Furthermore, the collection unit can also collect the history of books purchased by the user from an online bookstore and information on books borrowed from a library. This allows a comprehensive understanding of the user's entire reading activity. The collection unit stores this information on a cloud server and makes it accessible to the analysis unit and the suggestion unit. In addition, the collection unit can encrypt and anonymize data to protect the user's privacy. This enables the collection unit to collect information about the user's reading activities efficiently and safely, thereby improving the performance of the entire system.

[0077] The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology and machine learning algorithms to identify the user's preferences. Specifically, the analysis unit uses clustering and classification algorithms to analyze the collected data and extract the user's reading patterns and tendencies. For example, if a user frequently reads books of a specific genre, it is determined that the user has a high interest in that genre. In addition, the analysis unit can analyze the characteristics of books that the user has highly rated and identify common themes and styles. Furthermore, the analysis unit can use natural language processing technology to analyze the user's reviews and comments and perform sentiment analysis. This makes it possible to understand what elements the user is responding positively to. Based on this information, the analysis unit identifies the user's preferences in detail and provides them to the suggestion unit. As a result, the analysis unit can provide important information to improve the user's reading experience and increase the accuracy and effectiveness of the entire system.

[0078] The suggestion unit suggests the next book to be read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating to. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. Specifically, the suggestion unit selects the most suitable book for the user using collaborative filtering or a content-based recommendation algorithm. Collaborative filtering suggests books that users with similar preferences have given a high rating to based on the ratings and reading history of other users. On the other hand, content-based recommendation suggests similar books based on the contents and characteristics of books that the user has read in the past. Furthermore, the suggestion unit can update the user's reading history and rating history in real time and make suggestions based on the latest information. For example, when a user inputs a rating for a newly read book, the suggestion unit immediately reflects that information and updates the suggestions for the next book to be read. The suggestion unit can also collect user feedback and continuously improve the accuracy of the suggestion algorithm. This allows the suggestion unit to always suggest the most suitable book to the user and improve the reading experience.

[0079] The providing unit provides information and summaries about the contents of the book in response to the user's question. The providing unit uses AI to provide information and summaries about the contents of the book based on the user's question. For example, when the user asks about a specific book, the providing unit provides a summary of the book. Specifically, the providing unit uses natural language processing technology to analyze the contents of the book and generate a summary. When the user asks about a specific chapter or paragraph, the providing unit can extract and provide a summary of that part or important points. In addition, when the user asks about a specific theme or topic, the providing unit can suggest related books and chapters and summarize and provide their contents. Furthermore, the providing unit can respond interactively to the user's questions. For example, when the user asks an additional question, the providing unit provides appropriate information for that question as well, supporting the user to deepen their understanding. The providing unit provides this information in real time, allowing the user to quickly obtain the information they need. This allows the providing unit to accurately and quickly respond to the user's questions and improve the reading experience.

[0080] The organizing unit organizes and saves the parts and notes highlighted by the user. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. Specifically, the organizing unit uses natural language processing technology to analyze the content of the text highlighted by the user and automatically generates related tags. For example, if the user highlights a part related to a specific theme or topic, the tag related to that theme is attached and saved. In addition, when the user takes a note, the organizing unit can analyze the content of the note and classify it into a related category. Furthermore, the organizing unit creates an index to make it easier for the user to search the parts and notes highlighted by the user. This allows the user to easily search for parts and notes highlighted in the past and quickly obtain the information he or she needs. The organizing unit stores this information on a cloud server, allowing the user to access it from any device. In addition, the organizing unit can encrypt data and control access to protect the user's privacy. This allows the organizing unit to efficiently and safely organize the user's highlights and notes, improving the reading experience.

[0081] The reading assistant system includes a function for protecting the privacy of the user. The function for privacy protection includes, but is not limited to, data encryption, anonymization technology, and access control. The function for privacy protection may be realized using AI. For example, the function for privacy protection encrypts the user's data so that a third party cannot access the data. The function for privacy protection can also anonymize the user's data so that the individual cannot be identified. Furthermore, the function for privacy protection can also perform access control so that only specific users can access the data. This can protect the user's privacy.

[0082] The collection unit can collect the user's reading history, book evaluation history, or reading genre tendency. The collection unit collects, for example, information such as the title and author name of the book read by the user, the reading time, and the reading frequency. The collection unit can also automatically collect the user's reading history using AI. For example, the collection unit automatically acquires information on the book read by the user on the e-book reader. The collection unit can also collect information on the evaluation and review given by the user to the book. This makes it possible to collect detailed information on the user's reading. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input information on the book read by the user to AI, and the AI ​​can automatically collect the data.

[0083] The analysis unit can analyze the collected data and identify the user's preferences. The analysis unit can use AI to analyze the collected data and identify the user's preferences. For example, the analysis unit can identify the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology or machine learning algorithms to identify the user's preferences. This makes it possible to suggest more appropriate books by identifying the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can automatically analyze the data.

[0084] The suggestion unit can suggest the next book to read based on the analysis result. The suggestion unit uses AI to suggest the most suitable book to the user. For example, the suggestion unit suggests books in a genre that the user has previously given a high rating. The suggestion unit can also determine the number of pages of the book to be suggested based on the user's reading speed. This makes it possible to suggest the most suitable book to the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the analysis result to AI, and the AI ​​can automatically suggest the next book to read.

[0085] The providing unit can provide information or a summary about the contents of the book based on the user's question. The providing unit uses AI to provide information or a summary about the contents of the book based on the user's question. For example, when a user asks a question about a specific book, the providing unit provides a summary of the book. This makes it possible to provide information about the contents of the book in response to the user's question. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input a user's question into AI, and the AI ​​can automatically provide information or a summary about the contents of the book.

[0086] The organizing unit can tag the parts and notes highlighted by the user, categorize them, and organize them. The organizing unit uses AI to tag the parts and notes highlighted by the user, categorize them, and organize them. For example, the organizing unit automatically tags the parts highlighted by the user, categorizes them, and saves them. This allows the user's notes to be managed efficiently. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit can input the parts highlighted by the user into AI, and the AI ​​can automatically tag, categorize, and organize them.

[0087] The suggestion unit can determine the number of pages of a book to be suggested based on the user's reading speed. The suggestion unit uses AI to determine the number of pages of a book to be suggested based on the user's reading speed. For example, if the user reads quickly, the suggestion unit suggests a book that can be read in a short time. Also, if the user reads slowly, the suggestion unit can suggest a book that can be read over a long time. This makes it possible to suggest books according to the user's reading speed. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's reading speed data into AI, and the AI ​​can automatically suggest a book with an appropriate number of pages.

[0088] The collection unit can collect information on books shared by users on social media. The collection unit uses AI to collect information on books shared by users on social media. For example, the collection unit collects titles and comments on books shared by users on social media. The collection unit can also collect information on authors and reading groups that users follow on social media. This makes it possible to collect information shared on social media. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input social media data into AI, and the AI ​​can automatically collect related information.

[0089] The suggestion unit can preferentially suggest audiobooks based on the user's reading tendency. The suggestion unit preferentially suggests audiobooks based on the user's reading tendency using AI. For example, if the user has used audiobooks with a preference in the past, the suggestion unit preferentially suggests audiobooks. In addition, if the user likes audiobooks of a specific genre, the suggestion unit can also suggest audiobooks of that genre. This makes it possible to suggest audiobooks according to the user's reading tendency. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the user's reading tendency data into AI, and the AI ​​can automatically suggest audiobooks.

[0090] The collection unit can estimate the user's emotion and adjust the timing of acquiring the reading history based on the estimated user's emotion. The collection unit uses AI to estimate the user's emotion and adjust the timing of acquiring the reading history based on the estimated user's emotion. For example, the collection unit immediately acquires the reading history when the user is relaxed. In addition, the collection unit can also postpone the acquisition of the reading history when the user is stressed. Furthermore, the collection unit can also not interrupt the acquisition of the reading history when the user is concentrating. This makes it possible to adjust the timing of acquiring the reading history according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's emotion data into AI, and the AI ​​can automatically adjust the timing of acquiring the reading history.

[0091] The collection unit can analyze the user's past reading history and select the optimal acquisition method. The collection unit uses AI to analyze the user's past reading history and select the optimal acquisition method. For example, the collection unit preferentially acquires books in a genre that the user frequently reads. The collection unit can also preferentially acquire information on books that the user has given high ratings. Furthermore, the collection unit can also preferentially acquire information on books that the user has read for a long time. This makes it possible to select the optimal acquisition method based on the user's past reading history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's past reading history data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0092] The collection unit can perform filtering based on the user's current field of interest when acquiring the reading history. The collection unit uses AI to perform filtering based on the user's current field of interest when acquiring the reading history. For example, the collection unit preferentially acquires books in a genre in which the user has recently been interested. The collection unit can also acquire books related to keywords recently searched by the user. Furthermore, the collection unit can also acquire information related to books recently purchased by the user. This makes it possible to filter the reading history based on the user's current field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's current field of interest data into AI, and the AI ​​can automatically perform filtering.

[0093] The collection unit can estimate the user's emotion and determine the priority of the reading history to be acquired based on the estimated user's emotion. The collection unit uses AI to estimate the user's emotion and determine the priority of the reading history to be acquired based on the estimated user's emotion. For example, when the user is relaxed, the collection unit preferentially acquires the latest reading history. In addition, when the user is stressed, the collection unit can also preferentially acquire past reading history. Furthermore, when the user is concentrating, the collection unit can also preferentially acquire reading history of a specific genre. This makes it possible to determine the priority of the reading history according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the user's emotion data into AI, and the AI ​​can automatically determine the priority of the reading history.

[0094] The collection unit can preferentially acquire a history with high relevance in consideration of the geographical location information of the user when acquiring a reading history. The collection unit uses AI to preferentially acquire a history with high relevance in consideration of the geographical location information of the user when acquiring a reading history. For example, when the user is in a specific area, the collection unit preferentially acquires a history of books related to the area. In addition, when the user is traveling, the collection unit can also preferentially acquire a history of books related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially acquire a history of books related to the home. This makes it possible to acquire a history with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically acquire a history with high relevance.

[0095] The collection unit can analyze the user's social media activities when acquiring the reading history and acquire related history. The collection unit uses AI to analyze the user's social media activities when acquiring the reading history and acquire related history. For example, the collection unit acquires information on books shared by the user on social media. The collection unit can also acquire the history of books by authors the user follows on social media. Furthermore, the collection unit can also acquire information on reading groups in which the user participates on social media. This makes it possible to acquire related history based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, and the AI ​​can automatically acquire related history.

[0096] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit uses AI to estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. Furthermore, the analysis unit can also perform an analysis focusing on a specific genre when the user is concentrating. This makes it possible to adjust the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into AI, and the AI ​​can automatically adjust the analysis method.

[0097] The analysis unit can optimize the analysis algorithm by referring to the user's past reading history during the analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past reading history using AI during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the genre of books the user has read in the past. The analysis unit can also optimize the analysis algorithm based on the characteristics of books that the user has highly rated. Furthermore, the analysis unit can adjust the analysis algorithm based on the pattern of books that the user has read for a long time. This makes it possible to optimize the analysis algorithm based on the user's past reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's past reading history data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0098] The analysis unit can perform the analysis while taking into consideration the user's reading speed and reading time. The analysis unit performs the analysis while taking into consideration the user's reading speed and reading time using AI. For example, when the user reads quickly, the analysis unit prioritizes the analysis of books that can be read in a short time. In addition, when the user reads slowly, the analysis unit can also prioritize the analysis of books that can be read over a long time. Furthermore, when the user reads during a specific time period, the analysis unit can also analyze books that are suitable for that time period. This makes it possible to perform the analysis based on the user's reading speed and reading time. Part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's reading speed and reading time data into AI, and the AI ​​can automatically perform the analysis.

[0099] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit uses AI to estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit displays detailed analysis results when the user is relaxed. In addition, the analysis unit can also display simplified analysis results when the user is stressed. Furthermore, the analysis unit can display analysis results focusing on a specific genre when the user is concentrating. This makes it possible to adjust the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's emotion data into AI, and the AI ​​can automatically adjust the display method of the analysis result.

[0100] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. The analysis unit performs the analysis while taking into consideration the geographical location information of the user using AI. For example, when the user is in a specific area, the analysis unit analyzes books related to the area. In addition, when the user is traveling, the analysis unit can also analyze books related to the travel destination. Furthermore, when the user is at home, the analysis unit can also analyze books related to the home. This allows the analysis to be performed based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically perform the analysis.

[0101] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the user's social media activity during the analysis. For example, the analysis unit performs the analysis based on information of books shared by the user on social media. The analysis unit can also perform the analysis based on information of books by authors the user follows on social media. Furthermore, the analysis unit can also perform the analysis based on information of reading groups the user participates in on social media. This makes it possible to improve the accuracy of the analysis based on the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs the user's social media activity data into AI, and the AI ​​can automatically improve the accuracy of the analysis.

[0102] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, the suggestion unit makes a detailed suggestion when the user is relaxed. In addition, the suggestion unit can also make a simplified suggestion when the user is stressed. Furthermore, the suggestion unit can also make a suggestion focusing on a specific genre when the user is concentrating. This makes it possible to adjust the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's emotion data into AI, and the AI ​​can automatically adjust the expression method of the proposal.

[0103] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the book at the time of suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the book at the time of suggestion using AI. For example, the suggestion unit makes detailed suggestions for highly rated books. The suggestion unit can also make simplified suggestions for newly published books. Furthermore, the suggestion unit can also make special suggestions for popular books. This makes it possible to adjust the level of detail of the suggestion according to the importance of the book. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book importance data to AI, and the AI ​​can automatically adjust the level of detail of the suggestion.

[0104] The suggestion unit can apply different suggestion algorithms depending on the book category at the time of suggestion. The suggestion unit uses AI to apply different suggestion algorithms depending on the book category at the time of suggestion. For example, the suggestion unit makes a suggestion that emphasizes the story for novels. The suggestion unit can also make a suggestion that emphasizes practicality for business books. Furthermore, the suggestion unit can also make a suggestion that emphasizes motivation for self-help books. This makes it possible to make suggestions according to the book category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book category data to AI, and the AI ​​can automatically apply different suggestion algorithms.

[0105] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit uses AI to estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. For example, the suggestion unit makes a detailed proposal when the user is relaxed. The suggestion unit can also make a simplified proposal when the user is stressed. Furthermore, the suggestion unit can also make a proposal focusing on a specific genre when the user is focused. This allows the length of the proposal to be adjusted according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's emotion data into AI, and the AI ​​can automatically adjust the length of the proposal.

[0106] The suggestion unit can determine the priority of the suggestions based on the publication date of the book at the time of suggestion. The suggestion unit uses AI to determine the priority of the suggestions based on the publication date of the book at the time of suggestion. For example, the suggestion unit preferentially suggests new books. The suggestion unit can also suggest classic books at a specific timing. Furthermore, the suggestion unit can also preferentially suggest sequels to books that the user has read in the past. This makes it possible to determine the priority of the suggestions based on the publication date of the books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input publication date data of books into AI, and the AI ​​can automatically determine the priority of the suggestions.

[0107] The suggestion unit can adjust the order of suggestions based on the relevance of the books when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the books when making suggestions using AI. For example, the suggestion unit preferentially suggests books that are highly relevant to books that the user has read in the past. The suggestion unit can also preferentially suggest books that are highly relevant to books that the user has given a high rating to. Furthermore, the suggestion unit can also preferentially suggest books that are highly relevant to books that the user has read for a long time. This makes it possible to adjust the order of suggestions based on the relevance of the books. A part or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input book relevance data to AI, and the AI ​​can automatically adjust the order of suggestions.

[0108] The providing unit can estimate the user's emotion and adjust the expression method of the information to be provided based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the expression method of the information to be provided based on the estimated user's emotion. For example, the providing unit provides detailed information when the user is relaxed. In addition, the providing unit can also provide simplified information when the user is stressed. Furthermore, the providing unit can also provide information focused on a specific genre when the user is concentrating. This makes it possible to adjust the expression method of the information to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI and adjust the expression method of the information to be automatically provided by AI.

[0109] The providing unit can adjust the level of detail of the information based on the importance of the book when providing the information. The providing unit adjusts the level of detail of the information based on the importance of the book when providing the information using AI. For example, the providing unit provides detailed information for highly rated books. The providing unit can also provide simplified information for newly published books. Furthermore, the providing unit can also provide special information for popular books. This makes it possible to adjust the level of detail of the information according to the importance of the book. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the book to AI, and the AI ​​can automatically adjust the level of detail of the information.

[0110] The providing unit can apply different information provision algorithms depending on the book category at the time of provision. The providing unit uses AI to apply different information provision algorithms depending on the book category at the time of provision. For example, the providing unit provides a summary of the story for a novel. The providing unit can also provide main points for a business book. Furthermore, the providing unit can provide information that increases motivation for a self-help book. This makes it possible to provide information according to the book category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input book category data into AI, and the AI ​​can automatically apply different information provision algorithms.

[0111] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, the providing unit provides detailed information when the user is relaxed. The providing unit can also provide simplified information when the user is stressed. Furthermore, the providing unit can also provide information focused on a specific genre when the user is concentrating. This makes it possible to adjust the length of the information to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI and adjust the length of the information to be automatically provided by AI.

[0112] The providing unit can determine the priority of information based on the publication date of the book at the time of providing. The providing unit uses AI to determine the priority of information based on the publication date of the book at the time of providing. For example, the providing unit provides information on newly published books preferentially. The providing unit can also provide information on classic books at a specific timing. Furthermore, the providing unit can also provide information on sequels to books that the user has read in the past preferentially. This makes it possible to determine the priority of information based on the publication date of the book. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input publication date data of books into AI, and the AI ​​can automatically determine the priority of information.

[0113] The providing unit can adjust the order of information based on the relevance of books at the time of providing. The providing unit adjusts the order of information based on the relevance of books using AI at the time of providing. For example, the providing unit preferentially provides information on books that are highly relevant to books that the user has read in the past. The providing unit can also preferentially provide information on books that are highly relevant to books that the user has given high ratings to. Furthermore, the providing unit can also preferentially provide information on books that are highly relevant to books that the user has read for a long time. This makes it possible to adjust the order of information based on the relevance of books. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input book relevance data to AI, and the AI ​​can automatically adjust the order of information.

[0114] The organizing unit can estimate the user's emotion and adjust the way of organizing highlights and notes based on the estimated user's emotion. The organizing unit uses AI to estimate the user's emotion and adjust the way of organizing highlights and notes based on the estimated user's emotion. For example, the organizing unit performs detailed organizing when the user is relaxed. In addition, the organizing unit can also perform simplified organizing when the user is stressed. Furthermore, the organizing unit can also perform organizing focusing on a specific genre when the user is concentrating. This makes it possible to adjust the way of organizing highlights and notes according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit can input the user's emotion data into AI, and the AI ​​can automatically adjust the organizing method.

[0115] The organizing unit can select the optimal organizing method by referring to the user's past highlights and notes when organizing. The organizing unit uses AI to select the optimal organizing method by referring to the user's past highlights and notes when organizing. For example, the organizing unit adjusts the organizing method based on the parts that the user has highlighted in the past. The organizing unit can also optimize the organizing method based on the contents of the user's past notes. Furthermore, the organizing unit can adjust the organizing method based on information that the user has tagged in the past. This makes it possible to select the optimal organizing method based on the user's past highlights and notes. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's past highlights and memo data into AI, and the AI ​​can automatically select the optimal organizing method.

[0116] The organizing unit can organize the data by considering the user's reading speed and reading time. The organizing unit uses AI to organize the data by considering the user's reading speed and reading time. For example, when the user reads quickly, the organizing unit selects a method that can organize the data in a short time. In addition, when the user reads slowly, the organizing unit can select a detailed organizing method. Furthermore, when the user reads during a specific time period, the organizing unit can select an organizing method suitable for that time period. This allows the data to be organized based on the user's reading speed and reading time. Some or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's reading speed and reading time data into AI, and the AI ​​can automatically organize the data.

[0117] The organizing unit can estimate the user's emotion and adjust the display method of highlights and notes based on the estimated user's emotion. The organizing unit uses AI to estimate the user's emotion and adjust the display method of highlights and notes based on the estimated user's emotion. For example, the organizing unit performs detailed display when the user is relaxed. In addition, the organizing unit can also perform simplified display when the user is stressed. Furthermore, the organizing unit can also perform display focusing on a specific genre when the user is concentrating. This makes it possible to adjust the display method of highlights and notes according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit can input the user's emotion data into AI, and the AI ​​can automatically adjust the display method.

[0118] The organizing unit can select the optimal organizing method in consideration of the geographical location information of the user when organizing. The organizing unit uses AI to select the optimal organizing method in consideration of the geographical location information of the user when organizing. For example, when the user is in a specific area, the organizing unit prioritizes organizing highlights and notes related to the area. In addition, when the user is traveling, the organizing unit can also prioritize organizing highlights and notes related to the travel destination. Furthermore, when the user is at home, the organizing unit can also prioritize organizing highlights and notes related to the home. This makes it possible to select the optimal organizing method based on the geographical location information of the user. Part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically select the optimal organizing method.

[0119] The organizing unit can improve the accuracy of the organization by referring to the user's social media activity when organizing. The organizing unit uses AI to improve the accuracy of the organization by referring to the user's social media activity when organizing. For example, the organizing unit organizes based on highlights and notes shared by the user on social media. The organizing unit can also organize based on information on authors the user follows on social media. Furthermore, the organizing unit can organize based on information on reading groups the user participates in on social media. This makes it possible to improve the accuracy of the organization based on the user's social media activity. A part or all of the above-mentioned processing in the organizing unit may be performed using AI, or may be performed without using AI. For example, the organizing unit inputs the user's social media activity data into AI, and the AI ​​can automatically improve the accuracy of the organization.

[0120] The protection unit can estimate the user's emotion and adjust the privacy protection method based on the estimated user's emotion. The protection unit uses AI to estimate the user's emotion and adjust the privacy protection method based on the estimated user's emotion. For example, the protection unit performs standard privacy protection when the user is relaxed. The protection unit can also perform enhanced privacy protection when the user is stressed. Furthermore, the protection unit can also enhance privacy protection for specific information when the user is concentrating. This makes it possible to adjust the privacy protection method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit inputs the user's emotion data into AI, and the AI ​​can automatically adjust the privacy protection method.

[0121] The protection unit can select the optimal protection method by referring to the user's past privacy settings at the time of protection. The protection unit uses AI to select the optimal protection method by referring to the user's past privacy settings at the time of protection. For example, the protection unit adjusts the protection method based on the privacy protection level set by the user in the past. The protection unit can also optimize the protection method based on the privacy settings selected by the user in the past. Furthermore, the protection unit can adjust the protection method based on the privacy protection function used by the user in the past. This makes it possible to select the optimal protection method based on the user's past privacy settings. A part or all of the above-mentioned processing in the protection unit may be performed using AI or may be performed without using AI. For example, the protection unit inputs the user's past privacy setting data into AI, and the AI ​​can automatically select the optimal protection method.

[0122] The protection unit can estimate the user's emotion and determine the priority of privacy protection based on the estimated user's emotion. The protection unit can estimate the user's emotion using AI and determine the priority of privacy protection based on the estimated user's emotion. For example, the protection unit prioritizes standard privacy protection when the user is relaxed. The protection unit can also prioritize enhanced privacy protection when the user is stressed. Furthermore, the protection unit can also prioritize privacy protection for specific information when the user is concentrating. This makes it possible to determine the priority of privacy protection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit can input the user's emotion data into AI, and the AI ​​can automatically determine the priority of privacy protection.

[0123] The protection unit can select the optimal protection method in consideration of the geographical location information of the user at the time of protection. The protection unit uses AI to select the optimal protection method in consideration of the geographical location information of the user at the time of protection. For example, when the user is in a specific area, the protection unit prioritizes privacy protection related to the area. In addition, when the user is traveling, the protection unit can also prioritize privacy protection related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize privacy protection related to the home. This makes it possible to select the optimal protection method based on the geographical location information of the user. Part or all of the above-mentioned processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit inputs the geographical location information data of the user into AI, and the AI ​​can automatically select the optimal protection method.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The reading assistant system can also monitor the user's reading environment and suggest optimal reading times. For example, the system can detect the noise level and lighting conditions around the user and recommend reading in a quiet environment. It can also analyze that the user is more likely to concentrate during certain times of the day and suggest reading at those times. Furthermore, the system can take the user's schedule into account and suggest a time that is suitable for reading. This allows the user to enjoy reading in the optimal environment and time.

[0126] The reading assistant system can estimate the user's emotions and manage the progress of reading based on the estimated emotions. For example, if the user is stressed, the system can suggest books with relaxing content. If the user is excited, the system can also suggest short stories to improve concentration. Furthermore, if the user is sad, the system can also suggest books that will brighten the mood. In this way, a reading experience that corresponds to the user's emotions can be provided.

[0127] The reading assistant system can set reading goals for the user based on the user's reading history. For example, the system can set a goal for the user to read how many books per month and track the progress. The system can also set a goal for the user to read a certain genre of books within a certain period of time. In addition, the system can send reminders for the goals set by the user to help them achieve their goals. This makes it easier for the user to maintain their motivation to read.

[0128] The reading assistant system can estimate the user's emotions and adjust the reading pace based on the estimated emotions. For example, if the user is relaxed, the system can suggest reading at a slower pace. If the user is focused, the system can recommend speed reading. Furthermore, if the user is tired, the system can suggest chapters or sections that can be read in a short time. This allows the reading pace to be provided according to the user's emotions.

[0129] The reading assistant system can analyze the user's reading history and visualize the reading tendency. For example, the system can display the genre and rating of the books the user has read in the past in graphs and charts. It can also display a timeline of how many books the user has read in a specific period. Furthermore, it can analyze the characteristics of the books that the user has given high ratings and visually display the results. This allows the user to understand his or her reading tendency at a glance.

[0130] The reading assistant system can estimate the user's emotions and provide reading feedback based on the estimated emotions. For example, if the user is moved while reading, the system provides a comment function to share the emotion. Also, if the user has a question while reading, the system can provide an answer to the question. Furthermore, if the user is excited while reading, the system can record the emotion and allow the user to review it later. This makes it possible to provide feedback according to the user's emotions.

[0131] The reading assistant system can provide advice to improve a user's reading habits based on the user's reading history. For example, the system can analyze the time the user spends reading and suggest efficient reading methods. If the user is biased towards a particular genre, the system can also suggest books in other genres. Furthermore, if the user tends to stop reading, the system can send reminders to encourage the user to continue reading. This allows the user to develop better reading habits.

[0132] The reading assistant system can estimate the user's emotions and personalize reading recommendations based on the estimated emotions. For example, if the user is relaxed, the system can suggest books with relaxing content. Also, if the user is stressed, the system can suggest books that help relieve stress. Furthermore, if the user is excited, the system can suggest thrilling books to maintain the excitement. This makes it possible to provide personalized recommendations according to the user's emotions.

[0133] The reading assistant system can track the user's reading progress based on the user's reading history and support goal achievement. For example, the system can display the progress of the user toward the reading goal set by the user. If the user is approaching the goal, the system can also send encouraging messages. Furthermore, if the user is moving away from the goal, the system can provide advice for improvement. This makes it easier for the user to achieve the reading goal.

[0134] The reading assistant system can estimate the user's emotions and adjust the reading interaction based on the estimated emotions. For example, if the user is relaxed, the system can interact with the user in a gentle tone. If the user is stressed, the system can provide encouraging words. Furthermore, if the user is concentrating, the system can minimize the interaction. This allows the system to provide interactions according to the user's emotions.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The collection unit collects information about the user's reading. Specifically, it collects information such as the user's reading history, book rating history, reading genre tendencies, titles and authors of books read, reading time, and reading frequency. The collection unit can also use AI to automatically collect the user's reading history. For example, it collects information about books read on an e-book reader, and information about ratings and reviews given to books by users. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI to analyze the collected data and identify the user's preferences. For example, the analysis unit identifies the user's preferences based on the genres and ratings of books the user has read in the past. The analysis unit can use data mining technology and machine learning algorithms to identify the user's preferences. Step 3: The suggestion unit suggests the next book to read based on the analysis results by the analysis unit. The suggestion unit uses AI to suggest the best book for the user. For example, it suggests books in a genre that the user has previously rated highly. It can also determine the number of pages of the book to suggest based on the user's reading speed. Step 4: The provider uses AI to provide information and summaries about the book's contents based on the user's question. For example, if a user asks about a specific book, the provider provides a summary of that book. Step 5: The organizer organizes and saves the parts and notes highlighted by the user. The organizer uses AI to tag, categorize, and organize the parts and notes highlighted by the user. For example, the parts highlighted by the user are automatically tagged, categorized, and saved. This allows the user to manage their notes efficiently.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the collection unit can collect information on the user's reading using the camera 42 and microphone 38B of the smart device 14. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and automatically collects the user's reading history and evaluation history. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's preferences. The suggestion unit is realized by, for example, the control unit 46A of the smart device 14, and suggests the next book to be read based on the analysis result. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides information and summaries regarding the contents of the book in response to a user's question. The organization unit is realized by, for example, the control unit 46A of the smart device 14, and organizes and saves the parts and notes highlighted by the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the collection unit can collect information on the user's reading using the camera 42 and microphone 238 of the smart glasses 214. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and automatically collects the user's reading history and evaluation history. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's preferences. The suggestion unit is realized by, for example, the control unit 46A of the smart glasses 214, and suggests the next book to be read based on the analysis result. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides information and summaries regarding the contents of the book in response to a user's question. The organization unit is realized by, for example, the control unit 46A of the smart glasses 214, and organizes and saves the parts and notes highlighted by the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] For example, the collection unit can collect information about the user's reading using the camera 42 and microphone 238 of the headset terminal 314. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and automatically collects the user's reading history and evaluation history. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's preferences. The suggestion unit is realized by, for example, the control unit 46A of the headset terminal 314, and suggests the next book to be read based on the analysis result. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides information and summaries about the contents of the book in response to the user's questions. The organization unit is realized by, for example, the control unit 46A of the headset terminal 314, and organizes and saves the parts and notes highlighted by the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] For example, the collection unit can collect information on the user's reading using the camera 42 and microphone 238 of the robot 414. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and automatically collects the user's reading history and evaluation history. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's preferences. The suggestion unit is realized by, for example, the control unit 46A of the robot 414, and suggests the next book to be read based on the analysis result. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides information and summaries regarding the contents of the book in response to the user's questions. The organization unit is realized by, for example, the control unit 46A of the robot 414, and organizes and saves the parts and notes highlighted by the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) A collection unit that collects information about a user's reading; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests books based on the analysis result by the analysis unit; a providing unit that provides information in response to the user's question; An organizing unit that tags, categorizes, and organizes the portions and notes highlighted by the user. A system characterized by: (Appendix 2) Equipped with features to protect user privacy 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collecting user's reading history, book rating history, or reading genre tendencies 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze collected data to identify user preferences 2. The system of claim 1, (Appendix 5) The suggestion unit, Suggest the next book to read based on analysis results 2. The system of claim 1, (Appendix 6) The providing unit is Providing information or summaries about the book's contents based on a user's question 2. The system of claim 1, (Appendix 7) The sorting unit includes: Allows users to tag, categorize, and organize their highlights and notes 2. The system of claim 1, (Appendix 8) The suggestion unit, Determine how many pages of a book to suggest based on the user's reading speed 2. The system of claim 1, (Appendix 9) The collecting unit includes: Collect information about books shared by users on social media 2. The system of claim 1, (Appendix 10) The suggestion unit, Prioritize audiobook suggestions based on user reading habits 2. The system of claim 1, (Appendix 11) The collecting unit includes: A method for estimating a user's emotions and adjusting the timing of acquiring a reading history based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze the user's past reading history and select the optimal acquisition method 2. The system of claim 1, (Appendix 13) The collecting unit includes: When retrieving reading history, filter it based on the user's current interests 2. The system of claim 1, (Appendix 14) The collecting unit includes: A user's emotion is estimated, and a priority is determined for the reading history to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When retrieving reading history, the system takes into account the user's geographic location information to prioritize retrieving history that is more relevant to the user. 2. The system of claim 1, (Appendix 16) The collecting unit includes: When acquiring reading history, the system analyzes the user's social media activity and acquires related history. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing, the analysis algorithm is optimized by referring to the user's past reading history. 2. The system of claim 1, (Appendix 19) The analysis unit is When analyzing, the analysis takes into account the user's reading speed and reading time. 2. The system of claim 1, (Appendix 20) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing, the geographic location of the user is taken into account. 2. The system of claim 1, (Appendix 22) The analysis unit is During analysis, we refer to users' social media activities to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 23) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The suggestion unit, When proposing, adjust the detail of your proposal based on the importance of the book 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the book category 2. The system of claim 1, (Appendix 26) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 27) The suggestion unit, At the time of submission, we will prioritize proposals based on the publication date of the book. 2. The system of claim 1, (Appendix 28) The suggestion unit, When making suggestions, adjust the order of suggestions based on book relevance 2. The system of claim 1, (Appendix 29) The providing unit is Estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The providing unit is When provided, the level of detail is adjusted based on the importance of the book. 2. The system of claim 1, (Appendix 31) The providing unit is When serving, different information serving algorithms are applied depending on the book category. 2. The system of claim 1, (Appendix 32) The providing unit is The system estimates the user's emotions and adjusts the length of information provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 33) The providing unit is When provided, information will be prioritized based on the publication date of the book 2. The system of claim 1, (Appendix 34) The providing unit is When provided, the order of information is adjusted based on book relevance 2. The system of claim 1, (Appendix 35) The sorting unit includes: Infer user sentiment and adjust highlight and note organization based on the inferred user sentiment 2. The system of claim 1, (Appendix 36) The sorting unit includes: When organizing, the system refers to the user's past highlights and notes to select the best organization method. 2. The system of claim 1, (Appendix 37) The sorting unit includes: When sorting, consider the user's reading speed and reading time. 2. The system of claim 1, (Appendix 38) The sorting unit includes: Infer user emotions and adjust how highlights and notes are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 39) The sorting unit includes: When organizing, the system considers the user's geographic location to select the optimal organization method. 2. The system of claim 1, (Appendix 40) The sorting unit includes: When organizing, refer to users' social media activity to improve organization accuracy. 2. The system of claim 1, (Appendix 41) The protective part is Inferring user emotions and adjusting privacy protection methods based on the estimated user emotions 3. The system of claim 2, (Appendix 42) The protective part is When protecting, the system refers to the user's past privacy settings to select the optimal protection method. 3. The system of claim 2, (Appendix 43) The protective part is Estimating user emotions and determining privacy protection priorities based on the estimated user emotions 3. The system of claim 2, (Appendix 44) The protective part is When protecting, it takes into account the user's geographic location to select the optimal protection method 3. The system of claim 2, [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects information about a user's reading; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests books based on the analysis result by the analysis unit; a providing unit that provides information in response to the user's question; An organizing unit that tags, categorizes, and organizes the portions and notes highlighted by the user. A system characterized by:

2. Provide a function for protecting the privacy of the user 2. The system of claim 1 .

3. The collecting unit includes: Collecting the user's reading history, book rating history, or reading genre tendencies 2. The system of claim 1 .

4. The analysis unit is Analyzing the data collected by the collection unit to identify preferences of the user 2. The system of claim 1 .

5. The suggestion unit, Suggests the next book to read based on the analysis results by the analysis unit 2. The system of claim 1 .

6. The providing unit is Providing information or summaries about the contents of books based on the user's queries 2. The system of claim 1 .

7. The sorting unit includes: The user's highlights and notes are tagged, categorized, and organized.

2. The system of claim 1 .

8. The suggestion unit, Determining the number of book pages to suggest based on the user's reading speed 2. The system of claim 1 .

9. The collecting unit includes: Collect information about books shared by said users on social media 2. The system of claim 1 .

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